Search

To solve the control problem of time-varying state-scale nonlinear systems whose initial state is not affected by settling time, fixed-time convergence algorithms are proposed for first-order systems and higher-order systems in this paper. First, a scalar model is used to illustrate how the time-varying feedback parameter can guarantee that the system achieves asymptotic stability while achieving finite-time convergence, and it is proved that the settling time obtained in this paper is only related to the prescribed boundary. This allows us to design the settling time with an appropriate parameter based on the prescribed boundary. To exhibit the effectiveness and extensibility of the proposed algorithm for first-order scalar systems, the results are subsequently extended to general higher-order systems based on the backstepping method. By introducing numerical simulation results, this paper verifies that the proposed algorithm will make the system achieve asymptotic stability and its output can converge to a given boundary, regardless of the system's initial states. [ABSTRACT FROM AUTHOR]

In this paper we propose a new parameter estimator that ensures global exponential convergence of linear regression models requiring only the necessary assumption of identifiability of the regression equation, which we show is equivalent to interval excitation of the regressor vector. An extension to – separable and monotonic – nonlinear parameterisations is also given. The estimators are shown to be robust to additive measurement noise and – not necessarily slow-parameter variations. Moreover, a version of the estimator that is robust with respect to sinusoidal disturbances with unknown internal model is given. Simulation results that illustrate the performance of the estimator compared with other algorithms are given. [ABSTRACT FROM AUTHOR]

In this paper, an $ H_{\infty } $ H ∞ negative imaginary static output feedback (SOF) controller is designed for a low frequency (LF) networked control system (NCS) with time-delays. A difference operator is introduced to deal with the time-delays in a feedback configuration via integral quadratic constraints (IQCs). Both $ H_{\infty } $ H ∞ performance and negative imaginariness are analysed via LF IQCs and a generalised Kalman–Yakubovich–Popov (GKYP) lemma. A two-stage algorithm is presented to obtain the $ H_{\infty } $ H ∞ negative imaginary SOF controller for the LF NCS with time-delays. Simulation results are given to show the effectiveness and superiority of the $ H_{\infty } $ H ∞ negative imaginary SOF controller. [ABSTRACT FROM AUTHOR]

In this paper we present and provide a proof for a set of non-recursive formulae arising in the computation of the largest output-nulling and the smallest input-containing subspaces which have been used in a variety of contexts in the framework of the geometric approach. These expressions have been used in the literature both in the strictly proper and in the non-strictly proper case, but, to the best of our knowledge, a proof is still missing. These formulae are established here in the general possibly non-strictly proper case. Some ancillary side results of independent interest are also proposed. [ABSTRACT FROM AUTHOR]

This paper studies the problem of event-triggered impulsive control for discrete-time systems. A novel periodic event-triggering scheme with two tunable parameters is presented to determine the moments of updating impulsive control signals which are called event times. Sufficient conditions are established to guarantee asymptotic stability of the resulting impulsive systems. It is worth mentioning that the event times are different from the impulse times, that is, the control signals are updated at each event time but the actuator performs the impulsive control tasks at a later time due to time delays. The effectiveness of our theoretical result with the proposed scheme is illustrated by three examples. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralised stochastic control framework for a class of large-scale and complex dynamic networks. The proposed framework describes a decentralised probabilistic control and message passing architecture of mutually interacting quasi-independent subsystems. Within this framework, the outputs of the subsystems are communicated back to topologically-connected neighbours through output probabilistic message passing. This communication approach constitutes the main contribution of the current paper and allow the achievement of the global system goal. The proposed framework reduces the amount of communication required given the low dimensionality of the output space compared to the state space. The updated knowledge through the output message passing is then applied to induce a fully probabilistic local control strategy affecting only the local subsystem. Finally, a numerical example is presented to illustrate the effectiveness and usefulness of our novel proposed framework. [ABSTRACT FROM AUTHOR]

