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This paper considers two-rate based structures for variable fractional-delay (VFD) finite-length impulse response (FIR) filters. They are single-rate structures but derived through a two-rate approach. The basic structure considered hitherto utilizes a regular half-band (HB) linear-phase filter and the Farrow structure with linear-phase subfilters. Especially for wide-band specifications, this structure is computationally efficient because most of the overall arithmetic complexity is due to the HB filter which is common to all Farrow-structure subfilters. This paper extends and generalizes existing results. Firstly, frequency-response masking (FRM) HB filters are utilized which offer further complexity reductions. Secondly, both linear-phase and low-delay subfilters are treated and combined which offers trade-offs between the complexity, delay, and magnitude response overshoot which is typical for low-delay filters. Thirdly, the HB filter is replaced by a general filter which enables additional frequency-response constraints in the upper frequency band which normally is treated as a don't-care band. Wide-band design examples (90, 95, and 98% of the Nyquist band) reveal arithmetic complexity savings between some 20 and 85% compared with other structures, including infinite-length impulse response structures. Hence, the VFD filter structures proposed in this paper exhibit the lowest arithmetic complexity among all hitherto published VFD filter structures. [ABSTRACT FROM AUTHOR]

This paper studies the event-triggered robust model predictive control (MPC) problem for constrained linear systems subject to bounded disturbances. For control computations, explicit MPC reduces online optimizations to simple function evaluations, thus is suitable for resource constrained systems. We propose an event-triggering mechanism (ETM) for an explicit robust model predictive controller such that function evaluations are not needed at every time instant and the ETM itself requires comparably less online computations. In the proposed ETM, we design a contracting polytopic trigger set with respect to system constraints and the explicit robust control law. The resulting event-triggered control scheme ensures robust constraint satisfaction and robust stability for the closed-loop system while avoiding the Zeno-like behavior. Simulation results illustrate the validity of the event-triggered robust control scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a new minimax method for designing even-order finite-impulse-response (FIR) variable fractional-delay (VFD) digital filters with both the peak errors (maximum absolute errors) of variable frequency response (VFR) and VFD response being minimized. We call such a new minimax design the bi-minimax design, which minimizes a mixed error function that contains both the VFR peak error and VFD peak error. A relative weighting factor is used in the mixed error function for adjusting the relative weightings of the two peak errors. The central part of the biminimax design is how to formulate the biminimax design with highly non-linear constraints on the VFD errors as a solvable one. After linearizing the highly non-linear constraints as linear ones, the biminimax design problem can be easily solved by using the well-known efficient software SeDuMi. As a result, both the VFR peak error and VFD peak error can be simultaneously suppressed and the resulting VFR errors and VFD errors are made nearly equiripple (bi-equiripple). As compared with the existing SOCP-based minimax design that minimizes only the VFR peak error, the proposed biminimax method can achieve a nearly biequiripple design for both the VFR and VFD errors. A design example is given to illustrate the effectiveness of the biminimax design method. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the robust event-triggered model predictive control (MPC) of discrete-time constrained linear systems subject to bounded additive disturbances. We make use of the interpolation technique to construct a feedback policy and tighten the original system constraint accordingly to fulfill robust constraint satisfaction. A dynamic event trigger that allows the controller to solve the optimization problem only at triggering time instants is developed, where the triggering threshold is related to the interpolating coefficient of the feedback policy and determined via optimization. We show that the proposed algorithm is recursively feasible and the closed-loop system is input-to-state stable in the attraction region. Finally, a numerical example is provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

The explicit reference governor (ERG) is a simple and systematic approach that provides constraint handling capabilities to prestabilized systems. The basic idea behind this approach is to translate state and input constraints into an upper-bound on the value of the Lyapunov function, which is then enforced by suitably manipulating the derivative of the applied reference. When designing the ERG, one of the main challenges is the determination of an upper-bound on the value of the Lyapunov function that ensures constraints satisfaction. This paper proposes a systematic approach for estimating online the optimal upper-bound for systems subject to the intersection of concave constraints. To do this, the Barrier function method is used. The effect of the estimation error caused by the time-varying nature of the auxiliary reference on the constraint satisfaction capability of the ERG is studied analytically. A procedure is proposed to modify the estimated upper-bound to avoid constraints violation in the presence of estimation errors. The effectiveness of the proposed scheme is demonstrated through a simulation study on an overhead gantry crane system. [ABSTRACT FROM AUTHOR]

This paper presents a robust adaptive stabilizing control scheme via partial-state feedback for a class of nonlinear cascaded systems with a time-varying output constraint. Different from the existing achievements, the nonlinear parameterization and unmeasured zero-dynamics are considered in this paper. The notion of input-to-state stability (ISS) and ISS-Lyapunov function are used to describe the zero-dynamics. The time-varying barrier Lyapunov function (TVBLF) is employed to ensure the output constraint satisfaction and the changing supply rates technique is used to deal with the unknown cascaded zero-dynamic states. It is shown that all the signals in the closed-loop system are bounded, and the asymptotic stabilization is achieved without violation of the output constraint. The performance of the proposed control scheme is illustrated through a simulation example. [ABSTRACT FROM AUTHOR]

