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This article addresses the problem of lateral control problem for networked-based autonomous vehicle systems. A novel solution is presented for nonlinear autonomous vehicles to smoothly follow the planned path under external disturbances and network-induced issues, such as cyber-attacks, time delays, and limited bandwidths. First, a fuzzy-model-based system is established to represent the nonlinear networked vehicle systems subject to hybrid cyber-attacks. To reduce the network burden and effects of cyber-attacks, an asynchronous resilient event-triggered scheme (ETS) is proposed. A dynamic output-feedback control method is developed to address the underlying problem. Conditions are derived to obtain the output-feedback controller and resilient asynchronous ETS such that the closed-loop switched fuzzy system is globally exponentially stable. Examples are provided to demonstrate the effectiveness and merits of the proposed new control design techniques.

In this article, a distributed adaptive event-triggered formation control strategy is proposed for unified nonlinear heterogeneous multiagent systems under uncertainties and disturbances to achieve time-varying formations. To reduce the frequency of data transmission, a distributed dual adaptive observer with an event-triggered strategy is developed to estimate the states of a reference exosystem. Without incurring prior global information about a communication graph, a novel robust formation controller, with dynamic distributed compensators for uncertainties and disturbances, is designed based on an observer result and a nonlinear internal control principle. Finally, both simulation and experiment are conducted for tracking and patrolling formation to verify the effectiveness of the proposed formation control strategy and its robustness.

The formation control problem for a group of first-order agents with model uncertainty and actuator saturation is investigated in this manuscript. An algorithm-and-observer-based formation controller is developed to ensure the semi-global boundedness of the formation tracking error with actuator saturation. First, a fully local-error-related cooperative weight tuning procedure is proposed for the adaptive uncertainty estimation of each agent. The effect of actuator saturation on both the cooperative adaptive estimation and the controller design part is then analysed and discussed. A three-layer neural-based observer is further constructed to achieve finite-time uncertainty approximation with actuator saturation. Besides, the reverse effect led by coupled and saturated control inputs is defined and a new control input distribution algorithm is presented to attenuate the potential oscillation in system states. Finally, comparative simulations based on a multiple omnidirectional robot system are conducted to illustrate the performance of the proposed formation controllers and the new algorithm.

In this paper, we investigate the problems of robust exponential stabilization in mean square and ${\mathcal{H}_{\infty}}$ -based integral sliding mode controller design for uncertain stochastic Takagi-Sugeno (T-S) fuzzy switched time delay systems with both matched and unmatched uncertainties under synchronous switching and asynchronous switching, respectively. In the systems, the control input is dependent on switching signal. Under synchronous switching and asynchronous switching, respectively, sufficient conditions are developed to guarantee that the resulting closed-loop systems are robustly exponentially stable in mean square with a prescribed ${\mathcal{H}_{\infty}}$ -performance. The integral sliding mode surfaces and the sliding mode controllers are designed for the underlying systems under synchronous switching and asynchronous switching, respectively. By two examples stemming from the mass-spring-damper model and the single-link robot arm model under stochastic perturbation, the effectiveness of the proposed new design techniques are verified.

In this paper, a new robust optimization method is proposed to simultaneously optimize the expectation and variability of system performance with parametric uncertainties and fuzzy variables. The expectation entropy model is presented to characterize the fuzzy robust optimization problem as an equivalent bi-objective optimization problem. An approximate mapping method is developed to calculate the response of fuzzy variables, which improves the computational efficiency of objective functions. Then, according to the decision makers' preference for objectives, the optimization framework based on Stackelberg game is established. A leader-follower state transition algorithm is designed to search for the equilibrium solutions. Two practical case studies are provided to show the effectiveness of the new optimization approach in both subjective judgment and objective assessment.

This article studies the issue of stability for complex network control systems, which comprise multisystems and multicontrollers. These systems and controllers are coupled with each other and linked by communication networks with random delays. The system and controller network topologies are subject to jump, and the dynamic is captured by two models, as: 1) dual-Markov model and 2) hidden Markov model. Correspondingly, the stability conditions based on the Lyapunov-functional method are given for closed-loop stability. Then, the stability conditions are derived by transforming into the linear matrix inequality-based constraint minimization problem for solving controller gains. An illustration is carried out to testify the validation and potentials of the proposed new design techniques.