This paper designs event-triggered delay compensation and disturbance attenuation control algorithms for multi-agent systems. First, through predictor-based model reduction method, an unknown large input delay can be compensated and delay-free dynamics is obtained. Based on the transformed systems, predictive descriptor observers are proposed to obtain estimated system states and sensor faults. Then, an event-triggered fault-tolerant algorithm is designed to save energy resources for communication among the agents while attenuating the disturbances. Moreover, it is validated that the systems do not exhibit Zeno behaviour using the designed event-triggering sequence. Numerical simulations are conducted to demonstrate the effectiveness of designed algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed resource allocation problem of a second-order multi-agent system with external disturbances. The global network resource equality constraint is considered. A distributed algorithm with an exponential convergence rate is proposed based on an internal model for a known bounded disturbance. By using the active disturbance approach to design a reduced-order extended state observer, the proposed algorithm is modified to reject an unknown external disturbance. Moreover, exponential convergence is achieved through the Lyapunov theory when the disturbance is constant. When the differential of the disturbance is bounded, by adjusting the control parameter of the observer, the proposed algorithm converges to an arbitrarily small neighbourhood of the optimal solution. Finally, our simulation results demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a class of discrete-time inhomogeneous bilinear systems whose controllability cannot be checked by the classical results is considered. By using an algebraic technique, it is shown that the systems are never controllable. Near-controllability of the systems is thus studied and, by defining a novel transition operator, an algebraic necessary and sufficient criterion is established, where the control inputs to achieve state transition can also be computed. Furthermore, the established near-controllability result is applied to study nearly controllable subspaces of the discrete-time homogeneous bilinear systems. Although such systems were investigated in the previous work, a new and larger class of nearly controllable subspaces of the homogeneous systems is derived, which cannot be found by the previous work due to different algebraic structures. Finally, an algorithm for solving the control inputs to achieve state transition as well as examples are given to demonstrate the results of this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid robust control method for control of an autonomous underwater vehicle. Fractional sliding mode control (FSMC) is robust against external disturbances. The main drawback of the FSMC method is creating a chattering phenomenon. Therefore, a compound control method is applied, which benefits in both robustness of the FSMC method and chattering elimination by the new control algorithm. A random noise is applied in order to verify the robustness of the proposed control method. The stability of FSMC and proposed compound control method has been verified by Lyapunov theory. The effectiveness of the proposed control method is compared with FSMC, which numerical simulation results confirm the best performance of the proposed control method. [ABSTRACT FROM AUTHOR]

Direct model predictive control applications for nonlinear systems can result in non-convex minimisation problems, which is additionally complicated if systems are non-minimum phase. In this paper, the Particle Swarm Optimisation (PSO) is applied for these global minimisation problems and tested on two selected nonlinear systems. Obtained results show that, in comparison to the sequential quadratic programming method, PSO is less vulnerable to local minima, and, with appropriate parameters, provides superior quality in nonlinear predictive control. The main results concerning PSO application show that the higher number of particles, comparing to the number of iterations, provides improved accuracy with limited computational burden; this way up to 5 % reduction of a proposed quality index is obtained preserving unchanged time of calculations. The performance is also boosted when after several iterations of the algorithm greater emphasis is put on the local search, which is achieved by appropriate learning factors and inertia weight. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic surface control (DSC) strategy for discrete-time strict-feedback multiple-input-multiple-output systems is proposed. State variables are estimated by Luenberger observer using latest outputs received in controller. The control signals, bearing the same time-stamps as the target system, are constructed based on DSC methodologies. The control mechanism also considers the network-induced time-varying delays, packet drops and disorder; these parameters are systematically stacked into the network and transmitted into an actuator where the control function is performed. In order to establish all the design parameters, a simplified methodology of utilising linear matrix inequality and homotopy algorithm is proposed. The simulation results prove that the proposed method is effective. [ABSTRACT FROM AUTHOR]