This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e., without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSSs and an integrated PnP distributed FD architecture is proposed. Simulation results in two paradigmatic examples show the effectiveness and the potential of the general methodology. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel algorithm for sparse finite impulse response (FIR) filter designs. The objective of the sparse digital filter design problem considered in this paper is to reduce the number of nonzero-valued filter coefficients, subject to a weighted least-squares (WLS) approximation error constraint imposed on the frequency domain. The proposed design method is inspired by the iterative shrinkage/thresholding (IST) algorithms, which are used in sparse and redundant representation for signals. The basic idea of the proposed design algorithm is to successively transform the original nonconvex problem to a series of constrained subproblems in a simpler form. Despite of their nonconvexity, these subproblems can be efficiently and reliably solved in each iterative step by a numerical approach developed in this paper. Furthermore, it can be demonstrated that the obtained solutions are essentially optimal to their respective subproblems. Since its major part only involves scalar operations, the proposed algorithm is computationally efficient. Three sets of numerical examples are presented in this paper to illustrate the effectiveness of the proposed design algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an output-constrained control algorithm is presented for the consensus control of a class of unknown nonaffine multiagent systems (MASs) with partially unknown control directions. Our contribution includes a step forward beyond the usual consensus stabilization result to show that the outputs of agents remain within user-defined time-varying constraints. To achieve the new results, an error transformation technique is established to generate an equivalent MAS from the original one. Stabilization and consensus of the transformed agent states ensure both the satisfaction of the time-varying constraints and the consensus of the original agent states. Based on the Nussbaum gain technique, the unknown control direction problem is solved. By the Lyapunov synthesis, the asymptotic consensus result and the satisfaction of the output constraints are theoretically proved, along with all the closed-loop signals being bounded. Additionally, the developed consensus controller is distributed since each agent only exchanges information with its neighbors. Finally, simulations on a nonaffine MAS demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Previous works provide a few effective discretization formulas for zeroing neural network (ZNN), of which the precision is a square pattern. However, those formulas are separately developed via many relatively blind attempts. In this paper, general square-pattern discretization (SPD) formulas are proposed for ZNN via the idea of the second-order derivative elimination. All existing SPD formulas in previous works are included in the framework of the general SPD formulas. The connections and differences of various general formulas are also discussed. Furthermore, the general SPD formulas are used to solve future optimization under linear equality constraints, and the corresponding general discrete ZNN models are proposed. General discrete ZNN models have at least one parameter to adjust, thereby determining their zero stability. Thus, the parameter domains are obtained by restricting zero stability. Finally, numerous comparative numerical experiments, including the motion control of a PUMA560 robot manipulator, are provided to substantiate theoretical results and their superiority to conventional Euler formula. [ABSTRACT FROM AUTHOR]

This paper introduces a nonsmooth (NS) neural network that is able to operate in a time-dependent (TD) context and is potentially useful for solving some classes of NS-TD problems. The proposed network is named nonsmooth time-dependent network (NTN) and is an extension to a TD setting of a previous NS neural network for programming problems. Suppose $C(t)$ , t \ge 0$ , is a nonempty TD convex feasibility set defined by TD inequality constraints. The constraints are in general NS (nondifferentiable) functions of the state variables and time. NTN is described by the subdifferential with respect to the state variables of an NS-TD barrier function and a vector field corresponding to the unconstrained dynamics. This paper shows that for suitable values of the penalty parameter, the NTN dynamics displays two main phases. In the first phase, any solution of NTN not starting in C(0)$ at t=0$ is able to reach the moving set C(\cdot ) , whereas in the second phase, the solution tracks the moving set, i.e., it stays within C(t)$ for all subsequent times t \ge t_{h} . NTN is thus able to find an exact feasible solution in finite time and also to provide an exact feasible solution for subsequent times. This new and peculiar dynamics displayed by NTN is potentially useful for addressing some significant TD signal processing tasks. As an illustration, this paper discusses a number of examples where NTN is applied to the solution of NS-TD convex feasibility problems. [ABSTRACT FROM AUTHOR]

In a previous paper (Kim, 2019), an analytical framework based on the constraint satisfaction problems was proposed to reveal the characteristics of households using event logs from smart door lock systems. This work provides a more rigorous justification for the previous approach. This paper proposes a novel parameter estimation method called the maximum feasibility estimation (MFE). The MFE does not rely on any assumption about the parametric family of probability densities from which a random observation is drawn. Instead, we assume that constraints are imposed on observations and that some of the constraints are a function of a parameter of interest. The proposed estimator maximizes the feasible region, a set of all possible observations that satisfy those constraints. The method proposed is validated using synthetic data as well as real streaming event log data. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor. Design/methodology/approach -- A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints. Findings -- The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller. Originality/value -- This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints. [ABSTRACT FROM AUTHOR]

• New scenario-based MPC strategy developed for linear systems subject to correlated parametric and/or additive uncertainties. • The proposed algorithm adapts the conditional scenario reduction method to the MPC framework. • Two numerical examples validate the higher probability of constraint satisfaction of the proposed MPC. • The high probability of constraint satisfaction of the proposed MPC allows solving a OCP with fewer scenarios when a shorter solution time is needed. This paper proposes a novel MPC approach called conditional scenario-based model predictive control (CSB-MPC), developed for discrete-time linear systems affected by parametric uncertainties and/or additive disturbances, which are correlated and with bounded support. At each control period, a primary set of equiprobable scenarios is generated and subsequently approximated to a new reduced set of conditional scenarios in which each has its respective probabilities of occurrence. This new set is considered for solving an optimal control problem in whose cost function the predicted states and inputs are penalised according to the probabilities associated with the uncertainties on which they depend in order to give more importance to predictions that involve realisations with a higher probability of occurrence. The performances of this new approach and those of a standard scenario-based MPC are compared through two numerical examples, and the results show greater probabilities of not transgressing the state constraints by the former, even when considering a smaller number of scenarios than the scenario-based MPC. [ABSTRACT FROM AUTHOR]

