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This paper is concerned with iterative learning control (ILC) algorithms for two-dimensional (2-D) linear discrete systems described by the first Fornasini–Marchesini model (FMMI) with iteration-varying reference trajectories/profiles. The variation of reference trajectories in iteration domain is represented by a high-order internal model (HOIM) formula. Robustness and convergence of two types of HOIM-based ILC laws with different boundary conditions are investigated, respectively. A strategy employed in this paper is to reconstruct the HOIM-based ILC process of the 2-D linear FMMI system into a set of 2-D linear inequalities or a 2-D linear Roesser model such that sufficient robustness/convergence conditions of the HOIM-based ILC laws are obtained. Under random boundary conditions, the designed ILC law (9) is capable to drive the ILC tracking error into a bounded range. Moreover, under the HOIM-based boundary conditions, a perfect tracking to the iteration-varying reference trajectories can be achieved by utilizing the proposed ILC law (32). Two simulation examples are given to validate the effectiveness of the two proposed ILC algorithms. [ABSTRACT FROM AUTHOR]

This paper establishes an approximate optimal critic learning algorithm based on single neural network (NN) policy iteration (PI) aiming at solving for continuous-time (CT) 2-player zero-sum games (ZSGs). In fact, we have to face the problem that the errors will disturb the dynamics and in turn identifying dynamics will generate errors. In order to prevent the effect of errors, in this paper, a single NN-based online PI algorithm is developed for the CT system, which is disturbed nonlinear ZSG. With plenty of online data, the Hamilton–Jacobi–Isaacs equation can be solved without complete dynamics. Then by the least-squares method, we can obtain the NN weights. Moreover, in the process of dealing with the undisturbed system, we find the way that obtains NN weights in this paper is equal to the way that obtains the optimal solution by the Gauss–Newton method. Based on the convergence of the Gauss–Newton method, we can efficiently obtain the optimal controller for the undisturbed system by utilizing online data. After getting the controller of the undisturbed system, it is time to take disturbance into consideration, so that we design a robust control pair to overcome the disturbance. In order to demonstrate the effectiveness of this algorithm, we design a set of simulations. The results verify that we can solve the disturbed nonlinear ZSG by this algorithm. [ABSTRACT FROM AUTHOR]

Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally. [ABSTRACT FROM AUTHOR]

As a class of batch-mode reinforcement learning (RL) methods for Markov decision problems with large or continuous state spaces, approximate policy iteration (API) has received increasing attention in the past decades. One open problem in the design of API algorithms is how to construct the basis functions or features for value function approximation (VFA). In this paper, we propose a novel batch-mode RL approach with randomly projected features for VFA. The proposed approach can be viewed as an extension of extreme learning machines (ELMs) to RL problems so it can be called ELM-API. The ELMs have been popularly studied in supervised learning problems, but there is not much work on the extension of ELMs to learning control problems. The proposed approach has advantages over the previous API algorithms in that the features for VFA can be quickly generated without complex parameter selection and the performance will be adaptive to different sample sets in batch-mode RL. In particular, the ELM-API approach can realize fast and efficient feature reconstruction when training sample sets are relatively small. Comprehensive simulation studies on two benchmark learning control problems were carried out to test the performance of API algorithms with different feature construction methods. It is shown that the ELM-API algorithm can obtain comparable or better performance than the previous API approaches. To further show the effectiveness of ELM-API in real-world applications, the simulation results on a more challenging high-dimensional lane-changing decision problem in dynamic traffic environment are also reported, which show the capability of the ELM-API algorithm in learning satisfactory lane-changing policies with high data efficiency. [ABSTRACT FROM AUTHOR]

This paper studies two distributed resource allocation problems of second-order systems over weight-balanced communication networks. In the first problem, the decisions of agents are coupled by network resource constraints, and in the second problem, the decisions of agents are constrained by local constraints and network resource constraints. Compared with many existing resource allocation problems, the formulation involves the dynamics of agents. The second-order dynamics of agents induce the difficult in algorithm design and analysis, since the decisions of agents could not be directly decided by their control inputs. In order to optimally allocate the network resource, two distributed algorithms are designed via state feedback and gradient descent for the two problems, respectively. Besides, the convergence of the two algorithms are analyzed. By the two algorithms, the second-order agents converge to the optimal allocation of the two problems, respectively. Finally, two examples about economic dispatch problems verify the two algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an evolutionary algorithm-based energy-saving control strategy to control a highly nonlinear and time-varying active magnetic bearing (AMB) positioning system that considers energy efficiency and control performance simultaneously. Most AMB control schemes use a bias current with a superimposed control current to improve the linearity and dynamic performance of the system. However, the bias current causes power losses even if no electromagnetic force is required. As such, a recurrent wavelet fuzzy neural network with adaptive differential evolution (RWFNN-ADE)-based dynamic bias current control strategy is proposed in this paper so as to minimize the energy consumed by an AMB without altering its positioning performance and robustness. To begin with, this paper analyzes the operation principle of the AMB positioning system with a differential driving mode. Subsequently, the proposed RWFNN-ADE control scheme, in which the control current and bias current are controlled by the RWFNN and ADE, respectively, is introduced in detail. Finally, the experimental results demonstrate the high-accuracy control and significant energy-saving performances of the proposed RWFNN-ADE-controlled AMB positioning system. In the tests corresponding to operation periods of 10 and 50 s, the energy improvements compared to the baseline values were 20.24% and 17.65%, respectively, in nominal cases, and 18.89% and 18.68%, respectively, in parameter variation cases, for the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents a three-dimensional (3-D) path planning method that combines the gliding with dolphin-like motions for the gliding robotic dolphin. A specific task that the robot uses the gliding motion for long-distance cruise and the dolphin-like motion for maneuverable obstacle avoidance is employed. The results of simulations and aquatic experiments validate the full-state dynamic model and the specific task, further offer some theoretical supports for 3-D path planning. Further, the 3-D path planning method is composed of three main components: 1) gliding path generation; 2) improved Astar (A*) algorithm; and 3) segmented Bezier curve smoothing. First, the gliding path is generated autonomously with the kinematic constraints that are obtained via the simulations of dynamic model. Furthermore, when the obstacles are detected by the sonar, an improved A* algorithm is employed to avoid the obstacles. Afterward, considering the path planned by A* is unsmoothed, a segmented Bezier curve method is presented. Simulation results demonstrate the effectiveness of the method, offering valuable insight into the utilization of hybrid underwater robots in the context of real-time task execution. [ABSTRACT FROM AUTHOR]