In this paper, we analyse the convergence properties of the Dynamic Programming Value Iteration algorithm by exploiting the stability theory of discrete-time switched affine systems. More specifically, by formulating the Value Iteration algorithm as a switched affine system, a Lyapunov-based optimal policy selection strategy is designed to guarantee the practical stabilisation of the resulting system towards an invariant set of attraction containing a given target value function. The switching control algorithm, referred to as Lyapunov-based Value Iteration algorithm, can be regarded as a convergence analysis tool and can be adopted to verify if and how such target value function can be approached by choosing from a subset of suitable stationary policies, at each time slot. The usage of the proposed algorithm in practice is also discussed. Finally, two different applications are provided to further illustrate and examine the key-aspects of the approach presented. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm to reduce the number of gains of a polytopic LPV controller considering generic tuples of vertices, for which a common controller gain can be used. The use of Frobenius norm and the inclusion of the input matrix in the LMIs perturbation matrix allows decreasing the conservativeness to select vertices which are combinable, with respect to a previous approach based on Gershgorin circles. A combinability metric that can be applied to an arbitrary partition of the set of vertices is defined. Then, a recursive algorithm finds a lesser-fragmented combinable partition at each iteration by combining together two elements of a partition. The algorithm aims at finding combinable partitions with minimal cardinality in fewer attempts, always preserving the original control performance specifications. The proposed method is validated using numerical examples, a twin rotor MIMO system and a two-link robotic manipulator. [ABSTRACT FROM AUTHOR]

In this paper, a class of multi-agent flocking control algorithms are contrived, which can specify communication networking, inter-agent distance and position-pattern subgrouping for second-order particle multi-agent systems, by embedding Olfati-Saber's algorithm with what we call the topological specification matrices/vectors. In other words, by equipping Olfati-Saber's algorithms with more weighting factors, the steady-state formation behaviours in a concerned multi-agent system can be manoeuvered while collision-free flocking is induced. More specifically, we create novel collective potential functions with the topological specification matrices/vectors to evaluate multi-agent dynamics and achieve the expected steady-state features in terms of graph connectivity, distance keeping, position-pattern-related subgrouping and obstacle avoidance. Moreover, formation convergence can be improved by integral specification of position difference. Numerical simulations well demonstrate effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed smooth optimisation problem of multi-agent systems over undirected connected communication topologies. The objective is to minimise the global objective function, which is the sum of local private objective functions, by exchanging and computing local information among each agent or with its neighbourhoods. To avoid continuous communication among agents and reducing communication overheads, we propose an event-triggered distributed optimisation algorithm based on a proportional-integral control strategy. We first show that the developed algorithm does not exhibit Zeno behaviour. Then, it is shown that the proposed algorithm exponentially converges to an exact global minimiser under the restricted secant inequality condition. This condition admits a more general class of the global objective function since it does not require the convexity of the global objective function and the global minimisers are not required to be unique. Theoretical results are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper aims to provide a methodology for generating autonomous and non-autonomous systems with a fixed-time stable equilibrium point where an Upper Bound of the Settling Time (UBST) is set a priori as a parameter of the system. Furthermore, some conditions for such an upper bound to be the least one are provided. This construction procedure is a relevant contribution compared with traditional methodologies for generating fixed-time algorithms satisfying time constraints since current estimates of an UBST may be too conservative. The proposed methodology is based on time-scale transformations and Lyapunov analysis. It allows the presentation of a broad class of fixed-time stable systems with predefined UBST, placing them under a common framework with existing methods using time-varying gains. To illustrate the effectiveness of our approach, we generate novel, autonomous and non-autonomous, fixed-time stable algorithms with predefined least UBST. [ABSTRACT FROM AUTHOR]

The expectation–maximisation (EM) algorithm is one of the most popular methods used to solve the problem of parametric distribution-based clustering in unsupervised learning. In this paper, we propose to analyse a generalised EM (GEM) algorithm in the context of Gaussian mixture models, where the maximisation step in the EM is replaced by an increasing step. We show that this GEM algorithm can be understood as a linear time-invariant (LTI) system with a feedback nonlinearity. Therefore, we explore some of its convergence properties by leveraging tools from robust control theory. Lastly, we explain how the proposed GEM can be designed and present a pedagogical example to understand the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a novel modification to the predictive functional control (PFC) algorithm to facilitate significant improvements in the tuning efficacy. The core concept is the use of an alternative parameterisation of the degrees of freedom in the PFC law. Building on recent insights into the potential of Laguerre functions in traditional MPC (Rossiter, Wang, & Valencia-Palomo, 2010; Wang, 2009), this paper develops an appropriate framework for PFC and then demonstrates that these functions can be exploited to allow easier and more effective tuning in PFC as well as facilitating strong constraint handling properties. The proposed design approach and the associated tuning methodology are developed and their efficacy is demonstrated with a number of numerical examples. [ABSTRACT FROM AUTHOR]