• The constrained clustering problem is studied. • An efficient local search algorithm is proposed. • A node filtering strategy is introduced for improving efficiency. • The proposed algorithm is more effective than state-of-the-art heuristics. Constrained clustering extends clustering by integrating user constraints, and aims to determine an optimal assignment under the constraints. In this paper, we propose a local search algorithm called FastCCP to solve the constrained clustering problem. In the algorithm, instances connected by must-link constraints are first merged into nodes, and then, a local search method is performed to handle the cannot-link constraints while minimizing the Within-Cluster Sum of Squares (WCSS). Several strategies are proposed to enhance the solution diversity and achieve a trade-off between constraint satisfaction and WCSS minimization during the search. Furthermore, a node-filtering strategy is proposed to improve the efficiency of the algorithm. Experiments are performed on benchmark datasets to evaluate our algorithm. The comparative results indicate that our algorithm outperforms state-of-the-art algorithms in terms of both the solution quality and CPU runtime. [ABSTRACT FROM AUTHOR]

In this paper, we study the fragility of a popular distributed averaging algorithm when the information exchange among the nodes is limited by communication delays, fading connections and additive noise. We show that the otherwise well studied and benign multi-agent system can generate a collective global complex behavior. We characterize this behavior, common to many natural and human-made interconnected systems, as a collective hyper-jump diffusion process and as a Lévy flight process in a special case. We further describe the mechanism for its emergence and predict its occurrence, under standard assumptions, by checking the Mean Square instability of a certain part of the system. We show that the strong connectivity property of the network topology guarantees that the complex behavior is global and manifested by all the agents in the network, even though the source of uncertainty is localized. [ABSTRACT FROM AUTHOR]

In this paper, we construct fault-tolerant linear finite-state machines (LFSMs) in which error detection and correction can be performed nonconcurrently (e.g., periodically). More specifically, by jointly choosing the state encoding constraints and the redundant dynamics of the fault-tolerant LFSM, we enable an external checker to detect and Identify errors due to past faults based on the current, possibly corrupted state of the LFSM. the paper presents systematic constructions of fault-tolerant LFSMs based on a characterization of non concurrent error detection/correction in terms of state encoding constraints and redundant dynamics. In particular, we develop a scheme that uses Bose-Chaudhuri-Hocquenghem (BCH) coding and obtains fault-tolerant LFSMS that require 2D additional state variables and have the ability to correct up to D errors In any state variable at any time step in the tithe interval consisting of the latest N time steps of operation. The construction uses the minimum possible number of additional state variables and requires an error detecting/correcting mechanism with computational complexity that is only linear in N. [ABSTRACT FROM AUTHOR]

Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment (F0) of a data stream. Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment (F0) of a data stream. [ABSTRACT FROM AUTHOR]

In this paper, a distributed control architecture is presented for addressing the cooperative perimeter surveillance of rectangular areas for multi-agent system whose dynamics is described by double integrator models subject to exogenous bounded disturbances. In particular, a novel methodology to generate proper state trajectories of a swarm of agents is provided and then exploited as references for the underlying model predictive controllers bank. Specifically, the swarm is first driven along the perimeter of a rectangular strip and next imposed to enter a given containment region while remaining simultaneously outside a forbidden region around a prescribed target. Steady-state conditions are analyzed and sufficient conditions derived in terms of the control law parameters. Simulation results put in light the main properties of the control architecture that is designed to adequately switch between two different distributed algorithms in order to guarantee constraints satisfaction within the capturing region despite any disturbance realization. [ABSTRACT FROM AUTHOR]

In this paper, a compound control strategy is proposed to realize the trajectory tracking task of quadrotors under operating constraints and disturbances. Disturbances caused by model uncertainties, environmental noises, and measurement disturbances are divided into matched disturbances and unmatched ones, which are compensated and suppressed separately by using two control components. The integral sliding mode control component is designed to actively reject the matched disturbances, and the control system is then transformed into an equivalent control system subject to equivalent disturbances only related to the unmatched disturbances. The remaining equivalent disturbances are treated by a robust model predictive control component based on the idea of constraints tightening, which minimizes the tracking error in an optimization framework and takes both state and input constraints into account explicitly. The derived compound control strategy is based on these two control components. Conditions are provided to guarantee the robust constraint satisfaction, recursive feasibility and closed-loop stability of the tracking error system. An illustrative example on the quadrotors shows the efficiency and robustness of this compound tracking control algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input constraints. The tracking error dynamics and reference trajectory dynamics are first combined to form an augmented system. Then, a new discounted performance function based on the augmented system is presented for the optimal nonlinear tracking problem. In contrast to the standard solution, which finds the feedforward and feedback terms of the control input separately, the minimization of the proposed discounted performance function gives both feedback and feedforward parts of the control input simultaneously. This enables us to encode the input constraints into the optimization problem using a nonquadratic performance function. The DT tracking Bellman equation and tracking Hamilton–Jacobi–Bellman (HJB) are derived. An actor–critic-based reinforcement learning algorithm is used to learn the solution to the tracking HJB equation online without requiring knowledge of the system drift dynamics. That is, two neural networks (NNs), namely, actor NN and critic NN, are tuned online and simultaneously to generate the optimal bounded control policy. A simulation example is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A novel distributed coordination strategy is presented for networked, locally regulated and possibly dynamically coupled, interconnected systems. Such systems are assumed to be connected via data links and subject to pointwise-in-time global constraints on some relevant variables of them, to be enforced as a coordination goal along the overall system evolutions. Such a coordination-by-constraint paradigm is accomplished by resorting to a novel distributed multi-agent Command Governor (CG) approach where each agent is in charge to locally modify, whenever necessary and on the basis of a reduced amount of data exchanged with the other agents, the prescribed set-points to the regulated subsystems so that the coordination constraints are always satisfied. The strategy is described and its main properties analyzed, especially for what it concerns the stability, feasibility and Pareto optimality of the solution. A liveness analysis concerning the possible presence of undesirable Nash equilibria and/or deadlock situations is presented and a discussion on the scalability of the solution with the problem dimension is reported as well. The constrained coordination of a network of interconnected water tanks is presented as a final example in order to show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Pickup and delivery problems (PDPs), in which objects or people have to be transported between specific locations, are among the most common combinatorial problems in real-world logistical operations. A widely-encountered type of PDP is the Stacker Crane Problem (SCP), where each commodity/customer is associated with a pickup location and a delivery location, and the objective is to find a minimum-length tour visiting all locations with the constraint that each pickup location and its associated delivery location are visited in immediate, consecutive order. The SCP is NP-Hard and the best known approximation algorithm only provides a 9/5 approximation ratio. In this paper, we examine an embedding of the SCP within a stochastic framework, and our objective is three-fold: First, we describe a large class of algorithms for the SCP, where every member is asymptotically optimal, i.e., it produces, almost surely, a solution approaching the optimal one as the number of pickups/deliveries goes to infinity; moreover, one can achieve computational complexity O(n^2+\varepsilon) within the class, where n is the number of pickup/delivery pairs and \varepsilon is an arbitrarily small positive constant. Second, we characterize the length of the optimal SCP tour asymptotically. Finally, we study a dynamic version of the SCP, whereby pickup and delivery requests arrive according to a Poisson process, and which serves as a model for large-scale demand-responsive transport (DRT) systems. For such a dynamic counterpart of the SCP, we derive a necessary and sufficient condition for the existence of stable vehicle routing policies, which depends only on the workspace geometry, the distributions of pickup and delivery points, the arrival rate of requests, and the number of vehicles. Our results leverage a novel connection between the Euclidean Bipartite Matching Problem and the theory of random permutations, and, for the dynamic setting, exhibit novel features that are absent in traditional spatially-distributed queueing systems. [ABSTRACT FROM AUTHOR]