In this paper, we propose an enhanced branch-and-bound (B&B) framework for a class of sequencing problems, which aim to find a permutation of all involved elements to minimize a given objective function. We require that the sequencing problems satisfy three conditions: 1) incrementally computable; 2) monotonic; and 3) overlapping subproblems. Our enhanced B&B framework is built on the classical B&B process by introducing two techniques, i.e., dominance rules and caching search states. Following the enhanced B&B framework, we conduct empirical studies on three typical and challenging sequencing problems, i.e., quadratic traveling salesman problem, traveling repairman problem, and talent scheduling problem. The computational results demonstrate the effectiveness of our enhanced B&B framework when compared to classical B&B and some exact approaches, such as dynamic programming and constraint programming. Additional experiments are carried out to analyze different configurations of the algorithm. [ABSTRACT FROM AUTHOR]

Nonlinear optimization problems with dynamical parameters are widely arising in many practical scientific and engineering applications, and various computational models are presented for solving them under the hypothesis of short-time invariance. To eliminate the large lagging error in the solution of the inherently dynamic nonlinear optimization problem, the only way is to estimate the future unknown information by using the present and previous data during the solving process, which is termed the future dynamic nonlinear optimization (FDNO) problem. In this paper, to suppress noises and improve the accuracy in solving FDNO problems, a novel noise-tolerant neural (NTN) algorithm based on zeroing neural dynamics is proposed and investigated. In addition, for reducing algorithm complexity, the quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is employed to eliminate the intensively computational burden for matrix inversion, termed NTN-BFGS algorithm. Moreover, theoretical analyses are conducted, which show that the proposed algorithms are able to globally converge to a tiny error bound with or without the pollution of noises. Finally, numerical experiments are conducted to validate the superiority of the proposed NTN and NTN-BFGS algorithms for the online solution of FDNO problems. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered distributed ${ {\mathcal {H}}_{\infty }}$ constrained control algorithm is designed for physically interconnected large-scale partially unknown strict-feedback systems with constrained-input and external disturbance. The advantage of both physical interconnection and communication is synchronously exploited for the scheme. First, an event-triggered feedforward control policy is proposed to transform control of physically interconnected large-scale systems into equivalent event-triggered control of decoupled multiagent systems. Then, an event-triggering condition and an event-triggered feedback control algorithm are designed to learn the optimal control policy and the disturbance policy in the worst case. The algorithm eliminates identifier, actor, and disturber neural networks and also relaxes the persistent excitation condition. It guarantees that the closed-loop dynamics is stabilized and the cost function is converged to the bounded ${\mathcal {L}}_{2}$ -gain optimal value while the Zeno phenomenon is excluded. Finally, the effectiveness of the proposed algorithm is verified through simulation results of event-triggered distributed control of a physically interconnected constrained-torques multimobile robot system. [ABSTRACT FROM AUTHOR]

In this paper, a robust output voltage-tracking algorithm is proposed for dc/dc boost power conversion systems based on a variable invariant manifold. A systematic multivariable approach, considering not only nonlinearity but also the parametric uncertainties, is used for deriving the control law. The proposed method has two features. First, a variable cut-off frequency algorithm is constructed to automatically adjust the invariant manifold, improving the transient output voltage-tracking performance. Second, nonlinear disturbance observers are introduced to enable the control law to exponentially recover the desirable tracking performance without any offset errors in the variable invariant manifold. The performance of the proposed technique is experimentally confirmed with a 3-kW dc/dc boost power conversion system. [ABSTRACT FROM AUTHOR]