Finding feasible solutions to motion planning and control problem of robotic systems in different environments with various applications is an active area of research. This article presents a new solution to the motion planning and control problem of a three-dimensional articulated mobile manipulator comprising a car-like mobile platform and a three-dimensional n-link articulated arm using the Lyapunov-based control scheme. The motion of the system is described as twofold: first, the car-like mobile platform moves from an initial position to its pseudo-target, and second, when the mobile platform is within some predefined distance from the pseudo-target, the end-effector of the robot arm is attracted to its designated target. Therefore, presenting a new 2-Step Algorithm in this paper for dual movement of the articulated mobile manipulator in 3D. In addition, a workspace cluttered with fixed spherical and rod obstacles of random sizes and positions is considered in this research. For the mobile manipulator to avoid an obstacle, the Minimum Distance Technique is adapted where a point on the robot that is closest to an obstacle will avoid the obstacle. The convergence of the two bodies and the stability of the mechanical system are guaranteed by the Lyapunov's direct method. The continuous nonlinear control laws proposed from the control scheme also take into account all mechanical singularities and velocity limitations associated with the system. Theoretical proofs and computer simulations validate the new continuous, acceleration-based, nonlinear, time-invariant control laws. [ABSTRACT FROM AUTHOR]

In this paper, the delay-dependent stability of linear, high-order delay differential systems is investigated. First, two bounds of the unstable eigenvalues of the systems are derived. The two bounds are based on the spectral radius and the norms of the parameter matrices of the systems, respectively. We emphasise that the bounds of the unstable eigenvalues involve only the spectral radius and norms of the matrices of lower size. They can be obtained with much less computational effort and work well in practice for large problems. Then, using the argument principle, a computable stability criterion is presented which is a necessary and sufficient condition for the delay-dependent stability of the systems. Furthermore, a numerical algorithm is provided for checking the delay-dependent stability of the systems. Numerical examples are given to illustrate the main results. [ABSTRACT FROM AUTHOR]

A novel Lyapunov-based methodology for both stability analysis and stabilisation of second-order sliding mode algorithms in the presence of exogenous disturbances is presented. Nowadays, second-order sliding modes such as the well-known super-twisting algorithm are widely recognised for their usefulness in the design of controllers, observers, and exact differentiators. However, a theoretical proof for an optimal and systematical selection of parameters for the algorithm to be stable is of particular interest. The solution proposed in this paper is based on combining exact convex representations of the nonlinear algorithms with a strict Lyapunov function, which leads to stability or design conditions in terms of linear matrix inequalities that can be efficiently solved via convex optimisation techniques, allowing us to provide an enlarged and optimal parameter setting in contrast with existing results; moreover, the systematic nature of the proposal is able to deal with different scenarios avoiding ad-hoc solutions of previous works. Effectiveness of the results is verified via simulation examples. [ABSTRACT FROM AUTHOR]

This paper proposes a projection algorithm which can be employed to bound actuator signals, in terms of both magnitude and rate, for uncertain systems with redundant actuators. The investigated closed-loop control system is assumed to contain an adaptive control allocator to distribute the total control input among actuators. Although conventional control allocation methods can handle actuator rate and magnitude constraints, they cannot consider actuator uncertainty. On the other hand, adaptive allocators manage uncertainty and actuator magnitude limits. The proposed projection algorithm enables adaptive control allocators to handle both magnitude and rate saturation constraints. A mathematically rigorous analysis is provided to show that with the help of the proposed projection algorithm, the performance of the adaptive control allocator can be guaranteed, in terms of error bounds. Simulation results are presented, where the Aero-Data Model In Research Environment (ADMIRE) is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a class of distributed event-triggered algorithms that solve the average consensus problem in multi-agent systems. By designing events such that a specifically chosen Lyapunov function is monotonically decreasing, event-triggered algorithms succeed in reducing communications among agents while still ensuring that the entire system converges to the desired state. However, depending on the chosen Lyapunov function the transient behaviours can be very different. Moreover, performance requirements also vary from application to application. Consequently, we are instead interested in considering a class of Lyapunov functions such that each Lyapunov function produces a different event-triggered coordination algorithm to solve the multi-agent average consensus problem. The proposed class of algorithms all guarantee exponential convergence of the resulting system and exclusion of Zeno behaviours. This allows us to easily implement different algorithms that all guarantee correctness to meet varying performance needs. We show that our findings can be applied to the practical clock synchronisation problem in wireless sensor networks (WSNs) and further corroborate their effectiveness with simulation results. [ABSTRACT FROM AUTHOR]