Coupled local minimizers (CLMs) turn out to be a potential global optimization strategy to explore a search space, avoid overfitting and produce good generalization. In this paper, convergence properties of CLMs based on an augmented Lagrangian function in the context of equality constrained minimization, are studied. We first consider the augmented Lagrangian by taking the objective of minimizing the average cost of an ensemble of local minimizers subject to pairwise synchronization constraints. Then we study an array of CLMs within the Lagrange programming network framework and analyze the local stability of CLMs using a linearization strategy. We further show that, under some mild conditions, global asymptotical stability of the unique equilibrium point of the network can be guaranteed. Afterwards, some sufficient conditions are presented to ensure the stability of synchronization between any two minimizers via a directed graph method. The results show that the CLMs usually can be synchronized if the penalty factors in the array of CLMs are chosen large enough. It is worth pointing out that CLMs possess the capability of global exploration in the search space and the advantage of faster running time on convex problems in comparison with most of the neural network approaches, which is also illustrated through two test functions and their numerical simulations. [ABSTRACT FROM AUTHOR]

This paper considers high-speed control of constrained linear parameter-varying systems using model predictive control. Existing model predictive control schemes for control of constrained linear parameter-varying systems typically require the solution of a semi-definite program at each sampling instance. Recently, variants of explicit model predictive control were proposed for linear parameter-varying systems with polytopic representation, decreasing the online computational effort by orders of magnitude. Depending on the mathematical structure of the underlying system, the constrained finite-time optimal control problem can be solved optimally, or close-to-optimal solutions can be computed. Constraint satisfaction, recursive feasibility and asymptotic stability can be guaranteed a priori by an appropriate selection of the terminal state constraints and terminal cost. The paper at hand gathers previous developments and provides new material such as a proof for the optimality of the solution, or, in the case of close-to-optimal solutions, a procedure to determine a bound on the suboptimality of the solution. [ABSTRACT FROM AUTHOR]

We consider the problem of designing controllers to steer mobile robots to the source (the minimizer) of a signal field. In addition to the mobility constraints, e.g., posed by the nonholonomic dynamics, we assume that the field is completely unknown to the robot and the robot has no knowledge of its own position. Furthermore, the unknown field is randomly switching. In the case where the information of the field (e.g., the gradient) is completely known, standard motion planning techniques for mobile robots would converge to the known source. In the absence of mobility constraints, convergence to the minimum of unknown fields can be pursued using the framework of numerical optimization. By considering these facts, this paper exploits an idea of the stochastic approximation for solving the problem mentioned in the beginning and proposes a source seeking controller which sequentially generates the next waypoints such that the resulting discrete trajectory converges to the unknown source and which steers the robot along the waypoints, under the assumption that the robot can move to any point in the body fixed coordinate frame. To this end, we develop a rotation-invariant and forward-sided version of the simultaneous-perturbation stochastic approximation algorithm as a method to generate the next waypoints. Based on this algorithm, we design source seeking controllers. Furthermore, it is proven that the robot converges to a small set including the source in a probabilistic sense if the signal field switches periodically and sufficiently fast. The proposed controllers are demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper exploits knowledge made available by an external source in the form of a predictive distribution in order to elicit a parameter prior. It uses the terminology of Bayesian transfer learning, one of many domains dealing with reasoning as coherent knowledge processing. An empirical solution of the addressed problem was provided in [19] , based on an interpretation of the external predictor as an empirical distribution constructed from fictitious data. In this paper, two main contributions are provided. First, the problem is solved using formal hierarchical Bayesian modeling [25] , and the knowledge transfer is achieved optimally, i.e. in the minimum-KLD sense. Second, this hierarchical setting yields a distribution on the set of possible priors, with the choice [19] acting as the base distribution. This allows randomized choices of the prior to be generated, avoiding costly and/or intractable estimation of this prior. It also provides measures of uncertainty in the prior choice, allowing subsequent learning tasks to be assessed for robustness to this prior choice. The instantiation of the method in already published applications in knowledge elicitation, recursive learning and flat cooperation of adaptive controllers is recalled, and prospective application domains are also mentioned. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Nonlinear Model Predictive Control (NMPC) framework for tracking for piece-wise constant reference signals. The main novelty is the use of quasi-Linear Parameter Varying (qLPV) embeddings in order to describe the nonlinear dynamics. Furthermore, these embeddings are exploited by an extrapolation mechanism, which provides the future behaviour of the scheduling parameters with bounded estimation error. Therefore, the resulting NMPC becomes computationally efficient (comparable to a Quadratic Programming algorithm), since, at each sampling period, the predictions are linear. Benefiting from artificial target variables, the method is also able to avoid feasibility losses due to large set-point variations. Robust constraint satisfaction, closed-loop stability, and recursive feasibility certificates are provided, thanks to uncertainty propagation zonotopes and parameter-dependent terminal ingredients. A benchmark example is used to illustrate the effectiveness of the method, which is compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory tracking control strategy for Skid-Steered wheeled UGVs based on a simplified controller design and robust analysis. The position control loops are decoupled by using a virtual orientation angle so that the analysis and design problems can be handled independently as disturbed first-order control systems. The transformed model is useful because it simplifies the robust stability analysis and the definition of the conditions that ensure velocity constraint satisfaction. Furthermore, the proposed analytical control law does not require the solution of any online optimization problem. The usefulness and the design simplicity of the proposed control strategy are illustrated in an experimental case study performed with a Clearpath Husky UGV. • A novel approach for trajectory control of Skid-Steer UGVs has been proposed. • Linear robust design rules are provided for the new analytical control law. • Robust stability and constrains satisfaction can be ensured during navigation. [ABSTRACT FROM AUTHOR]