This paper investigates the resource allocation problem for a group of agents communicating over a strongly connected directed graph, where the total objective function of the problem is composted of the sum of the local objective functions incurred by the agents. With local convex sets, we first design a continuous-time projection algorithm over a strongly connected and weight-balanced directed graph. Our convergence analysis indicates that when the local objective functions are strongly convex, the output state of the projection algorithm could asymptotically converge to the optimal solution of the resource allocation problem. In particular, when the projection operation is not involved, we show the exponential convergence at the equilibrium point of the algorithm. Second, we propose an adaptive continuous-time gradient algorithm over a strongly connected and weight-unbalanced directed graph for the reduced case without local convex sets. In this case, we prove that the adaptive algorithm converges exponentially to the optimal solution of the considered problem, where the local objective functions and their gradients satisfy strong convexity and Lipachitz conditions, respectively. Numerical simulations illustrate the performance of our algorithms. [ABSTRACT FROM AUTHOR]

Virtual network embedding (VNE) is an important problem in network virtualization for the flexible sharing of network resources. While most existing studies focus on centralized embedding for VNE, distributed embedding is considered more scalable and suitable for large-scale scenarios, but how virtual resources can be mapped to substrate resources effectively and efficiently remains a challenging issue. In this paper, we devise a distributed VNE system with historical archives (HAs) and metaheuristic approaches. First, we introduce metaheuristic approaches to each delegation of the distributed embedding system as the optimizer for VNE. Compared to the heuristic-based greedy algorithms used in existing distributed embedding approaches, which are prone to be trapped in local optima, metaheuristic approaches can provide better embedding performance for these distributed delegations. Second, an archive-based strategy is also introduced in the distributed embedding system to assist the metaheuristic algorithms. The archives are used to record the up-to-date information of frequently repeated tasks. By utilizing such archives as historical memory, metaheuristic algorithms can further improve embedding performance for frequently repeated tasks. Following this idea, we incorporate the set-based particle swarm optimization (PSO) as the optimizer and propose the distributed VNE system with HAs and set-based PSO (HA-VNE-PSO) system to solve the VNE problem in a distributed way. HA-VNE-PSO is empirically validated in scenarios of different scales. The experimental results verify that HA-VNE-PSO can scale well with respect to substrate networks, and the HA strategy is indeed effective in different scenarios. [ABSTRACT FROM AUTHOR]

This paper is concerned with the optimal output synchronization control problem for a class of heterogeneous complex dynamical networks (CDNs) with partially unknown system dynamics. By defining a new quadratic performance index including the information of state couplings, an optimal controller consisting of a feedback control and a compensated feedforward control is then designed. A necessary and sufficient condition for the optimal control minimizing the quadratic performance index is established. Especially, an algebraic Riccati equation (ARE) associated with a discount factor is obtained, and a novel online iteration algorithm by iteratively solving the ARE is proposed to compute the optimal controller gain for the case that the CDNs are with partially unknown system dynamics. Moreover, it is proved that the output synchronization errors converge to zero asymptotically by applying the optimal control laws. Finally, two simulation examples are provided to verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper suggests a position-tracking algorithm for the outer-loop through the invariant dynamic surface approach for balancing robot applications. The main feature is to devise a dynamic surface representing the target position-tracking performance with variable cut-off frequency. The proposed controller makes the dynamic surface invariant while updating the closed-loop cut-off frequency accordingly with the self-tuner. The closed-loop properties are rigorously analyzed. The experimental verification result shows that the proposed controller establishes the 48% enhancement of the circular tracking performance in comparison with the feedback linearization method, where the LEGO Mindstorms EV3 is used. [ABSTRACT FROM AUTHOR]

The second-order sliding mode (SOSM) controller design problem for a class of sliding mode dynamics subject to an upper-triangular structure has been discussed in this paper. The proposed SOSM controller design involves two steps. First, a Lyapunov-based SOSM controller is developed by using the adding a power integrator technique to locally finite-time stabilize the sliding variables. Second, by combining the local SOSM controller with a saturation function, a novel SOSM controller with a saturation level is constructed. The feature of the new SOSM controller lies in that the saturation level can be tuned not only to guarantee the global convergence but also to improve the dynamic performance. Lyapunov analysis has been utilized to test the finite-time stability of the closed-loop sliding mode dynamics. The proposed method is eventually demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive dynamic programming (ADP) algorithm is developed for the infinite-horizon ($H_{\infty}$) optimal control problems with unknown continuous-time (CT) nonlinear systems subject to external disturbances. To facilitate the implementation of the algorithm, generalized fuzzy hyperbolic models (GFHMs) are utilized to establish an identifier–critic architecture, where the identifier is designed to reconstruct the unknown system dynamics, and the GFHM-based critic network is employed to approximate the value functions. The CT $H_{\infty}$ optimal control issue is converted into a two-player zero-sum game and the corresponding Hamilton–Jacobi–Isaacs equation is derived. The learning procedure of the critic design is adaptively implemented with the help of the reconstructed model, thus the requirement of the complete system dynamics is relaxed. Furthermore, by the means of Lyapunov direct method, the uniform ultimate boundedness stability analysis of the closed-loop control system is explicitly provided. Finally, to compare the control performances and disturbance attenuation properties of the proposed method and the existing ADP algorithms, two numerical examples are given. [ABSTRACT FROM AUTHOR]