The objective of this paper is to develop an algorithm for the estimation of time-varying wind parameters by taking into account a detailed quadrotor model. The design objectives include the time convergence optimisation, robustness to measurement noises, and a guaranteed convergence of the estimates to the true values under mild applicability conditions. It is supposed that the estimation algorithm can use inertial measurement unit (IMU) (accelerometers, gyroscopes) sensors augmented with an earth reference tracking system and rotor rotational velocity sensors. To this end, three time-varying parameter estimation algorithms are introduced, compared and finally merged to estimate the varying wind velocity in onboard quadrotor systems. Final numerical experiments, using a nonlinear quadrotor simulator, are used to validate the proposed approaches. [ABSTRACT FROM AUTHOR]

In this paper, the problem of relative predictability of failure event occurrences is investigated. The notion of relative predictability is proposed for logical automata and the concept of predictable rate is introduced to characterise the predictability property of a discrete-event system, which takes values in the interval [0, 1]. Intuitively, a discrete-event system being relatively predictable means that there exist some failure events which can be predicted from the observations of the system. The relationship between relative predictability and predictability introduced by Sahika Genc et al. analysed and the analysis shows that relative predictability is weaker than predictability for discrete-event systems. Furthermore, a necessary and sufficient condition for relative predictability is presented. In particular, an opacity-based algorithm is developed to test the relative predictability, which is polynomial in the number of states of the system. Also, some examples are provided to illustrate the presented results. [ABSTRACT FROM AUTHOR]

This paper aims to design a high-performance controller with any predefined structure for continuous-time LTI systems. The control law employed is the generalized sampled-data hold function (GSHF), which can have any special form, e.g. polynomial, exponential, piecewise constant, etc. The GSHF is first written as a linear combination of a set of basis functions obtained in accordance with its desired form and structure. The objective is to find the coefficients of this linear combination, such that a prespecified linear-quadratic performance index is minimized. A necessary and sufficient condition for the existence of such GSHF is first obtained in the form of matrix inequality, which can be solved by using the existing methods to obtain a set of stabilizing initial values for the coefficients or to conclude the non-existence of such structurally constrained GSHF. An efficient algorithm is then presented to compute the optimal coefficients from their initial values, so that the performance index is minimized. The paper utilizes the latest developments in the area of sum-of-square polynomials. The effectiveness of the proposed method is demonstrated in two numerical examples. [ABSTRACT FROM AUTHOR]

Computing the McMillan degree of a transfer function matrix G requires writing G in the Smith-McMillan form or computing all of the minors of G. This becomes more difficult and computationally expensive as the dimension of G increases. This paper introduces a simple approach to compute the McMillan degree of G without the need to write G in the Smith-McMillan form and without the need to compute all of the minors of G. The presented approach requires less computations than the approaches in the literature and clarifies the relationship between the McMillan degree, the poles, and the transmission zeros of a MIMO system. We assume that the roots of the least common multiple of the denominators of the entries of G satisfy a multiplicity condition, which can be verified prior to the application of the algorithm. The proposed approach can be beneficial for systems with many inputs and many outputs. [ABSTRACT FROM AUTHOR]