This paper investigated the multiple performance constrained sliding mode fuzzy control problem for continuous-time stochastic nonlinear systems with multiplicative noises. The stochastic nonlinear systems considered in this paper are represented by Takagi–Sugeno (T–S) fuzzy models with a bilinear consequent part. To simultaneously satisfy stability, passivity and variance constraints, sufficient conditions are derived in terms of linear matrix inequalities based on Lyapunov theory. By solving the corresponding sufficient conditions, a parallel distributed compensation based fuzzy controller is obtained to ensure the stability of the closed-loop nonlinear systems subject to variance and passivity performance constraints. The main contribution of this paper is a sliding mode fuzzy controller developed such that the considered multiple performance constraints can be satisfied for the T–S fuzzy systems with a bilinear consequent part. Finally, a numerical example for controlling a ship steering system is provided to demonstrate the validity and effectiveness of the proposed sliding mode fuzzy control method. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical decentralized reference governor for constrained cascade systems. The reference governor (RG) approach is reformulated in terms of receding horizon strategy such that a locally receding horizon optimization is obtained for each subsystem with a pre-established prediction horizon. The algorithm guarantees that not only the nominal overall closed-loop system without any constraint is recoverable but also the state and control constraints are satisfied in transient conditions. Also, considering unfeasible reference signals, the output of any subsystem goes locally to the nearest feasible value. The proposed dynamic constraint tightening strategy uses a receding horizon to reduce the conservatism of conventional robust RGs. Moreover, a decentralized implementation of the algorithms used to compute tightened constraints and output admissible sets is introduced that allow to deal with large scale systems. Furthermore, a set of dynamic constraints are presented to preserve recursive feasibility of distributed optimization problem. Feasibility, stability, convergence, and robust constraint satisfaction of the proposed algorithm are also demonstrated. The proposed approach is verified by simulating a system composed of three cascade jacketed continuous stirred tank reactors. [ABSTRACT FROM AUTHOR]

The paper investigates the emergency scheduling problem of multiple aerial targets staring surveillance with multiple optical satellites (MSMT). To fulfill the requirement of state estimation and trajectory prediction, a novel scheduling model including specified duration of cooperative observation and revisits has been proposed. The total observation profit, introducing the independent observation for detection efficiency and cooperative observation for positioning precision, is regarded as the optimization objective. The corresponding time-varying observation gain functions of the two kinds of observations are illustrated respectively. Combined with the constraints of visibility, optic axis (OA) adjustment ability, cooperation duration and immunity, the constraint satisfaction mission scheduling model is established. In addition, a cooperation-oriented ant colony optimization algorithm (CO-ACO) is designed to determine the observation sequence for each satellite. Five elements for heuristics, balancing the quality and quantity of observations, are introduced to the design which can fully exploit the intelligence and improve the algorithm effectiveness. Simulations are performed to verify the effectiveness and demonstrate the superiority to existing algorithms under different conditions in terms of optimization performance. [ABSTRACT FROM AUTHOR]

• Targeted feasibility rules achieve the classification of the population. • Classification-collaboration mutation achieves group communication and cooperation. • Adaptive global search framework weakens the greedy of the mutation operator. • The comparison results on three sets of benchmark problems are highly competitive. Expensive Constrained Optimization Problems (ECOPs) widely exist in various scientific and industrial applications. Surrogate-Assisted Evolutionary Algorithms (SAEAs) have recently exhibited great ability in solving these expensive optimization problems. This paper proposes a Surrogate-Assisted Classification-Collaboration Differential Evolution (SACCDE) algorithm for ECOPs with inequality constraints. In SACCDE, the current population is classified into two subpopulations based on certain feasibility rules, and a classification-collaboration mutation operation is designed to generate multiple promising mutant solutions by not only using promising information in good solutions but also fully exploiting potential information hidden in bad solutions. Afterwards, the surrogate is utilized to identify the most promising offspring solution for accelerating the convergence speed. Furthermore, considering that the population diversity may decrease due to the excessive incorporation of greedy information brought by the classified solutions, a global search framework that can adaptively adjust the classification-collaboration mutation operation based on the iterative information is introduced for achieving an effective global search. Therefore, the proposed algorithm can strike a well balance between local and global search. The experimental results of SACCDE and other state-of-the-art algorithms demonstrate that the performance of SACCDE is highly competitive. [ABSTRACT FROM AUTHOR]