In this article, the problem of robust optimization is considered for dynamical systems with both constraints and uncertainties. Conditions are established to ensure the existence of solutions to the problem with both robust optimality and feasibility. The objective performance with respect to fuzzy uncertainties is evaluated based on the expectation-entropy model. A feasibility robustness analysis method is proposed to handle the uncertainties in the constraints. Using the hierarchy structure in robust design, the optimization framework based on Stackelberg–Nash game is developed. A leader–followers state transition algorithm is designed to search for the equilibrium solution. Two application examples are given to demonstrate that the proposed robust optimization method can accurately evaluate the robustness performance and successfully search for a compromise solution.

This article proposes, a novel obstacle avoidance algorithm for a mobile robot based on finite memory filtering (FMF) in unknown dynamic environments. To overcome the limitations of the existing dynamic window approach (DWA), we propose a new version of the DWA, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles. FDEDWA estimates the distribution of obstacles through the FMF, and predicts the future distribution of obstacles. The FMF is derived to minimize the effect of the measurement noise through the Frobenius norm, and covariance matrix adaptation evolution strategy. The estimated information is used to derive the control input for the robust mobile robot navigation effectively. FDEDWA allows for the fast perception of the dynamic environment, and superior estimation performance, and the mobile robot can be controlled by a more optimal path while maintaining real-time performance. To demonstrate the performance of the proposed algorithm, simulations, and experiments were carried out under dynamic environments by comparing the latest dynamic window for dynamic obstacle, and the existing DWA.

In this paper, the security control problem is considered for nonlinear networked control systems under cyber attacks . The nonlinearities are described by interval type-2 fuzzy models. To enhance bandwidth utilization and improve network control performance, a hybrid-triggered scheme is proposed. The networked control systems contain parametric uncertainties and time delays and are subject to external disturbances and cyber attacks. A hybrid-triggered-based control method is established to ensure the robust stability and a prescribed performance of the resulting closed-loop systems against attacks. Numerical simulation and a practical example of the bolt-tightening tool are provided to illustrate the effectiveness of the new control design method.

In this paper, we discuss the modeling and stabilization problems for a class of discrete-time stochastic networked systems (DSNSs) subject to successive packet losses. The system under consideration is transformed into a stochastic one with bounded stochastic delay. Considering that the stochastic delay is characterized by nonuniform distribution, a new equivalent model is then constructed that enables the DSNS’s controller design to benefit from knowing the probability characteristic of packet losses. Specially, to verify this feature, the probabilities of the delay can be explicitly obtained by utilizing the formula of total probability, which is critical to model transformation and analyze practical problems that exist in DSNSs. Based on the proposed model, sufficient conditions are established under which the globally mean-square asymptotic stability of resulting stochastic delay closed-loop system is guaranteed, and a delay-distribution-dependent design procedure is then proposed. A numerical example with simulation is provided to validate the analytical results and demonstrate the effectiveness of the design procedure.

This paper focuses on exponential synchronization of complex networks controlled by aperiodically intermittent noise with time delays. An aperiodically intermittent controller is designed to synchronize complex networks and the interval of intermittent rate is derived. Delay observations on the state of noise are made and the upper bound of time delays is obtained. A sufficient condition for exponential synchronization of the underlying systems is derived. Moreover, the auxiliary networks without time delays are introduced and the sufficient criterion for stochastic synchronization of the networks is also obtained. The comparison principle plays a significant role in synchronizing complex networks in this paper. An example is given to demonstrate the feasibility and effectiveness of the new approaches.

