This study is concerned with the problem of reliable dissipative control for a class of discrete-time switched singular systems with mixed time delays and multiple actuator failures. The failure probability of each actuator is individually quantified and is governed by an individual random variable satisfying a certain probabilistic distribution in the interval [0, 1]. Attention is focused on identifying a class of slow switching signals and designing a set of reliable mode-dependent state-feedback controllers such that, for all admissible mixed time delays and multiple probabilistic actuators faults, the closed-loop system is stochastically exponentially admissible and strictly (𝒬, 𝒮, ℛ)-dissipative. By using the Lyapunov function approach and the average dwell-time scheme, sufficient conditions for the existence of such class of stabilising switching signals and the reliable mode-dependent controllers are derived in terms of linear matrix inequalities, and the explicit expression for the desired controller gains is also given. A numerical example is given to demonstrate the effectiveness of the theoretical results.

In this study, we discuss a dynamic scaling approach exploiting the separator-type robust stability condition for discrete-time linear time-invariant systems. We confine ourselves to a class of what we call finite impulse response (FIR) separators, and establish a systematic and practical framework of searching for an FIR separator satisfying the separator-type condition. The first step is to give explicit structure of FIR separators suitable for dealing with a given set of structured uncertainties. The second step is to give an explicit linear matrix inequality condition for the analysis. In particular, a minimal realisation of an augmented system to be dealt with in FIR scaling is derived, which is non-trivial and is very important in reducing the computational load in the numerical computation. Effectiveness of the developed framework is demonstrated numerically, through comparison with the conventional static scaling and μ-analysis.

In this study, a novel numerical adaptive learning control scheme based on adaptive dynamic programming (ADP) algorithm is developed to solve numerical optimal control problems for infinite horizon discrete-time non-linear systems. Using the numerical controller, the domain of definition is constrained to a discrete set that makes the approximation errors always exist between the numerical controls and the accurate ones. Convergence analysis of the numerical iterative ADP algorithm is developed to show that the numerical iterative controls can make the iterative performance index functions converge to the greatest lower bound of all performance indices within a finite error bound under some mild assumptions. The stability properties of the system under the numerical iterative controls are proved, which allow the present iterative ADP algorithm to be implemented both on-line and off-line. Finally, two simulation examples are given to illustrate the performance of the present method.

This study investigates the finite-time cooperative-tracking problem for a class of networked Euler–Lagrange systems with a leader–follower structure, where the leader has an active dynamics and only a subset of the followers have access to the leader system. A novel framework for the design of finite-time cooperative-tracking controller is proposed by using sliding-mode control theory and graph theory. First, a finite-time sliding-mode tracking protocol is proposed for the networked Lagrange systems in the presence of bounded model uncertainties and external disturbances. Under the condition that the bounds of the model uncertainties and external disturbances are unknown, adaptive finite-time cooperative-tracking protocol is then presented. The finite convergence time is also estimated. Finally, we analyse the tracking performance of the networked uncertain Lagrange systems under the action of a continuous control protocol, which guarantees that the tracking errors converge to an arbitrarily small bound around zero in finite time. Simulation examples are presented to show the effectiveness of the obtained theoretical results.

Data-driven fault detection has emerged as one of the most prevalent topics in the fault diagnosis. In this study, a novel data-driven subspace-based fault-detection scheme is proposed to handle the problem of fault detection with system uncertainties in solar power generation systems. A data-driven subspace-based predictor is developed by using the input–output measurements. The residual signal is generated from the predictive error of the predictor and a fault-detection filter that is designed to diminish the influence of system uncertainties. An adaptive algorithm is developed for updating the fault-detection filter. Faults can be detected by comparing the evaluated residual signal with a threshold. The reliability of the designed fault-detection scheme is verified in three cases in a solar power generation system.

In this study, neural networks (NN) impedance control is proposed for robot–environment interaction. Iterative learning control is developed to make the robot dynamics follow a given target impedance model. To cope with the problem of unknown robot dynamics, NN are employed such that neither the robot structure nor the physical parameters are required for the control design. The stability and performance of the resulted closed-loop system are discussed through rigorous analysis and extensive remarks. The validity and feasibility of the proposed method are verified through simulation studies.