In this paper, a novel policy iteration adaptive dynamic programming (ADP) algorithm is presented which is called “local policy iteration ADP algorithm” to obtain the optimal control for discrete stochastic processes. In the proposed local policy iteration ADP algorithm, the iterative decision rules are updated in a local space of the whole state space. Hence, we can significantly reduce the computational burden for the CPU in comparison with the conventional policy iteration algorithm. By analyzing the convergence properties of the proposed algorithm, it is shown that the iterative value functions are monotonically nonincreasing. Besides, the iterative value functions can converge to the optimum in a local policy space. In addition, this local policy space will be described in detail for the first time. Under a few weak constraints, it is also shown that the iterative value function will converge to the optimal performance index function of the global policy space. Finally, a simulation example is presented to validate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

For systems that can only be locally stabilized, control laws and their effective regions are both important. In this paper, invariant policy iteration is proposed to solve the optimal control of discrete-time systems. At each iteration, a given policy is evaluated in its invariantly admissible region, and a new policy and a new region are updated for the next iteration. Theoretical analysis shows the method is regionally convergent to the optimal value and the optimal policy. Combined with sum-of-squares polynomials, the method is able to achieve the near-optimal control of a class of discrete-time systems. An invariant adaptive dynamic programming algorithm is developed to extend the method to scenarios where system dynamics is not available. Online data are utilized to learn the near-optimal policy and the invariantly admissible region. Simulated experiments verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, an $H_{\infty }$ optimal tracking controller for completely unknown discrete-time nonlinear systems with control constraints is obtained by using an iterative adaptive learning algorithm. An augmented system is established by integrating the tracking error system and the reference trajectory. As an identifier of the unknown systems, a neural network (NN) is introduced with asymptotic stability of the estimation error. An action–disturbance–critic NN structure is proposed to implement the iterative dual heuristic programming algorithm with convergence guarantee of the costate function and the control policy. Simulation results and comparisons are provided to illustrate the superior performance of the designed optimal tracking controller. [ABSTRACT FROM AUTHOR]

In this paper, a distributed optimal steady-state regulation problem is formulated and investigated for heterogeneous linear multiagent systems subject to external disturbances. We aim to steer this high-order multiagent network to a prescribed steady-state determined as the optimal solution of a resource allocation problem in a distributed way. To solve this problem, we employ an embedded control design and convert the formulated problem to two simpler subproblems. Then, both state-feedback and output feedback controls are presented under mild assumptions to solve this problem with disturbance rejection. Moreover, we extend these results to the case with only real-time gradient information by high-gain control techniques. Finally, the numerical simulations verify their effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a deep reinforcement learning-based robust control strategy for quadrotor helicopters is proposed. The quadrotor is controlled by a learned neural network which directly maps the system states to control commands in an end-to-end style. The learning algorithm is developed based on the deterministic policy gradient algorithm. By introducing an integral compensator to the actor-critic structure, the tracking accuracy and robustness have been greatly enhanced. Moreover, a two-phase learning protocol which includes both offline and online learning phase is proposed for practical implementation. An offline policy is first learned based on a simplified quadrotor model. Then, the policy is online optimized in actual flight. The proposed approach is evaluated in the flight simulator. The results demonstrate that the offline learned policy is highly robust to model errors and external disturbances. It also shows that the online learning could significantly improve the control performance. [ABSTRACT FROM AUTHOR]

The real-time data generated from various smart devices will be released and shared for public to obtain numerous benefits. However, it will lead to individual privacy leakage because of data mining or analysis. Currently, many existing privacy protection models either fail to be directly applied in real-time data release or are unsatisfactory in terms of data utility and privacy protection. Toward this end, based on m-signature and fuzzy processing, an anonymous privacy protection model, named PMF, is proposed in this paper. Specifically, for the proposed model there are five advantages: 1) PMF defines m-signature for making each bucket with at least m different sensitive values instead of generating any counterfeit tuples, which can not only resist h-difference attack but also improve practical value; 2) the buckets satisfying m-signature are variable over time, and this flexibility of m-signature can improve the efficiency of dynamic update; 3) PMF can effectively insert, delete, and modify real-time data for release; 4) PMF applies fuzzy processing to handle the tuples in the candidate list, which strikes a good balance between the utility of released data and privacy protection; and 5) PMF adopts greedy heuristic algorithm to process update operations, which greatly reduces the information loss of released data. Furthermore, PMF is obviously more secure than the existing models in the real-time data release. Finally, the results of the comparison experiments on real-world and synthetic datasets illustrate that PMF is superior to the existing models in terms of data utility and efficiency. [ABSTRACT FROM AUTHOR]

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages. [ABSTRACT FROM AUTHOR]

