Decentralized Event-Triggered Online Adaptive Control of Unknown Large-Scale Systems Over Wireless Communication Networks

Decentralized Control for Large-Scale Systems With Actuator Faults and External Disturbances: A Data-Driven Method

This article investigates optimal control for a class of large-scale systems using a data-driven method. The existing control methods for large-scale systems in this context separately consider disturbances, actuator faults, and uncertainties. In this article, we build on such methods by proposing an architecture that accommodates simultaneous consideration of all of these effects, and an optimization index is designed for the control problem. This diversifies the class of large-scale systems amenable to optimal control. We first establish a min-max optimization index based on the zero-sum differential game theory. Then, by integrating all the Nash equilibrium solutions of the isolated subsystems, the decentralized zero-sum differential game strategy is obtained to stabilize the large-scale system. Meanwhile, by designing adaptive parameters, the impact of actuator failure on the system performance is eliminated. Afterward, an adaptive dynamic programming (ADP) method is utilized to learn the solution of the Hamilton-Jacobi-Isaac (HJI) equation, which does not need the prior knowledge of system dynamics. A rigorous stability analysis shows that the proposed controller asymptotically stabilizes the large-scale system. Finally, a multipower system example is adopted to illustrate the effectiveness of the proposed protocols.

Adaptive Decentralized Control for Interconnected Time-Delay Uncertain Nonlinear Systems With Different Unknown Control Directions and Deferred Full-State Constraints

In this article, the problem of adaptive decentralized control is investigated for a class of interconnected time-delay uncertain nonlinear systems with different unknown control directions and deferred asymmetric time-varying (DTV) full-state constraints. By constructing the novel time-varying asymmetric integral barrier Lyapunov function (TVAIBLF), the conservative limitation of constant integral barrier Lyapunov function (IBLF) or symmetric IBLF is reduced and the need on the prior knowledge of control gains is also avoided, while the deferred constraints directly imposed on the states of system are achieved by introducing the shifting function into the controller design. Furthermore, based on the Nussbaum-type functions, a new adaptive decentralized control strategy for interconnected time-delay nonlinear systems with subsystems having different control directions is proposed via backstepping method. And it is proven that the proposed control method can guarantee that all signals in closed-loop system are bounded and the transform errors asymptotically converge to zero. Finally, the effectiveness of the proposed control strategy is illustrated through the simulation results.

Decentralized Adaptive Neural Inverse Optimal Control of Nonlinear Interconnected Systems

Existing methods on decentralized optimal control of continuous-time nonlinear interconnected systems require a complicated and time-consuming iteration on finding the solution of Hamilton-Jacobi-Bellman (HJB) equations. In order to overcome this limitation, in this article, a decentralized adaptive neural inverse approach is proposed, which ensures the optimized performance but avoids solving HJB equations. Specifically, a new criterion of inverse optimal practical stabilization is proposed, based on which a new direct adaptive neural strategy and a modified tuning functions method are proposed to design a decentralized inverse optimal controller. It is proven that all the closed-loop signals are bounded and the goal of inverse optimality with respect to the cost functional is achieved. Illustrative examples validate the performance of the methods presented.

Decentralized Event-Driven Constrained Control Using Adaptive Critic Designs

We study the decentralized event-driven control problem of nonlinear dynamical systems with mismatched interconnections and asymmetric input constraints. To begin with, by introducing a discounted cost function for each auxiliary subsystem, we transform the decentralized event-driven constrained control problem into a group of nonlinear H₂ -constrained optimal control problems. Then, we develop the event-driven Hamilton-Jacobi-Bellman equations (ED-HJBEs), which arise in the nonlinear H₂ -constrained optimal control problems. Meanwhile, we demonstrate that all the solutions of the ED-HJBEs together keep the overall system stable in the sense of uniform ultimate boundedness (UUB). To solve the ED-HJBEs, we build a critic-only architecture under the framework of adaptive critic designs. The architecture only employs critic neural networks and updates their weight vectors via the gradient descent method. After that, based on the Lyapunov approach, we prove that the UUB stability of all signals in the closed-loop auxiliary subsystems is assured. Finally, simulations of an illustrated nonlinear interconnected plant are provided to validate the present designs.

Dynamic Event-Sampled Control of Interconnected Nonlinear Systems Using Reinforcement Learning

We develop a decentralized dynamic event-based control strategy for nonlinear systems subject to matched interconnections. To begin with, we introduce a dynamic event-based sampling mechanism, which relies on the system's states and the variables generated by time-based differential equations. Then, we prove that the decentralized event-based controller for the whole system is composed of all the optimal event-based control policies of nominal subsystems. To derive these optimal event-based control policies, we design a critic-only architecture to solve the related event-based Hamilton-Jacobi-Bellman equations in the reinforcement learning framework. The implementation of such an architecture uses only critic neural networks (NNs) with their weight vectors being updated through the gradient descent method together with concurrent learning. After that, we demonstrate that the asymptotic stability of closed-loop nominal subsystems and the uniformly ultimate boundedness stability of critic NNs' weight estimation errors are guaranteed by using Lyapunov's approach. Finally, we provide simulations of a matched nonlinear-interconnected plant to validate the present theoretical claims.

Model-Free Adaptive Optimal Control for Unknown Nonlinear Multiplayer Nonzero-Sum Game

In this article, an online adaptive optimal control algorithm based on adaptive dynamic programming is developed to solve the multiplayer nonzero-sum game (MP-NZSG) for discrete-time unknown nonlinear systems. First, a model-free coupled globalized dual-heuristic dynamic programming (GDHP) structure is designed to solve the MP-NZSG problem, in which there is no model network or identifier. Second, in order to relax the requirement of systems dynamics, an online adaptive learning algorithm is developed to solve the Hamilton-Jacobi equation using the system states of two adjacent time steps. Third, a series of critic networks and action networks are used to approximate value functions and optimal policies for all players. All the neural network (NN) weights are updated online based on real-time system states. Fourth, the uniformly ultimate boundedness analysis of the NN approximation errors is proved based on the Lyapunov approach. Finally, simulation results are given to demonstrate the effectiveness of the developed scheme.

Composite Neural Learning Fault-Tolerant Control for Underactuated Vehicles With Event-Triggered Input

This article presents a novel composite neural learning fault-tolerant algorithm to implement the path-following activity of underactuated vehicles with event-triggered input. With the input event-triggered mechanism, the dominant superiority is to reduce the communication burden in the channel from the controller to actuators. In the proposed scheme, the system uncertainties are dealt with in the fusion of the neural networks (NNs) and the dynamic surface control (DSC) method. The serial-parallel estimation model (SPEM) is constructed to estimate the error dynamics, where the derived prediction error could improve the compensation effect of the NNs. As for the gain uncertainties and the unknown actuator faults, four adaptive parameters are designed to stabilize the related perturbation and not be affected by the triggering instants. Based on the direct Lyapunov theorem, considerable efforts have been made to guarantee the semiglobal uniformly ultimately bounded (SGUUB) stability of the closed-loop system. Finally, comparison and practical experiments are illustrated to verify the superiority of the proposed algorithm.