This paper considers the problems on the mean square exponential synchronization and the almost surely exponential synchronization for neutral stochastic dynamical delay networks with Markovian switching via the single pinning impulsive control. By using a unified approach incorporating the concept of average impulsive interval and Lyapunov function, and the Borel–Cantelli lemma, two criteria on the mean square exponential stability and the almost surely exponential stability for impulsive neutral stochastic delay systems with Markovian switching are derived. It is shown that the impulsive control with a suitable impulsive strength can stochastically stabilize the underlying systems. With the symmetry and irreducibility of the switching topologies, and the obtained stochastic stability criteria, sufficient conditions are derived to determine the mean square exponential synchronization of the underlaying networks via the single pinning impulsive controller, which are given with algebraic inequalities and ${\mathcal {M}}$ -matrix. The almost surely exponential synchronization is also analyzed. Two examples are given to verify the validity of the theoretic results presented.

This study is concerned with the design of an event-triggered fault estimation and fault tolerant model predictive control scheme for linear systems subject to disturbances and actuator faults by using the method of delta operator. First, an observer is established to estimate the state and the fault simultaneously. An H 2 / H ∞ model predictive controller with fault compensation signals is then proposed through solving a constrained optimisation problem. An event-triggered scheme is employed to conserve the network bandwidth. The new design techniques are shown to be effective via simulation results.

This paper considers the problem of achieving time-varying formation for second-order multi-agent systems with actuator hysteresis, unknown system dynamics and external disturbances. A novel adapti...

In this manuscript we formulate the general Target–Attacker–Defender differential game of degree in both its continuous-time and discrete-time turn-based variants in n-dimensional euclidean space. ...

This paper presents a nonmyopic energy management strategy (EMS) for controlling multiple energy flow in fuel cell hybrid vehicles. The control problem is solved by convex programing under a partially observable Markov decision process-based framework. We propose an average-reward approximator to estimate a long-term average cost instead of using a model to predict future power demand. Thus, the dependence between the system closed-loop performance and the model accuracy for predicting the future power demand is decoupled in the energy management design for fuel cell hybrid vehicles. The energy management scheme consists of a real-time self-learning system, an average-reward filter based on the Markov chain Monte Carlo sampling, and an action selector system through the rollout algorithm with a convex programing-based policy. The performance evaluation of the EMS is conducted via simulation studies using the data obtained from real-world driving experiments and its performance is compared with three benchmark schemes.

In this paper, the issue of stochastic stabilization of nonlinear systems via intermittent Brownian motion under random disturbance is investigated. Two types of noise are considered. One is Brownian noise that is regarded as an intermittent control input. The other is the random disturbance caused by the unknown external environment, which can lead to time fluctuations of the intermittent Brownian noise stabilization. We first present an intermittent stochastic system with random disturbance. We then propose an almost sure stability condition for the underlying system. The stability results are then used to stabilize the memristive Lorenz system. Numerical simulations are also given to validate the effectiveness of the theoretic results obtained.

The three agent zero-sum differential game of active defence is investigated; in this pursuit-evader game, agent A attempts to capture agent T, and agents T and D coordinate to achieve the opposite...

This paper is concerned with the distributed estimation problem in sensor networks subjected to unknown attacks. Network attacks are considered to exist in two classes of channels: 1) communication channels from the plant to sensors and 2) communication channels among sensors. The status of an attack is viewed as a stochastic phenomenon, and the transmitted information will be affected when the attacker successfully carries out an attack on the related data packet. Based on the sensors’ own measurements and their neighbors’ local information, a novel distributed estimation model against two-channel stochastic attacks is presented. A sufficient condition on the existence of the desired distributed ${H} _{\boldsymbol {\infty }}$ estimators is derived and the distributed estimator gains are designed by solving a linear matrix inequality. Two illustrative examples are provided to demonstrate the effectiveness of the new design techniques.

This paper presents an active fault tolerant control scheme for discrete-time semi-Markov jump systems. The semi-Markov kernel approach and the finite sojourn time restriction are employed to obtain finite stability sufficient conditions for stability and stabilization. Semi-Markov processes are applied to model the faults occurrence of the actuators and the fault detection process. To guarantee the stability of the resulting closed loop system, an elapsed-time-dependent controller design method is applied. Finally, a numerical example and a vertical take-off and landing vehicle (VTOL) example are shown to illustrate the effectiveness and potential of the proposed design technique.