In this paper, a novel event-sampled robust optimal controller is proposed for a class of continuous-time constrained-input nonlinear systems with unknown dynamics. In order to solve the robust optimal control problem, an online data-driven identifier is established to construct the system dynamics, and an event-sampled critic-only adaptive dynamic programming method is developed to replace the conventional time-driven actor–critic structure. The designed online identification method runs during the solving process and is not applied as a priori part for the solutions, which simplifies the architecture and reduces computational load. The proposed robust optimal control algorithm tunes the parameters of critic-only neural network (NN) by event-triggering condition and runs in a plug-n-play framework without system functions, where fewer transmissions and less computation are required as all the measurements received simultaneously. Based on the novel design, the stability of system and the convergence of critic NN are demonstrated by Lyapunov theory, where the state is asymptotically stable and weight error is guaranteed to be uniformly ultimately bounded. Finally, the applications in a basic nonlinear system and the complex rotational–translational actuator problem demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed continuous-time optimization problem, which consists of a group of agents with variant local cost functions. An adaptive consensus-based algorithm with event triggering communications is introduced, which can drive the participating agents to minimize the global cost function and exclude the Zeno behavior. Compared to the existing results, the proposed event-based algorithm is independent of the parameters of the cost functions, using only the relative information of neighboring agents, and hence is fully distributed. Furthermore, the constraints of the convexity of the cost functions are relaxed. [ABSTRACT FROM AUTHOR]

Under consensus-based economic dispatch (ED) algorithm, multiple agents, which control local generation units, cooperatively minimize the total generation cost subject to the balance of the generation and expected demand in smart grids. As ubiquitous time delays on communication links exist in communication networks, studying the effect of delays on the dispatch performance is of both theoretical merit and practical value for the efficient and stable operation of smart grids. In this paper, we consider a well-developed consensus-based ED protocol under constant time delays. We find that there always exists a sufficiently small learning gain parameter under finite constant delays such that the convergence of the consensus-based algorithm is guaranteed. Further, an analytical expression of the upper bound is established for the learning gain parameter, which is determined by the largest delay, the weight matrix and the parameters of generation cost functions. In order to guarantee the optimality of the final solution, we propose the updating rule for iterations when initial states are not received by their neighbors due to time delays. The optimality of the final solution under the proposed updating rule is analyzed. We validate our theoretical results through extensive simulation studies. [ABSTRACT FROM AUTHOR]

The key problems in Bayesian network (BN) modeling are how to consider expert knowledge and select a structure learning algorithm for system risk assessment, because of the uncertainty and inaccuracy in the expert knowledge and high time-consumption in the structure learning for real systems. A control change cause analysis (3CA)-based BN modeling approach is proposed for system risk assessments in this paper. 3CA is used to identify the substances and causal relationships among different nodes in a new four-layer BN model, including cause, change, ineffective control, and risk layers. The four-layer BN model can be built by connecting the four-layer nodes with causal relationships, thus avoiding the use of a complicated structure learning algorithm. We use the proposed approach to perform risk assessment of a vehicle engine fuel supply system and obtain the assessment results by calculating the posterior probability of causes that lead to different operational risks. It can be used in various complex systems to perform their risk assessment conveniently and successfully. [ABSTRACT FROM AUTHOR]

Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet—a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Crowd density estimation is one of the critical issues in social activities. The traditional solution to this problem is to leverage video surveillance to monitor a crowd. However, this is not accurate for crowd density estimation because it is still hard to identify people from background. In the past few years, more and more people use Wi-Fi enabled smartphones. Smartphones can send Wi-Fi request packets periodically, even when they are not connected to access points. This gives another promising solution to the crowd density estimation even for the public environment. In this paper, we first develop a Wi-Fi monitor detection that can capture smartphone passive Wi-Fi signal information including MAC address and received signal strength indicator. Then, we propose a positioning algorithm based on smartphone passive Wi-Fi probe and a dynamic fingerprint management strategy. In real-world public social activities, a person may have zero, one, two, or multiple smartphones with variant Wi-Fi signals. Therefore, we design a method of computing the probability of a user generating one Wi-Fi signal to identify people population. Finally, we propose a crowd density estimation solution based on Wi-Fi probe packets positioning algorithm. Experiments were conducted in an indoor laboratory class and three public social activities, clearly demonstrated that the proposed solution can effectively and accurately estimate crowd density. [ABSTRACT FROM AUTHOR]

Owing to environmental concerns, the energy-efficient subway train scheduling problem is necessary in subway operation management. This paper designs an approximate dynamic programming (DP) approach for energy-efficient subway train scheduling problem with time-dependent demand. The train traffic model is proposed with the dynamic equations for the evolution of train headway, train passenger loads, and the energy consumption along the subway line. For the dynamic changing of the onboard passengers with time, the total train energy usage is modeled as the sum of energy consumptions from the traction system and auxiliary facilities. A nonlinear DP problem is formulated to generate a near optimal timetable to realize the tradeoff among the utilization of trains, passenger waiting time, service levels, and energy consumption. To overcome the curse of dimensionality in this optimization problem, we construct an approximate DP framework, where the conceptions of states, policies, state transitions, and reward function are introduced. And this algorithm is able to converge to a good solution with a short time compared to the genetic algorithm and differential evolution algorithm. Finally, the numerical experiments are given to demonstrate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Aiming to solve the cluster lag consensus (CLC) problem of networked nonlinear agents with a switching topologies, this paper proposes a control algorithm based on the measurements of position and velocity. When the network has several clusters of agents, under the conditions that each cluster is a strongly connected digraph including isolated agents, and at least one agent in each cluster can communicate with the common leader each time, a sufficient condition is derived for reaching CLC. Finally, a numerical simulation is demonstrated to validate the proposed control algorithm and the developed conditions. [ABSTRACT FROM AUTHOR]

