Adaptive Output Regulation Research Articles

Adaptive Disturbance Suppression Output Regulation for Switched Systems With Multi-Source Disturbances

Adaptive Disturbance Suppression Output Regulation for Switched Systems With Multi-Source Disturbances

Distributed adaptive cooperative optimal output regulation via integral reinforcement learning

This paper studies the optimal cooperative output regulation problem for unknown linear multi-agent systems by the integral reinforcement learning technique. Existing results on this problem were obtained by a non-fully distributed learning process. In contrast, we propose a distributed learning algorithm over the jointly connected switching communication networks. Moreover, by modifying the existing algorithm, we reduce the computational cost and weaken the solvability conditions. Two numerical examples are used to illustrate the effectiveness of our approach.

Adaptive event-triggered robust output regulation for uncertain switched T-S fuzzy systems

This paper focuses on the adaptive event-triggered robust output regulation issue for uncertain switched Takagi–Sugeno fuzzy systems with unknown exosystems and model uncertainty. The purpose is to pursue the output regulationcharacteristic and the disturbances suppression characteristic. First, an event-triggered mechanism is introduced to ease the communication burden. Then, on the basis of the event-triggered program, an adaptive event-triggered robust output regulation control law is developed to battle the uncertainty and disturbances. Further, constraints are arrived to force the solvability of the adaptive event-triggered robust output regulation issue for uncertain switched Takagi–Sugeno fuzzy systems by virtue of the average dwell-time way. In the end, a simulation instance with a mass–spring-damping model employed is arranged to showcase the practicality and effectiveness of the developed control methodology.

Robust Adaptive Output Regulation for EV Dynamic Wireless Charging System With Sinusoidal Disturbance of Unknown Frequency

Robust Adaptive Output Regulation for EV Dynamic Wireless Charging System With Sinusoidal Disturbance of Unknown Frequency

Concurrent Learning Robust Adaptive Fault Tolerant Boundary Regulation of Hyperbolic Distributed Parameter Systems.

This article develops a robust adaptive boundary output regulation approach for a class of complex anticollocated hyperbolic partial differential equations subjected to multiplicative unknown faults in both the boundary sensor and actuator. The regulator design is based on the internal model principle, which amounts to stabilize a coupled cascade system, which consists of a finite-dimensional internal model driven by a hyperbolic distributed parameter system (DPS). To this end, a systematic sliding mode equipped with a backstepping approach is developed such that the robust state feedback control can be realized. Moreover, since the available information is a faulty boundary measurement at the right side point, state estimation is required. However, due to the presence of boundary unknown faults, we need to solve an issue of joint fault-state estimation. Restrictive persistent excitation conditions are usually required to guarantee the exact estimation of faults but are unrealistic in practice. To this end, a novel concurrent learning (CL) adaptive observer is proposed so that exponential convergence is obtained. It is the first time that the spirit of CL is introduced to the field of DPSs. Consequently, the observer-based adaptive boundary fault tolerant control scheme is developed, and rigorous theoretical analysis is given such that the exponential output regulation can be achieved. Finally, the effectiveness of the proposed methodology is demonstrated via comparative simulations.

Adaptive optimal tracking control of networked linear systems under two-channel stochastic dropouts

This work investigates the adaptive optimal tracking control problem for networked discrete-time linear systems by directly using the data transmitted via communication networks. It is shown that the concerned problem can be addressed by solving two sub-problems: an adaptive optimal control one and an adaptive output regulation one. Two novel online reinforcement learning based policy iteration and value iteration algorithms are developed, which constitute an integrated framework to learn the optimal feedback control gain and the solutions to regulator equations by directly using the data transmitted via communication networks. Furthermore, it is shown that the tracking error of the closed-loop control system is mean square asymptotically stable even in the case of network packet stochastic dropouts. Simulation results demonstrate the efficacy of the proposed approaches.

Finite-Time Time-Varying Formation Tracking for Heterogeneous Nonlinear Multiagent Systems Using Adaptive Output Regulation.