This paper studies the control synthesis for uncertain semi-Markov jump systems in a discrete-time domain subjected to external disturbance. The switching between modes is determined by a function ...

In this paper, a control scheme is proposed for a group of elliptical agents to achieve a predefined formation. The agents are assumed to have the same dynamics, and communication among the agents are limited. The desired formation is realized based on the reference formation and the mapping decision. In the control design, searching algorithms for both cases of minimum distance and tangents are established for each agent and its neighbors. In order to avoid collision, an optimal path planning algorithm based on collision angles, and a self-center-based rotation algorithm are also proposed. Moreover, randomized method is used to provide the optimal mapping decision for the underlying system. Two examples and analyses are presented to demonstrate the effectiveness and potential of the new control scheme.

This paper presents a moving horizon algorithm with mode detection for state estimation in Markov jump systems with Gaussian noise. This state estimation scheme is a combination of the maximum-likelihood algorithm and the moving horizon approach. The maximum-likelihood algorithm provides optimal estimate of the mode sequence within a moving fixed-size horizon, and the moving horizon estimation is an optimization-based solution. As a result, a mode detection-moving horizon estimator design method is proposed. Through the stochastic observability properties of the Markov jump linear systems, sufficient conditions for stability are established.

Formation control for a team of robots is investigated in this paper. We consider the case where no communication channels are available. Only sensors with limited ranges can provide the robots wit...

In this study, an optimally distributed formation control approach is proposed for mixed-order multi-agent systems to achieve formation consistency with obstacle avoidance. The systems consist of unmanned aerial vehicles and ground robots. The authors present a formation consistency control for a mixed-order system under switching topologies. An optimally distributed method is developed by matrix transformation method and linear quadratic regulator theory, which leads to an optimisation on both horizontal and vertical control. For obstacle avoidance, a non-quadratic cost function is introduced based on optimal control. The resulting closed-loop mixed-order system is validated to be stable with an optimal performance. The formation consensus and obstacle avoidance are verified by simulations to show the effectiveness and potentials.

To gain a better understanding the synchronization mechanism of networked neurons, this paper studies the synchronization control for a class of reaction–diffusion FitzHugh–Nagumo systems, associated with a digraph containing at least one directed spanning tree. A novel control method adopting spatial sampling strategies is proposed, in which the control inputs are constructed directly on the spatial means of system state variables on sampling subsets. After discussing the existence and uniqueness of classical solutions, we show analytically that the synchronization of the controlled systems is equivalent to that of the corresponding FitzHugh–Nagumo systems under the corresponding control inputs. Based on the study on general algebraic connectivity, a sufficient condition for the system synchronization is given, together with case simulations to illustrate the effectiveness and potential of the new control method.

This paper studies the problem of mean square asymptotical synchronization and $$H_\infty $$ synchronization for coupled stochastic reaction–diffusion systems (SRDSs) via boundary control. Based on the deduced synchronization error dynamic, we design boundary controllers to achieve mean square asymptotical synchronization. By virtue of Lyapunov functional method and Wirtinger’s inequality, sufficient conditions are obtained for ensuring mean square asymptotical synchronization. When coupled SRDSs are subject to external disturbance, mean square $$H_\infty $$ synchronization is investigated and corresponding criterion is presented under a designed boundary controller. In addition to focusing on systems with Neumann boundary conditions, we also briefly study coupled SRDSs with mixed boundary conditions and sufficient conditions are provided to achieve the desired performance. Numerical examples are used to verify the effectiveness of our theoretical results.

This paper proposes a novel interval observer design method for discrete-time linear systems with unknown but bounded disturbance and measurement noise. The proposed interval observer has a new structure that provides more design degrees of freedom. A direct method based on H ∞ technique is used to improve the accuracy of interval estimation. The design conditions are formulated into linear matrix inequalities, which can be efficiently solved. Two numerical examples are given to illustrate the design and validate the performance of the interval observers.