Nonlinear equations system (NES) arises commonly in science and engineering. Repulsion techniques are considered to be the effective methods to locate different roots of NES. In general, the repulsive radius needs to be given by the user before the run. However, its optimal parameter setting is difficult and problem-dependent. To alleviate this drawback, in this paper, we first propose a dynamic repulsion technique, and then a general framework based on the dynamic repulsion technique and evolutionary algorithms (EAs) is presented to effectively solve NES. The major advantages of our framework are: 1) the repulsive radius is controlled dynamically during the evolutionary process; 2) multiple roots of NES can be simultaneously located in a single run; 3) the diversity of the population is preserved due to the population reinitialization; and 4) different repulsion techniques and different EAs can be readily integrated into this framework. To extensively evaluate the performance of our framework, we choose 42 problems with diverse features as the test suite. In addition, some representative differential evolution and particle swarm optimization variants are incorporated into the framework. Our method is also compared with other state-of-the-art methods. Experimental results indicate that the dynamic repulsion technique can improve the performance of the original repulsion technique with static repulsive radius. Moreover, the proposed method is able to yield better results compared with other methods. [ABSTRACT FROM AUTHOR]

Nowadays, datasets are always dynamic and patterns in them are changing. Instances with different labels are intertwined and often linearly inseparable, which bring new challenges to traditional learning algorithms. This paper proposes adaptive hyper-sphere (AdaHS), an adaptive incremental classifier, and its kernelized version: Nys-AdaHS. The classifier incorporates competitive training with a border zone. With adaptive hidden layer and tunable radii of hyper-spheres, AdaHS has strong capability of local learning like instance-based algorithms, but free from slow searching speed and excessive memory consumption. The experiments showed that AdaHS is robust, adaptive, and highly accurate. It is especially suitable for dynamic data in which patterns are changing, decision borders are complicated, and instances with the same label can be spherically clustered. [ABSTRACT FROM AUTHOR]

In this paper, we aim at developing a new collaborative filtering recommender system using soft ratings, which is capable of dealing with both imperfect information about user preferences and the sparsity problem. On the one hand, Dempster–Shafer theory is employed for handling the imperfect information due to its advantage in providing not only a flexible framework for modeling uncertain, imprecise, and incomplete information, but also powerful operations for fusion of information from multiple sources. On the other hand, in dealing with the sparsity problem, community context information that is extracted from the social network containing all users is used for predicting unprovided ratings. As predicted ratings are not a hundred percent accurate, while the provided ratings are actually evaluated by users, we also develop a new method for calculating user–user similarities, in which provided ratings are considered to be more significant than predicted ones. In the experiments, the developed recommender system is tested on two different data sets; and the experiment results indicate that this system is more effective than CoFiDS, a typical recommender system offering soft ratings. [ABSTRACT FROM AUTHOR]

In this paper, a data-based control method based on adaptive dynamic programming (ADP) algorithm is proposed for a class of discrete-time (DT) systems in the case of multiple delays. Data-based ADP method is implemented by virtue of the measured input and output data. The condition of the existence of the corresponding equivalent multiple delays system is derived according to the characteristics of time-delay system. A novel data-based state equation is developed that only composed of input and output data, which is very meaningful in practical applications. By using the data-based ADP method, the output feedback control problem is solved only by measuring the input and output of the system with multiple delays. The convergence proofs of the designed policy iteration and value iteration algorithms are given, respectively. The simulation example is presented to demonstrate the validity of the proposed data-based ADP method. [ABSTRACT FROM AUTHOR]

The spiral optimization (SPO) algorithm proposed by Tamura and Yasuda is a relatively novel and simple search concept inspired by natural spiral phenomena. This algorithm searches continuous space using no gradient and only spiral trajectories composed of spiral vectors generated by deterministic spiral models. The primary purpose of this paper is to propose conditions and settings that mathematically ensure the convergence of the SPO algorithm to a stationary point. The conditions relating to the sizes and directions of the spiral vectors and the initial search points are based on direct search theory and recent SPO algorithm theories. The presented convergence was numerically verified using test functions with different properties. [ABSTRACT FROM AUTHOR]

As the mutation strategy and algorithmic parameters in differential evolution (DE) are sensitive to the problems being solved, a hot research topic is to adaptively control the strategy and parameters according to the requirements of the problem. In the literature, most adaptive DE use either historical experiences of the population or heuristic information of the individuals to promote adaptation. In this paper, we develop a novel variant of adaptive DE, utilizing both the historical experience and heuristic information for the adaptation. In this novel historical and heuristic DE (HHDE), each individual dynamically adjusts its mutation strategy and associated parameters not only by learning from previous successful experience of the whole population, but also according to heuristic information related with its own current state. These help the algorithm select a more suitable mutation strategy and determinate better parameters for each individual in different evolutionary stages. The performance of the proposed HHDE is extensively evaluated on 30 benchmark functions with different dimensions. Experimental results confirm the competitiveness of the proposed algorithm to a number of DE variants. [ABSTRACT FROM AUTHOR]