This paper is devoted to the practical tracking control and disturbance rejection for a class of discrete-time stochastic linear systems (DTSLSs). Firstly, a criterion on practical stability in probability (PS-P) of discrete-time stochastic systems is given. Secondly, based on internal model principle (IMP) and pole assignment algorithm, a dynamic controller is designed such that practical tracking in mean square (PTMS) sense and disturbance rejection are realised, while maintaining the PS-P of DTSLSs. In the end, the feasibility of the proposed method is verified by the application to the mass-spring-damper system, and the efficiency is illustrated by the simulation results. [ABSTRACT FROM AUTHOR]

In this paper parameter optimization through a quadratic performance index is introduced as a method to establish a new iterative learning control law. With this new algorithm, monotonic convergence of the error to zero is guaranteed if the original system is a discrete-time LTI system and it satisfies a positivity condition. If the original system is not positive, two methods are derived to make the system positive. The effect of the choice of weighting parameters in the performance index on convergence rate is analysed. As a result adaptive weights are introduced as a method to improve the convergence properties of the algorithm. A high-order version of the algorithm is also derived and its convergence analysed. The theoretical findings in this paper are highlighted with simulations. [ABSTRACT FROM AUTHOR]

This paper investigates the input–output decoupling problem of mix-valued logical control networks by using the semi-tensor product method. First, an algorithm is proposed to construct an output-friendly subspace of mix-valued logical control networks and necessary and sufficient conditions for the existence of the consistent output-friendly subspaces are derived. Then, necessary and sufficient conditions for the solvability of input–output decoupling problem of mix-valued logical control networks are obtained under open-loop controller. Finally, an illustrative example is given to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

The objective of this paper is to build feedback control solutions for LTI systems with unknown constant input-delay. The control scheme includes a time-delay estimation algorithm and a predictor-based controller. The Lyapunov–Krasovskii theorem provides the global exponential stability of the closed-loop system. Some examples illustrate the theoretical results and the performances of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers a multi-agent system with multiple leader agents. All the agents have bounded inputs. Any two agents can communicate only if they are within a fixed distance of each other. Leaders are special agents which generate trajectories autonomously based upon certain objectives. The follower agents must partition into groups such that agents in each group track exactly one leader. Given a particular partition and the resulting information exchange topology, every follower in each group must converge onto their leader's trajectory in min–max time. A special directed spanning tree is selected in each group. Each follower agent in this tree pursues its tail agent using a time-optimal feedback policy. These local feedback laws achieve global min–max time consensus tracking for any general information graph. Further, a decentralised algorithm is proposed to partition the agents so that consensus tracking for all the groups is accomplished in the shortest possible time. [ABSTRACT FROM AUTHOR]

A partially decentralised scheme for the semi-automatic control of network systems with stabilising agents is presented in this paper. The semi-automatic control of interconnected systems employing the stabilising agent whose installation is segregated from the associated control algorithm has been presented previously. In this development, the quadratic dissipativity constraint (QDC) associated with a non-negative supply rate is newly introduced for the stabilising agent. The closed-loop system having bounded disturbances is input-to-state stabilised with a non-monotonic Lyapunov function when the QDC is used with model predictive controllers. The effectiveness of the QDC for stabilising agents in the presented partially decentralised architecture is demonstrated via simulation studies of a frequency regulation problem in power systems. [ABSTRACT FROM AUTHOR]

Despite advantages of variable geometry inlet (VGI) in improving scramjet efficiency, a concern of its negative effects on aerodynamics persists. This paper presents a novel longitudinal control for the hypersonic vehicle model with further considerations on both strong VGI effects and unpredictable parametric variations, which is practical but hard to handle. Our design uses an auxiliary signal and a bound adaptation mechanism to synthetically accommodate VGI effects (including VGI servomechanism characteristics and VGI couplings with aerodynamics), time-varying parameters and structural flexibility while achieving good tracking performances via a relatively low-dimension adaptive algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an event-triggered control for multi-agent systems in which all agents have an identical linear dynamic mode. The asynchronous event-triggered control algorithms are proposed based on the triggering time sequences of all agents. The main contribution of this paper is to extend the event-triggered control method to investigate general linear multi-agent systems. First, by applying variable substitution method, we give the asynchronous triggering conditions. Based on the conditions, the consensus can be achieved both under fixed and switching topologies. Meanwhile, all the proposed event-triggered algorithms can exclude Zeno behaviours of the closed-loop systems. Then, the asynchronous results are applied to cope with formation control problem. Finally, numerical simulations are presented to illustrate the effectiveness of the part event-triggered protocol designs. [ABSTRACT FROM AUTHOR]