Abstract: The sense of approximate reasoning within fuzzy logic has given rise to speculation as to whether precise reasoning within classical logic can even be considered a feasible way of handling most of the world’s problems. Now, another question can be raised; can fuzzy logic, by itself, justify/handle the sort of uncertainty found in problems that belong to the class of open world problems? This family of problems corresponds to the case of ambiguous mathematical theories mainly due to the fact that there is just imperfect/incomplete information about them. Recently, Zadeh introduced an extension of fuzzy logic, claimed to bridge the gaps in conventional approaches to the open world problems. In this paper, we present a basic background of extended fuzzy logic (FLe). We address several main concepts such as f-transformation and f-validity, enhance the definition of generalized constraints, and propose that FLe’s rules have two layers: one based on the implication model and another based on the conjunction model. In addition, we explore how FLe can handle those conditions described in antonym pairs as well as those occurred without considering rationality in the framework. Both cases challenge most decision-making theories. We also suggest FLe and second-order probability are f-complementary rather than f-competitive. The paper asserts that FLe is highly capable of constructing mathematical solutions to problems (including computational problems) that are stated in natural language. [Copyright &y& Elsevier]

Abstract: This paper considers robust control for networked control systems (NCSs) with communication constraints, network-induced delay and uncertainties in both system dynamic and access rates. Medium access constraints of both sensor–controller and controller–actuator channels are considered, that is, not all the sensors and actuators are granted access to the network simultaneously to transfer measurements, the access status of sensors or actuators are governed by some stochastic variables. Then new NCSs model is proposed by using these stochastic variables. The purpose of this paper is to design the robust controller to stabilize the NCSs with communication constraints and random network-induced delay. By using the Lyapunov functional method and the linear matrix inequality technology, sufficient conditions for the robust mean square stable (RMSS) of the NCSs with an norm bound are obtained. A simulation example is given to demonstrate the effectiveness and application of the proposed method. [Copyright &y& Elsevier]

Abstract: Data Broadcasting is an effective approach to provide information to a large group of clients in ubiquitous environments. How to generate the data broadcast schedule to make the clients’ average waiting time as short as possible is an important issue. In particular, when the data access pattern is dynamic and data have time constraints, such as traffic and stock information, scheduling the broadcast for such data to fulfill the requests is challenging. Since the content of the broadcast is dynamic and the request deadlines should be met, such data broadcasting is referred to as on-demand data broadcasting with time constraints. Many papers have discussed this type of data broadcasting with a single broadcast channel. In this paper, we investigate how to schedule the on-demand broadcast for the data with time constraints using multiple broadcast channels and provide two heuristics to schedule the data broadcast. The objective of the proposed heuristics is to minimize the miss rate (i.e., ratio of the number of requests missing deadlines to the number of all requests) and latency (i.e., time between issuing and termination of the request). We show that the offline version of the considered problem is NP-hard and present a competitive analysis on the proposed heuristics. More discussion about the proposed heuristics is given through extensive simulation experiments. The experimental results validate that the proposed heuristics achieve the objectives. [Copyright &y& Elsevier]

In A. Henderson, A. Laddha, and C. Tomlin, preceding article, [ Phys. Rev. D 88 044028 (2013)], we initiated an approach towards quantizing the Hamiltonian constraint in loop quantum gravity by requiring that it generates an anomaly-free representation of constraint algebra of -shell. We investigated this issue in the case of a toy model of a (2+1)-dimensional U(1)³ gauge theory, which can be thought of as a weak coupling limit of Euclidean three-dimensional gravity. However, in paper I, we only focused on the most nontrivial part of the constraint algebra that involves the commutator of two Hamiltonian constraints. In this paper we continue with our analysis and obtain a representation of full constraint algebra in a loop quantized framework. We show that there is a representation of the diffeomorphism group with respect to which the Hamiltonian constraint quantized in paper I is diffeomorphism covariant. Our work can be thought of as a potential first step towards resolving some longstanding issues with the Hamiltonian constraint in canonical loop quantum gravity. [ABSTRACT FROM AUTHOR]

Abstract: Weak sequencing is the implicit composition operator for interactions defined by the OMG specification. Accordingly, most semantics retain this operator to compose a CombinedFragment with the rest of the interactions. But all of them use only formalisms based on trace or interleaving semantics. True-concurrency-based formalisms ignore the standard interpretation and introduce synchronization on entering and exiting fragments. In this paper, we propose to revise the formal semantics of the CombinedFragments using a formalism that offers a high expressivity power to describe execution traces with regard to true concurrency as well as interleaving. We define an appropriate semantics, which is in accordance with the UML 2.4 specification regarding the event ordering over the operands and the constraints evaluation. For this purpose, we propose an approach to translate the CombinedFragments into Colored Petri Nets, or CPNs. The derived specification is value-oriented, composed of identified objects and events, thus allowing a more precise analysis of the model behavior. It is verified by model checking. A case study is given to illustrate the approach throughout the paper. [Copyright &y& Elsevier]