Inspired by Nash game theory, a multiplayer mixed-zero-sum (MZS) nonlinear game considering both two situations [zero-sum and nonzero-sum (NZS) Nash games] is proposed in this paper. A synchronous reinforcement learning (RL) scheme based on the identifier-critic structure is developed to learn the Nash equilibrium solution of the proposed MZS game. First, the MZS game formulation is presented, where the performance indexes for players 1 to ${N}$ − 1 and ${N}$ NZS Nash game are presented, and another performance index for players ${N}$ and ${N}$ + 1 zero-sum game is presented, such that player ${N}$ cooperates with players 1 to ${N}$ − 1, while competes with player ${N}$ + 1, which leads to a Nash equilibrium of all players. A single-layer neural network (NN) is then used to approximate the unknown dynamics of the nonlinear game system. Finally, an RL scheme based on NNs is developed to learn the optimal performance indexes, which can be used to produce the optimal control policy of every player such that Nash equilibrium can be obtained. Thus, the widely used actor NN in RL literature is not needed. To this end, a recently proposed adaptive law is used to estimate the unknown identifier coefficient vectors, and an improved adaptive law with the error performance index is further developed to update the critic coefficient vectors. Both linear and nonlinear simulations are presented to demonstrate the existence of Nash equilibrium for MZS game and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In network-centric warfare, the interconnections among various combat resources enable an advanced operational pattern of cooperative engagement. The operational effectiveness and outcome strongly depends on the reasonable utilization of available sensors and weapons. In this paper, a mathematical model for the coallocation of sensors and weapons is built, taking into account the interdependencies between weapons and sensors, the resource constraints, the capability constraints, as well as the strategy constraints. A marginal-return-based constructive heuristic (MRBCH) is proposed to solve the formulated sensor–weapon–target assignment (S-WTA) problem. MRBCH exploits the marginal return of each sensor–weapon–target triplet and dynamically updates the threat value of all targets. It relies only on simple lookup operations to choose each assignment triplet, thus resulting in very low computational complexity. For performance evaluation, we build a general Monte Carlo simulation-based S-WTA framework. Furthermore, we employ a random sampling method and an extension of the state-of-the-art algorithm Swt_opt as competitors. The computational results show that MRBCH consistently performs very well in solving S-WTA instances of different scales, and it can generate assignment schemes much more efficiently than its competitors. [ABSTRACT FROM AUTHOR]

Based on the multiagent networks, this paper introduces a continuous-time algorithm to deal with distributed convex optimization. Using nonsmooth analysis and algebraic graph theory, the distributed network algorithm is modeled by the aid of a nonautonomous differential inclusion, and each agent exchanges information from the first-order and the second-order neighbors. For any initial point, the solution of the proposed network can reach consensus to the set of minimizers if the graph has a spanning tree. In contrast to the existing continuous-time algorithms for distributed optimization, the proposed model holds the least number of state variables and relaxes the strongly connected weighted-balanced topology to the weaker case. The modified form of the proposed continuous-time algorithm is also given, and it is proven that this algorithm is suitable for solving distributed problems if the undirected network is connected. Finally, two numerical examples and an optimal placement problem confirm the effectiveness of the proposed continuous-time algorithm. [ABSTRACT FROM AUTHOR]

Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fault-tolerant consensus control for a class of uncertain multiagent systems (MASs) via weighted edge dynamics. Different from the existing consensus results, the considered communication graph is weighted and the system model is assumed to be completely unknown. First, based on an important factorization of Laplacian matrix, a novel system named as weighted edge dynamics is introduced, and it is then proved that the stabilizing controller design for the transformed weighted edge dynamics is equivalent to the consensus controller design for the initial MAS. Within this framework, an efficient data-based algorithm is given to derive the fault-tolerant stabilizing controller, and the consensus of the initial MAS can be further ensured under the designed control protocol. Finally, some simulation examples on the LC oscillator networks are given to demonstrate the validity of theoretical results. [ABSTRACT FROM AUTHOR]

Cash-related costs constitute a large portion of management cost of an automated teller machine (ATM) network. Cash should be delivered to or picked-up from ATM devices in certain intervals in order to both meet customer satisfaction and to be able to generate additional revenue from excess cash through daily interest rates. Unlike classical ATMs, new-generation ATMs, also called recycle ATMs, have a single cassette for cash withdrawal and deposit; this property imposes new restrictions on ATM cash management. Moreover, recycle ATMs are costly, and hence their deployment should be planned carefully. In this paper, our aim is to optimize the ATM networks in terms of cash related costs. We formulate an optimization problem as an integer linear program, which jointly decides on when to visit an ATM, how much money to deliver to which ATM and which road should be followed for the distribution of cash to the ATMs. We also decide on which ATMs in the network should be replaced by a recycle ATM. We then propose a polynomial-time heuristic algorithm and compare it with the optimization formulation in terms of cash cost and the recycle ATM decision. We demonstrate through performance evaluation that our heuristic algorithm is suitable for practical implementation. [ABSTRACT FROM AUTHOR]