This paper exploits the analogy between the electrical grid and modern communication networks to implement Electric Vehicle (EV) battery charging scheduling algorithms inspired by popular communication network techniques. In preliminary works, a similar approach was used to manage the Grid-to-Vehicle (G2V) active power flows. In this paper, we extend this framework to both implement the Vehicle-to-Grid (V2G) concept and to provide reactive power compensation capabilities that do not affect charging times. The ability of the proposed algorithms to optimally share the available/desired power in a fair way, with minimum communication requirements, in a very uncertain, dynamically changing framework, is illustrated through several examples for different scenarios of interest. [ABSTRACT FROM AUTHOR]

In this paper, we investigate new implementable cooperative adaptive backstepping controllers for a group of underactuated autonomous vehicles that are communicating with their local neighbours to track a time-varying virtual leader of which the relative position may only be available to a portion of the team members. At the kinematic cooperative control level of the autonomous underwater vehicle, the virtual cooperative controller is basically designed on a proportional and derivative consensus algorithm presented in Ren (2010), which involves velocity information from local neighbours. In this paper, we propose a new design algorithm based on singular perturbation theory that precludes the use of the neighbours’ velocity information in the cooperative design. At the dynamic cooperative control level, calculation of the partial derivatives of some stabilising functions which in turn will contain velocity information from the local neighbours is required. To facilitate the implementation of the cooperative controllers, we propose a command filter approach technique to avoid analytic differentiation of the virtual cooperative control laws. We show how Lyapunov-based techniques and graph theory can be combined together to yield a robust cooperative controller where the uncertain dynamics of the cooperating vehicles and the constraints on the communication topology which contains a directed spanning tree are explicitly taken into account. Simulation results with a dynamic model of underactuated autonomous underwater vehicles moving on the horizontal plane are presented and discussed. [ABSTRACT FROM PUBLISHER]

New time and frequency domain designs of unknown inputs functional observers (UIFO) for a class of descriptor systems with a constant time delay are investigated in this paper. The unknown inputs are present in both the state and the measurement equations. The time procedure design is based on the unbiasedness of the estimation error and the Lyapunov Krasovskii stability theory. After given the existence condition of such observers, the gain implemented in the functional observer with internal delay design is obtained in terms of linear matrix inequalities. The particular cases, where the observer is independent of internal delay and where it is independent of delay are also investigated. A design algorithm that summarises the different steps of the UIFO design is proposed. Then, the frequency procedure design is derived from time domain results by applying the factorisation approach, where we define some useful matrix fraction descriptions. Note that the order of this unknown input observers is equal to the dimension of the vector to be estimated. A numerical example is given to illustrate the effectiveness of the presented approach. [ABSTRACT FROM AUTHOR]

In this paper, we address the resilient group consensus of multi-agent networks in the presence of structured and unstructured Byzantine faults. In the case of structured Byzantine faults that feed different but structured values to the network, it is shown that non-faulty nodes can achieve group consensus without using any fault tolerant algorithm. Necessary and sufficient conditions on the network are derived so that the conventional consensus algorithm leads to group consensus values in the range determined by the initial values of the non-faulty nodes. In the presence of unstructured Byzantine faults, two fault tolerant algorithms are proposed to overcome the highly disruptive behaviour. Subsequently, convergence analysis of these algorithms is carried out by exploiting the robustness properties of the network. Finally, theoretical results are illustrated with several simulation examples. [ABSTRACT FROM AUTHOR]