Abstract: The dynamic routing and wavelength assignment (DRWA) problem is a challenging optimisation problem in optical wavelength division multiplexed (WDM) networks. This paper proposes a novel evolutionary programming algorithm, namely, ERWA, to reduce the set-up time of lightpaths in a WDM network. It is shown that the proposed algorithm with simple extensions, referred to in this paper as ESRWA, is able to provide dedicated protection at the optical layer of the network, thereby addressing the survivable DRWA problem. In contrast to other bio-inspired algorithms available in the literature for the DRWA problem, ERWA facilitates the online discovery of a requested lightpath. The novelty of ERWA lies in the fact that it uses an initial population consisting of only one individual or chromosome and integrates a soft constraint with a set of hard constraints to accomplish proper load balancing. These features result in a lower mean blocking probability and a lower mean execution time compared to those exhibited by the existing bio-inspired algorithms for both the DRWA and the survivable DRWA problems. [Copyright &y& Elsevier]

In two recent papers [K. R. Dienes and B. Thomas, Phys. Rev. D 85, 083523 (2012)] [K. R. Dienes and B. Thomas, Phys. Rev. D 85, 083524 (2012)] we introduced "dynamical dark matter" (DDM), a new framework for dark-matter physics in which the requirement of stability is replaced by a delicate balancing between lifetimes and cosmological abundances across a vast ensemble of individual dark-matter components whose collective behavior transcends that normally associated with traditional dark-matter candidates. We also presented an explicit model involving axions in large extra spacetime dimensions, and demonstrated that this model has all of the features necessary to constitute a viable realization of the general DDM framework. In this paper, we complete our study by performing a general analysis of all phenomenological constraints which are relevant to this bulk-axion DDM model. Although the analysis in this paper is primarily aimed at our specific DDM model, many of our findings have important implications for bulk-axion theories in general. Our analysis can also serve as a prototype for phenomenological studies of theories in which there exist large numbers of interacting and decaying particles. [ABSTRACT FROM AUTHOR]

Abstract: Many computational- or rational-level theories of human cognition suffer from computational intractability: the postulated optimization functions are impossible to compute in a reasonable time by a finite mind/brain, or any other computational mechanism. It has been proposed that such intractable theories can nevertheless have explanatory force if we assume that human cognitive processes somehow approximate the optimal function. This raises the question of when a cognitive process can be said to approximate an optimal function. In this paper, we distinguish between two notions of approximation, called value-approximation and structure-approximation respectively, and show that they are not equivalent. Although a mathematical framework for assessing degrees of tractable value-approximability has long been available, no such framework previously existed for structure-approximability. In this paper, we present a framework consisting of definitions and proof techniques for assessing degrees of structure-approximability. We illustrate the use of our framework for a particular intractable cognitive theory, i.e., Coherence model, known to be equivalent to harmony maximization in Hopfield networks. We discuss implications of our findings for this class of theories, as well as explain how similar results may be derived for other intractable optimization theories of cognition. [Copyright &y& Elsevier]

Abstract: The service network design problem (SNDP) is a core problem in freight transportation. It involves the determination of the most cost-effective transportation network and the characteristics of the corresponding services, subject to various constraints. The scale of the problem in real-world applications is usually very large, especially when the network contains both the geographical information and the temporal constraints which are necessary for modelling multiple service-classes and dynamic events. The development of time-efficient algorithms for this problem is, therefore, crucial for successful real-world applications. Earlier research indicated that guided local search (GLS) was a promising solution method for this problem. One of the advantages of GLS is that it makes use of both the information collected during the search as well as any special structures which are present in solutions. Building upon earlier research, this paper carries out in-depth investigations into several mechanisms that could potentially speed up the GLS algorithm for the SNDP. Specifically, the mechanisms that we have looked at in this paper include a tabu list (as used by tabu search), short-term memory, and an aspiration criterion. An efficient hybrid algorithm for the SNDP is then proposed, based upon the results of these experiments. The algorithm combines a tabu list within a multi-start GLS approach, with an efficient feasibility-repairing heuristic. Experimental tests on a set of 24 well-known service network design benchmark instances have shown that the proposed algorithm is superior to a previously proposed tabu search method, reducing the computation time by over a third. In addition, we also show that far better results can be obtained when a faster linear program solver is adopted for the sub-problem solution. The contribution of this paper is an efficient algorithm, along with detailed analyses of effective mechanisms which can help to increase the speed of the GLS algorithm for the SNDP. [Copyright &y& Elsevier]

Abstract We describe the properties of a constraint satisfaction network that is able to reason and decide rationally in strategic games. We use the structure of Bidirectional Associative Memory (BAM), a minimal two-layer recurrent neural network, and assume that network layers represent self and other strategies, whereas connection weights encode best responses. We apply BAM to finite-strategy two-player games, and show that network activation in the long run is restricted to the set of rationalizable strategies. The network is not guaranteed to reach a stable activation state, but any pure strategy profile that constitutes a stable state in the network must be a pure strategy Nash equilibrium. We illustrate the properties of the network using the traveler's dilemma, the rock–paper–scissors game, and coordination games. The network's behavior also depends on starting activation states, and we show how biases in these starting states can resolve equilibrium selection problems. Strategic decision making is a key part of complex social behavior, and our results illustrate how bidirectional constraint satisfaction networks can perform rational computations in this domain. Highlights • Bidirectional associative memory is used to study game theoretic decision making. • The network makes decisions through constraint satisfaction. • Long run activation is restricted to the set of rationalizable strategies. • Any pure strategy profile in a stable state must be a pure strategy Nash equilibrium. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces an approach to fitting a constrained linear regression model to interval-valued data. Each example of the learning set is described by a feature vector for which each feature value is an interval. The new approach fits a constrained linear regression model on the midpoints and range of the interval values assumed by the variables in the learning set. The prediction of the lower and upper boundaries of the interval value of the dependent variable is accomplished from its midpoint and range, which are estimated from the fitted linear regression models applied to the midpoint and range of each interval value of the independent variables. This new method shows the importance of range information in prediction performance as well as the use of inequality constraints to ensure mathematical coherence between the predicted values of the lower () and upper () boundaries of the interval. The authors also propose an expression for the goodness-of-fit measure denominated determination coefficient. The assessment of the proposed prediction method is based on the estimation of the average behavior of the root-mean-square error and square of the correlation coefficient in the framework of a Monte Carlo experiment with different data set configurations. Among other aspects, the synthetic data sets take into account the dependence, or lack thereof, between the midpoint and range of the intervals. The bias produced by the use of inequality constraints over the vector of parameters is also examined in terms of the mean-square error of the parameter estimates. Finally, the approaches proposed in this paper are applied to a real data set and performances are compared. [Copyright &y& Elsevier]

