Adaptive event-triggered decentralized control for nonlinear interconnected large-scale systems with actuator failures: a fully actuated system approach

This paper investigates decentralized adaptive event-triggered fault-tolerant control for interconnected nonlinear delay systems with actuator failures. The actuator failures suffered include loss of effectiveness and bias faults. A control scheme based on the K-filter is proposed, which effectively compensates for the effects of unknown actuator failures. A hyperbolic tangent function and neural network are introduced to approximate the unknown interconnection function and nonlinear delay function. By introducing the dynamic surface control method, the “explosion of complexity” issue is addressed. Furthermore, our proposed controller can ensure that all states of the corresponding closed-loop system are semi-globally uniformly ultimately bounded and that the tracking error can converge to a small neighborhood of zero. Meanwhile, Zeno behavior can be effectively avoided. Finally, the validity of the proposed control scheme is verified using a simulation example.

The adaptive control for strict-feedback nonlinear systems has drawn a lot of attention in various communities. Since neural network is a useful universal-approximator to approximate unknown plant model, the neural network–based adaptive control for nonlinear systems has attracted substantial interest over decades. Furthermore, to reduce the controller updating and save the control resource, the event-triggered mechanism has been widely applied. In this paper, the RBF neural network is applied to construct the state and composite disturbance observers and the back-stepping and Lyapunov-like method are applied to design the event-triggered adaptive controller. The theoretical framework of adaptive fault-tolerant control issue for strict-feedback nonlinear system that suffer from both unknown mismatched disturbance and actuator failures is formulated. This paper comes up with a novel event-triggered control strategy to guarantee that the tracking issue is resolved with better desired performance. In this study, a unified theoretical mechanism is developed to tackle the case where some factors consisting of unknown state variables, unknown mismatched disturbance, and actuator failures as well as event-triggered effects are merged together. We expect to extend the proposed method for the self-triggered case.

In this article, the problem of decentralized event-triggered fault-tolerant control (FTC) for a class of interconnected nonlinear systems with unknown strong coupling and actuator failures is considered. In order to enable each subsystem output to track the desired trajectory, a new decentralized adaptive control scheme is given. First, an event-triggering mechanism is introduced to reduce the signal transmission frequency between the controller and the actuator. Second, a fuzzy high-gain observer is designed for each subsystem to estimate unknown nonlinear functions and actuator efficiency factor. Third, a decentralized FTC strategy is proposed to compensate for the effects of actuator failures and achieve the desirable system tracking performance. With the aid of graph theory, it is shown that all the closed-loop signals are semiglobally uniformly ultimately bounded, and the tracking error of each subsystem can converge to an arbitrarily small residual set by adjusting a design parameter. The effectiveness of the proposed scheme is demonstrated by a practical interconnected system.

The problem of decentralised adaptive robust stabilisation is considered for a class of uncertain large-scale interconnected non-linear systems with time-varying delays. It is assumed that the upper bounds of the uncertainties and interconnection terms are unknown, and that the time-varying delays are any non-negative continuous and bounded functions, and do not require their derivatives to be less than one. In particular, it is only required that the non-linear interconnection terms, which can also include time-varying delays, are bounded in any non-negative non-linear functions, which are not required to be known for the system designer. For such a class of uncertain large-scale time-delay interconnected non-linear systems, a new method is presented whereby a class of continuous memoryless decentralised local adaptive robust state feedback controllers with a rather simpler structure is proposed. It is also shown that the solutions of uncertain large-scale time-delay interconnected systems can be guaranteed as uniformly exponentially convergent towards a ball that can be as small as desired. Finally, a numerical example is given to demonstrate the validity of the results.

This paper studies the issue of event-triggered adaptive fixed-time control for uncertain large-scale nonlinear systems. A state observer is employed to estimate unknown states. By combining the technologies of command filter and backstepping control, a command filtered-based fixed-time adaptive control approach is proposed, which can cope with the issue of ‘complexity explosion’ in the backstepping design scheme. Meanwhile, a novel continuous hyperbolic tangent function is designed to handle the difficulty of fixed-time stability analysis with output-feedback control form, where two types of observer errors are needed in the designed Lyapunov function to achieve the fixed-time stability. Further, based on the event-triggered strategy, an event-triggered fixed-time adaptive controller is designed. Based on Lyapunov stability theory, it is proven that all signals of the closed-loop system are bounded. Finally, the effectiveness of the presented approach is validated by a simulation.

Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

Finite-time adaptive event-triggered fault-tolerant control of nonlinear systems based on fuzzy observer

An adaptive event-triggered control scheme is presented for nonlinear interconnected systems in strict-feedback structure in the presence of infinite actuator faults. Based on event-triggered control algorithm, a decentralized adaptive backstepping control strategy is designed for large-scale systems with virtual control coefficients and unknown interactions among subsystems. When the relative threshold is exceeded, the control input will be updated by the triggering mechanism. The adaptive laws are given via backstepping control technique. The selection of virtual control coefficients will have a great influence on the performance of interconnected systems. All signals of the large-scale systems are semi-globally uniformly ultimately bounded and the tracking errors are ensured in a small scale of the origin, and Zeno behaviour is avoided. The effectiveness of the theoretical results is validated by some simulation results.

This paper studies the event-triggered adaptive compensation control problem of nonlinear cyber-physical systems under false data injection (FDI) attack and actuator failure. Firstly, in order to save the limited network resources, a new adaptive event triggering scheme (AETS) is presented, whose threshold can be adjusted according to the change of system state. Secondly, an observer based on neural networks is designed. Then, we design an event-triggered adaptive controller and adaptive laws to effectively compensate for FDI attack and actuator failure. Furthermore, through the system stability analysis, the result shows that the tracking error can converge exponentially to a compact set with an adjustable radius. Finally, the theoretical results are verified by the manipulator system example.

Delay-dependent robust and reliable [formula omitted] fuzzy hyperbolic decentralized control for uncertain nonlinear interconnected systems

An extension distributed discrete DISOPE (Dynamic Integrated System Optimisation and Parameter Estimation) algorithm is proposed to address the cooperative optimal control of multiple discrete-time nonlinear interconnected large-scale systems. The information flow among multiple interconnected large-scale systems is governed by a directed topological graph, where each large-scale system comprises numerous subsystems. Interactions between these subsystems are established through interconnected terms. Firstly, it employs model optimisation techniques to convert the nonlinear optimal control problem into that of a modified linear quadratic problem. Then, the optimality conditions of the modified model system are derived via the maximum principle. An extended distributed DISOPE algorithm is designed and the algorithm's convergent solution is realised through the application of iterative techniques. The designed algorithm's convergence has been validated, confirming that the resulting solution meets the requirements of the actual optimisation problem. Simulation examples are used to illustrate the algorithm's efficacy.

Decentralized adaptive neural control of nonlinear interconnected large-scale systems with unknown time delays and input saturation

This paper investigates event-triggered adaptive compensation control in the face of uncertain stochastic nonlinear system with actuator failure and output dead-zone. It is still an arduous task and challenge to design a compensation controller for uncertain stochastic nonlinear system. In order to avoid damaging output caused by the nonlinearity of the system, blended neural network integration with Nussbaum-type function is proposed. It is established to ensure the provision of the tracking error constraints, which is based on backstepping Lyapunov function technique. Additionally, system transmission resource constraints and actuator failure problems exist simultaneously in the system, which is extremely challenging for control design. More transmission resources are demanded when the system is suffered with actuator failures, while the system transmission resource is limited. Thus, the requirements cannot be achieved. It is difficult and challenged to ensure the system tracking performance. Using the proposed event-triggered controller and combining the Lyapunov synthesis, a novel optimization algorithm is deduced to guarantee the closed-loop system stability and the convergence of the tracking error. The simulation results illustrate the effectiveness of the proposed neural networks adaptive control approach.

Purpose This paper aims to investigate the problem of adaptive neural finite-time self-triggered tracking control for interconnected large scale nonlinear systems in nonstrict-feedback forms with sensor faults. Design/methodology/approach To begin with, by combining backstepping techniques and neural networks (NNs), an adaptive NN controller is designed to compensate for sensor faults. Then, command filters are introduced to deal with the complexity explosion problem in backstepping design processes. Moreover, to reduce unnecessary data transmissions, a self-triggered control strategy is presented. Findings Based on self-triggered strategy, an adaptive neural finite-time control scheme for interconnected large-scale systems with sensor faults is proposed. Originality/value This article considers sensor faults in interconnected large-scale nonlinear systems with nonstrict-feedback forms. Moreover, the introduction of command filters not only effectively avoids the complexity explosion problem arising from the repetitive differentiation of virtual control inputs, but also simplifies the controller design process. Besides, this paper proposes a self-triggered mechanism that calculates the next trigger point based on current system data, overcoming the need for continuous monitoring of measurement errors in event-triggered mechanisms. Furthermore, the controller guarantees the finite-time stability of interconnected large-scale systems, with the tracking error converging to a small neighborhood of the origin within a finite time frame.

Event-triggered adaptive compensation control for stochastic nonlinear systems with multiple failures: An improved switching threshold strategy.