This paper studies the optimal control of n-person random evolutionary Boolean games (REBGs). Firstly, the considered n-person REBG is converted to an equivalent stochastic control system by using the semi-tensor product of matrices. Secondly, based on the equivalent stochastic control system, an algorithm is proposed to solve the finite horizon optimal control problem of n-person REBGs, which is applied to the Mayer-type optimal control problem. Thirdly, using the receding horizon control method, the infinite horizon optimal control problem is considered, and an algorithm is established. Finally, an illustrate example is given to support the obtained results. [ABSTRACT FROM AUTHOR]

This paper presents a distributed set-membership estimation and formation control algorithm for a fleet of vehicles. Each vehicle is equipped with an agent with sensing, acting, computational and communication capacities. The agent's objective is to compute estimation set for the state of all the fleet of vehicles and, additionally, generate control actions to keep its local vehicle in formation. In order to improve the estimation, the agents exchange information with other neighbouring agents. The estimation sets are mathematically characterised by zonotopes. The novelty of the proposed solution lies in the fact that the estimation is guaranteed in spite of the lack of knowledge of the control signal applied by the rest of vehicles. Then, in the presence of bounded disturbances and noises, the position reference for the vehicles can be tracked with a bounded estimation. The proposed solution is tested by simulations. [ABSTRACT FROM AUTHOR]

The initial value problems (IVPs) of Caputo fractional-order differential equations are very important in control systems modelling and simulation. A series of numerical algorithms are proposed in the paper in solving systematically various kinds of Caputo equations. For linear Caputo equations, the divergent problems of the existing Taylor auxiliary function are pointed out, and two effective algorithms are presented to transform the nonzero IVPs into zero ones, where the closed-form solutions are available. Furthermore, algorithms for nonlinear Caputo equation are also presented, aiming at finding numerical solutions to all kinds of Caputo equations. Error analysis for the proposed algorithms is provided, and numerical examples are presented to illustrate the accuracy and effectiveness of the algorithms. [ABSTRACT FROM AUTHOR]

Feedforward control enables high performance of a motion system. Recently, algorithms have been proposed that eliminate bias errors in tuning the parameters of a feedforward controller. The aim of this paper is to develop a new algorithm that combines unbiased parameter estimates with optimal accuracy in terms of variance. A simulation study is presented to illustrate the poor accuracy properties of pre-existing algorithms compared to the proposed approach. Experimental results obtained on an industrial nanopositioning system confirm the practical relevance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, two control allocation algorithms have been proposed for overactuated systems. The algorithms are developed by formulating constrained control allocation problem into an equivalent fixed point framework. The first algorithm follows sequential solving method while the other one involves a zero finding technique by the Newton method. In order to consider limiting constraints, the saturation function has been taken into account that leads to a nonsmooth zero finding problem and the proposed method guarantees superlinear convergence. The second algorithm is easy to implement and faster than the algorithm developed based on the sequential solving technique. To demonstrate the effectiveness of the proposed algorithms, a detail simulation study has been carried out, in which one example considers both the actuator rate and amplitude constraints. [ABSTRACT FROM AUTHOR]

For an uncontrollable system, adding leaders and adjusting edge weights are two methods to improve controllability. In this paper, the controllability of multi-agent systems under directed topologies is studied, especially on the leader selection problem and the weight adjustment problem. For a multi-agent system, necessary and sufficient algebraic conditions for controllability with fewest leaders are proposed. To improve controllability by adjusting edge weights, the system is supposed to be structurally controllable, which holds if and only if the communication topology contains a spanning tree. It is also proved that the number of the fewest edges needed to be assigned on new weights equals the rank deficiency of controllability matrix. In addition, a leader selection algorithm and a weight adjustment algorithm are presented. Simulation examples are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Recently there has been an increasing interest in active fault detection when traditional approaches are not effective or cannot be used. Previous papers have shown how to construct continuously varying test (auxiliary) signals for use in fault detection and identification. Such signals may not always be easy to implement. In this paper a new algorithm is presented to compute optimal piecewise constant test signals. In the important case where the test signal changes values a small number of times, the new test signals can be both smaller and more effective than test signals computed by other means. Two computational examples are given to illustrate both computational issues and differences between different types of piecewise constant signals. [ABSTRACT FROM AUTHOR]

This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure-theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment. [ABSTRACT FROM AUTHOR]