This study deals with the problem of delay-dependent dissipative filtering for genetic regulatory networks (GRNs) with norm-bounded parameter uncertainties and time-varying delays. It is assumed that the non-linear function describing the feedback regulation satisfies the sector-bounded condition. Improved delay-dependent stochastic stability and filter design method for stochastic GRNs are obtained by applying the delay fractioning technique, Jensen inequalities and introducing some proper slack matrix variables. The conservatism caused by either model transformation or bounding techniques is reduced. Sufficient conditions of the robust stability and filtering problems are proposed, respectively. The filter, which can guarantee the resulting error system to be asymptotically stable in the mean-square sense and satisfy a prescribed performance level for all delays no larger than a given upper bound, are constructed. A numerical example is provided to demonstrate effectiveness of the proposed results in this study.

This study investigates the finite-time attitude-tracking problem for rigid spacecraft. A novel non-singular terminal sliding-mode control (NTSMC) law is designed to provide finite-time convergence and fast, high control precision even though inertia uncertainties and external disturbances affect the spacecraft systems under actuator failures and saturations. The proposed NTSMC scheme is chattering suppression and singularity-free. Simulation results are presented to demonstrate the efficiency of the proposed method.

In this study, we consider an acyclic rigid formation with a group of mobile autonomous agents moving in a two-dimensional space. The formation is generated via a Henneberg sequence construction in which there is one global leader that does not follow any other agents, one first-follower that only follows the global leader, and each of other agents has two leaders, which is added by a vertex addition or an edge splitting operation. The entire formation moves with the leadership of the global leader. Every follower agent tries to maintain distances towards its leaders. Under the constraint of the acceleration for the global leader, the distributed formation control laws are proposed for the followers that only use the locally relative distance measurement. The control law of the first-follower is proposed, which needs to know the velocity of the global leader and the relative distance between the global leader and itself. The global asymptotic stability of the expected formation is proved via a Lyapunov-based technique for the considered multi-agent system. Moreover, the stable rigidity problem of a formation is investigated for the proposed distributed relative position-only formation control law. Necessary and sufficient conditions are provided that must be satisfied by the architecture of the underlying graph. Simulation results illustrate the effectiveness of the proposed formation control approach.

An approximation-based adaptive fault-tolerant (AFT) control problem is investigated for strict-feedback non-linear systems with unknown time-delayed non-linear faults. The error surfaces restricted by prescribed performance bounds are employed to guarantee the transient performance at the moment when faults with unknown occurrence time and magnitude occur. Based on the surfaces, we design a memoryless AFT control system where the function approximation technique using neural networks is applied to adaptively approximate unknown non-linear effects and changes in model dynamics because of the time-delayed faults. It is shown from Lyapunov stability theorem that the tracking error of the proposed control system is preserved within the prescribed performance bound and converges to an adjustable neighbourhood of the origin regardless of unknown time-delayed non-linear faults.

This study investigates the finite-time tracking for double-integrator multi-agent systems with bounded control input under the conditions of fixed and switching jointly reachable digraphes. In detail, two continuous distributed tracking protocols are designed to track the virtual leader in finite time with the same velocity and converging position. In particular, one introduces a special continuous distributed tracking protocol with bounded control input to track the virtual leader in finite time and reduce the chatter together. An example is also given to validate the proposed approaches.

In this study, the authors consider the deployment of unmanned aerial vehicle networks for task accomplishment within a closed region. Each agent with limited sensing range and communication range needs to take charge of the task accomplishment within a part of the whole region. The main objective is to optimise the deployment of the agents such that the maximum travelling time the agents take to reach a place within the surveillance region is minimised. The deployment issue is formulated as the worst-case disc-covering problem and a distributed cooperative control strategy is designed for agents with limited mobility. It is proven that by the proposed control strategy the network configuration converges to a local optimum configuration. Moreover, a combined optimisation approach is developed to improve the performance by optimising the initial configuration. To guarantee the proper working of the designed control strategy and K-connectivity of the network, a distributed topology control scheme is proposed. Finally, the effectiveness of the proposed control strategy is testified by simulation.

This study presents a controller design method to achieve the robust attitude control for uncertain quadrotors. The proposed linear time-invariant controller consists of a proportional–derivative (PD) controller and a robust compensator. The PD controller is designed for the nominal linear system to achieve the desired tracking and the robust compensator is added to restrain the influence of the uncertainties. It is proven that attitude tracking errors are bounded and the boundaries can be made as small as desired. Experimental results on the quadrotor are given to confirm the effectiveness of this control method.