This paper investigates the construction of a fuzzy functional observer for nonlinear systems with time-delays, and the application of the observer to estimate the state functions of the parallel distributed compensation controller for stabilizing the system. Two types of time-delays are considered: constant and time-varying delays with bounded time derivative. Stability conditions are obtained using Lyapunov–Krasovskii functional approach; and the conditions are transformed into linear matrix inequalities with equality constraints so that observer parameters can be calculated using the solution of these inequalities. Functional observer construction procedures are presented considering both constant and time-varying time-delays. Two examples, including one for obtaining a power system stabilizer for a single machine infinite bus system, are presented to illustrate effectiveness of the proposed design procedures.

This paper addresses the control problem for a class of discrete-time Markov jump linear systems with partially unknown transition probabilities using model predictive controller subject to external disturbances and input constraints. Our focus is on the design of a model predictive controller to stabilize the system with a given mixed H2/H∞ performance index. Sufficient conditions are derived in terms of a set of linear matrix inequalities. Examples are presented to demonstrate the effectiveness of the proposed controller design method.

This paper presents a novel method of synthesising controllers for nonlinear systems represented by Takagi--Sugeno fuzzy model using functional observer. The proposed method uses minimum order observer for estimating the control input of each rule as a function of states. Existence conditions of the functional observer-based controller are provided in the form of rank equality conditions. The stability conditions of the observer are obtained using quadratic Lyapunov function, and the conditions are presented in the form of linear matrix inequalities. Observer parameters can be obtained by solving these inequalities. Finally, systematic synthesis procedures for constructing the observer are presented. Examples with simulation output are given to demonstrate this method and to compare its performance with a conventional full-state observer-based controller.

In this paper, distributed formation is studied for a team of mobile robots including leaders and followers. Followers are able to sense the relative displacements to neighbouring followers...

This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi–Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation error dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.

This paper considers the exponential stability in p th( p ≥ 1 ) moment and the almost sure exponential stability for neutral stochastic delay systems with Markovian switching. By using the generalized integral inequality and the nonnegative local martingale convergence theorem, the Lyapunov stability results on the exponential stability in p th( p ≥ 1 ) moment and the almost sure exponential stability for such systems are given when the time-varying delay is a bounded measurable function and the coefficients in the monotonicity condition are time varying. The derived results are illustrated by two examples.

This paper considers H − ∕ H ∞ fault detection observer design in the finite frequency domain for a class of linear parameter-varying (LPV) descriptor systems. We propose a novel fault detection observer with a non-singular structure. To make the residual sensitive to faults and robust against disturbances, we develop a finite frequency H − ∕ H ∞ design method based on a generalized Kalman–Yakubovich–Popov lemma for LPV descriptor systems. Design conditions for the proposed fault detection observer are derived and converted as linear matrix inequalities. Simulation studies are conducted to demonstrate the performance of the proposed method.

This paper considers robust fault estimation in the finite frequency domain for linear continuous-time descriptor systems. Based on state augmentation, we propose a fault estimation observer with a...

This paper presents a sliding mode control scheme for tracking control of nonlinear singularly perturbed systems in the presence of model errors and external disturbances. A dual-loop feedback control is developed to provide accurate tracking capability and sufficient robustness to system uncertainties. A sliding mode controller is proposed in the outer-loop feedback design such that the plant states are stabilised for given reference trajectories, while an additional robust controller is designed in the inner loop to increase the adaptability to uncertainties, and reduce the effect of unmodelled high-frequency dynamics on plant dynamics. An appealing feature of the control scheme is the attenuation of chattering. The effectiveness and merits of the new control scheme developed are shown via a verification example of velocity control of a quad-rotor.

This paper proposes a new adaptive controller for a class of uncertain nonlinear systems with a quantized signal. Fuzzy logic systems are utilized to approximate nonlinear terms without imposing prior matching conditions required. A hysteretic type of quantizer is incorporated to reduce chattering. A new adaptive backstepping controller is designed to guarantee that the underlying uncertain nonlinear system is semiglobally uniformly ultimately bounded. Two numerical examples are presented to demonstrate the effectiveness and potential of the proposed techniques.