In this paper, the optimal output tracking control problem of discrete-time nonlinear systems is considered. First, the augmented system is derived and the tracking control problem is converted to the regulation problem with a discounted performance index, which relies on the solution of the Bellman equation. It is known that policy iteration and value iteration are two classical algorithms for solving the Bellman equation. Through analysis of the two algorithms, it is found that policy iteration converges fast while requires an initial admissible control policy, and value iteration avoids the requirement of an initial admissible control policy but converges slowly. To achieve the tradeoff between policy iteration and value iteration, the multistep heuristic dynamic programming (MsHDP) is proposed by using multistep policy evaluation scheme. The convergence of MsHDP algorithm is proved by demonstrating that it converges to the solution of the Bellman equation. Subsequently, neural network-based actor-critic structure is developed to implement the MsHDP algorithm. The effectiveness and advantages of the developed MsHDP method are validated through comparative simulation studies. [ABSTRACT FROM AUTHOR]

This paper develops an optimal control scheme for continuous-time unknown nonlinear systems using the event-triggering mechanism. Different from designing controllers using the time-triggering mechanism, the event-triggered controller is updated only when the system state deviates more than a certain threshold from a prescribed value. To obtain the event-triggered optimal controller, we develop an identifier-critic architecture under the framework of reinforcement learning. The identifier network, composed of a feedforward neural network (FNN), aims to derive the knowledge of unknown system dynamics, and the critic network, constituted of an FNN, intends to derive the event-triggered optimal controller. The identifier network is tuned via the combination of a standard back-propagation algorithm and an ${e}$ -modification method, and the critic network is updated using a modification of the gradient descent method. By introducing an additional stability term to update the critic network, the initial admissible control is no longer required. Meanwhile, by using historical and instantaneous state data together, the persistence of excitation condition is relaxed. A stability analysis of the closed-loop system is provided based on the Lyapunov method. The effectiveness of the proposed designs is illustrated through simulations of a nonlinear example and a single link robot arm system. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN)-based tracking control algorithm is proposed for the wheeled mobile robotic (WMR) system with full state constraints. It is the first time to design an adaptive NN-based control algorithm for the dynamic WMR system with full state constraints. The constraints come from the limitations of the wheels’ forward speed and steering angular velocity, which depends on the motors’ driving performance. By employing adaptive NNs and a barrier Lyapunov function with error variables, then, the unknown functions in the systems are estimated, and the constraints are not violated. Based on the assumptions and lemmas given in this paper and the references, while the design and the system parameters chose properly, our proposed scheme can guarantee the uniform ultimate boundedness for all signals in the WMR system, and the tracking error converge to a bounded compact set to zero. The numerical experiment of a WMR system is presented to illustrate the good performance of the proposed control algorithm. [ABSTRACT FROM PUBLISHER]

In the previous work, Zhang et al. developed a special type of recurrent neural networks called Zhang neural network (ZNN) with continuous-time and discrete-time forms for time-varying matrix inversion. In this paper, a novel discrete-time ZNN (DTZNN) model for time-varying matrix inversion is proposed and investigated. Specifically, a new numerical difference rule based on Taylor series expansion is established in this paper for first-order derivative approximation. Then, by exploiting this Taylor-type difference rule, the novel DTZNN model, which is a five-step iteration algorithm, is thus proposed for time-varying matrix inversion. Theoretical results are also presented for the proposed DTZNN model to show its excellent computational property. Comparative numerical results with three illustrative examples further substantiate the efficacy and superiority of the proposed DTZNN model for time-varying matrix inversion compared with previous DTZNN models. [ABSTRACT FROM PUBLISHER]

This paper presents a general discrete-event system (DES)-based hard periodic real-time task model. Based on supervisory control theory (SCT), an optimal priority-free real-time scheduling technique is proposed to process all the tasks running in uniprocessor or multiprocessor real-time systems (RTS). The preemption relation in this paper generalizes priority-based preemption. First, regular languages are utilized to describe the processor behavior related to each task’s execution. Thereafter, the languages are represented by DES generators. Finally, the global processor behavior is generated as the synchronous product of these DES generators. By discarding the priorities, a novel preemption policy, namely conditional-preemption, is developed. Two sets of conditional-preemption specifications are developed, on the processor level and task level, respectively. Moreover, in order to control the system to be nonblocking and also limit the worst-case response time of the tasks, two corresponding sets of specifications are presented. After generating the global specification as the synchronous product, by implementing SCT the calculated supervisor can provide all the safe real-time execution sequences. The supervisor calculation can be sped up by a three-step algorithm. Finally, the real-time scheduling is implemented for real-world examples. [ABSTRACT FROM AUTHOR]

Population dynamics have been widely used in the design of learning and control systems for networked engineering applications, where the information dependency among elements of the network has become a relevant issue. Classic population dynamics (e.g., replicator, logit choice, Smith, and projection) require full information to evolve to the solution (Nash equilibrium). The main reason is that classic population dynamics are deduced by assuming well-mixed populations, which limits the applications where this theory can be implemented. In this paper, we extend the concept of population dynamics for nonwell-mixed populations in order to deal with distributed information structures that are characterized by noncomplete graphs. Although the distributed population dynamics proposed in this paper use partial information, they preserve similar characteristics and properties of their classic counterpart. Specifically, we prove mass conservation and convergence to Nash equilibrium. To illustrate the performance of the proposed dynamics, we show some applications in the solution of optimization problems, classic games, and the design of distributed controllers. [ABSTRACT FROM PUBLISHER]