Abstract: Layered image transmission is successfully adopted for different system applications. Generally, in this technique, image is divided into number of layers for easy and fast transmission keeping the concept of multi-description representation. This configuration helps to make overall system adaptive with respect to different system''s bandwidth. Proposed data compression layered scheme''s architecture not only works with JPEG but also with MPEG image formats. It includes all necessary steps and precautions to handle possible disturbances in image compression by analyzing different parameters affecting image quality and transmission time. In this paper, behavioral model of complete image compression scheme is discussed starting from image extraction from source, layering of image matrix, generating separate pixel layers for concurrent compression followed by efficient coder operation. This paper also addresses different parameters, which improve image quality with different constraints and limitations encountered during design verification. This proposed scheme is comprised of pre-coder, efficient transformation scheme followed by adaptive Huffman encoding/decoding in conjunction with quantization of image matrix which makes overall compression system lossy in nature. Due to flexibility of operation, this scheme can be applicable in different applications like Robot vision and real time video compression, etc. The results obtained after mathematical analysis and simulation are found useful to prove efficiency of layered compression scheme. [Copyright &y& Elsevier]

Abstract: Previous algorithms for unrestricted constraint satisfaction use reduction search, which inferentially removes values from domains in order to prune the backtrack search tree. This paper introduces partition search, which uses an efficient join mechanism instead of removing values from domains. Analytical prediction of quantitative performance of partition search appears to be intractable and evaluation therefore has to be by experimental comparison with reduction search algorithms that represent the state of the art. Instead of working only with available reduction search algorithms, this paper introduces enhancements such as semijoin reduction preprocessing using Bloom filtering. [Copyright &y& Elsevier]

Combinatorial optimization over continuous and integer variables is a useful tool for solving complex optimal control problems of hybrid dynamical systems formulated in discrete- time. Current approaches are based on mixed-integer linear (or quadratic) programming (MIP), which provides the solution after solving a sequence of relaxed linear (or quadratic) programs. MIP formulations require the translation of the discrete/logic part of the hybrid problem into mixed-integer inequalities. Although this operation can be done automatically, most of the original symbolic structure of the problem (e.g., transition functions of finite state machines, logic constraints, symbolic variables, etc.) is lost during the conversion, with a consequent loss of computational performance. In this paper, we attempt to overcome such a difficulty by combining numerical techniques for solving convex programming problems with symbolic techniques for solving constraint satisfaction problems (CSP). The resulting ‘hybrid’ solver proposed here takes advantage of CSP solvers for dealing with satisfiability of logic constraints very efficiently. We propose a suitable model of the hybrid dynamics and a class of optimal control problems that embrace both symbolic and continuous variables/functions, and that are tailored to the use of the new hybrid solver. The superiority in terms of computational performance with respect to commercial MIP solvers is shown on a centralized supply chain management problem with uncertain forecast demand. [ABSTRACT FROM AUTHOR]

• A RBF-assisted MOEA/D framework is designed to achieve adaptive search. • Different optimization states of subproblems are effectively identified by OSDM. • Optimization state-driven search strategies are used for targeted suboptimization. • The RBF accuracies are gradually improved by emphasizing specific points. • The performance difference of two classical decompositions has been well studied. In this paper, an adaptive surrogate-assisted MOEA/D framework (ASA-MOEA/D) is proposed for solving computationally expensive constrained multi-objective optimization problems, in which three specific search strategies are adaptively implemented based on the optimization states of subproblems to achieve targeted searches for different subproblems. To maintain feasibility, the RBF-based local search models are constructed by comprehensively considering the orthogonal distance difference and constraint satisfaction information for guiding infeasible solutions of the infeasible subproblems into feasible regions. To maintain convergence, the RBF surrogates of the aggregated objective and constraints are employed to construct local search models for locating better feasible solutions. To maintain diversity, the subregions of unexplored subproblems are effectively explored by utilizing the valuable information of their neighboring elite solutions. Moreover, the solution with the maximum overall uncertainty of RBF surrogates is selected for progressively increasing the prediction accuracies of surrogates. Therefore, ASA-MOEA/D strikes an adaptive balance among diversity, feasibility and convergence with the assistance of RBF surrogates as the optimization progresses. Empirical studies on three classical test suites demonstrate that ASA-MOEA/D with tchebycheff approach achieves highly competitive performance over other four state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In emerging Manufacturing Systems (MSs), flexibility is a key issue. It is related to the ability for a MS to recognize the context and switch its workflow accordingly. Although the literature has provided automated options to model and control MSs, programming context-dependent controllers remains challenging. This is an event-based construction that integrates a large and intricate combination of events and states in order to make the controller flexible, i.e., include context-sensitiveness strategies subject to switching at runtime. Without self-adaptation, each system configuration may require an entire control solution to be recalculated, which implies redesigning the whole modeling and implementation structures. This paper shows that a system model can nevertheless be enriched with elements collected from the context, which optimizes the design of formula-based constraints that can then be integrated to control frameworks for synthesis and code generation. The result is a controller that recognizes the context and takes control decisions accordingly. Examples are provided to illustrate the approach. [ABSTRACT FROM AUTHOR]