The finite-time output time-varying formation tracking (TVFT) problem for heterogeneous nonlinear multiagent system (MAS) is investigated in this article, where the dynamics of the agents can be nonidentical, and leader's input is unknown. The target of this article is that the outputs of followers need to track leader's output and realize the desired formation in finite time. First, for removing the assumption that all agents are required to know the information of leader's system matrices and the upper boundary of its unknown control input in previous studies, a kind of finite-time observer is constructed by exploiting the neighboring information, which can estimate not only the leader's state and system matrices but also can compensate for the effects of unknown input. On the basis of the developed finite-time observers and adaptive output regulation method, a novel finite-time distributed output TVFT controller is proposed with the help of the technique of coordinate transformation by introducing an extra variable, which removes the assumption that the generalized inverse matrix of follows' input matrix needs to be found in the existing results. By means of the Lyapunov and finite-time stability theory, it is proven that the expected finite-time output TVFT can be realized by the considered heterogeneous nonlinear MASs within a finite time. Finally, simulation results demonstrate the efficacy of the proposed approach.

Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems with Application to Power Systems

Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems with Application to Power Systems

Refined Algorithms for Adaptive Optimal Output Regulation and Adaptive Optimal Cooperative Output Regulation Problems

Refined Algorithms for Adaptive Optimal Output Regulation and Adaptive Optimal Cooperative Output Regulation Problems

Output Feedback Based Adaptive Optimal Output Regulation for Continuous-Time Strict-Feedback Nonlinear Systems

Output Feedback Based Adaptive Optimal Output Regulation for Continuous-Time Strict-Feedback Nonlinear Systems

Adaptive Anti-Disturbance Output Regulation for Switched Neural Network Systems

Adaptive Anti-Disturbance Output Regulation for Switched Neural Network Systems

Adaptive Observer-Based Output Regulation With Non-Smooth Non-Periodic Exogenous Signals

Adaptive Observer-Based Output Regulation With Non-Smooth Non-Periodic Exogenous Signals

Distributed Optimization of Disturbed Nonlinear Multi-Agent Systems via Adaptive Fault-Tolerant Output Regulation

Distributed Optimization of Disturbed Nonlinear Multi-Agent Systems via Adaptive Fault-Tolerant Output Regulation

Adaptive Output Regulation of a Class of 1-D Hyperbolic PDEs With Unknown Boundary Scaled Parameters

This paper considers the problem of adaptive output regulation of a class of 1-D anti-collocated hyperbolic PDEs. The model is subject to unknown scaled parameters in both the boundary condition and boundary measurement. An adaptive boundary observer, providing online estimates of the system state and parameters, is designed. Particularly, to realize that the pure boundary state at left side tracks a reference signal, a challenging problem with the estimate of the true value of unknown scaled parameter appearing in measurement (at the left side) is tackled in this paper. It is shown that the proposed observer is exponentially convergent and furthermore the exponential output regulation and stabilization are achieved.

Adaptive optimal output regulation of unknown linear continuous-time systems by dynamic output feedback and value iteration

This paper proposes a value iteration based learning algorithm to solve the optimal output regulation problem for linear continuous-time systems, which aims at achieving disturbance rejection and asymptotic tracking simultaneously. Notably, the state is unmeasurable and the system dynamics is completely unknown, which greatly increases the challenge of solving the output regulation problem. Firstly, we present a dynamic output feedback controller design method by combining the internal model, setting a virtual exo-system, and constructing the augmented internal state without using any knowledge of system. Then, by establishing a novel iterative learning equation which requires no repeated finite window integral operations, an adaptive dynamic programming based learning algorithm with employing value-iteration scheme is proposed to estimate the optimal feedback control gain, which may lead to a reduction of the computational load. The analysis on solvability and convergence shows that the estimated control gain converges to the optimal control gain. Finally, a physical experiment on control of an unmanned quadrotor illustrates the effectiveness of the proposed algorithm.

Adaptive self-triggered control-based cooperative output regulation of heterogeneous multi-agent systems under sensor and actuator attack

The cooperative output regulation problem for heterogeneous multi-agent systems under sensor and actuator attacks with an adaptive self-triggering strategy is considered in this article. The attack considered is the false data injection attack. To resolve the system, a dynamic internal model is introduced for each agent, and a distributed adaptive self-triggering control scheme is proposed. The results show that intermittent communication is possible for all agents using the proposed control mechanism and no agent shows Zeno behavior. The adaptive controller is used to guarantee that the regulated output for the heterogeneous multi-agent systemss is achieved with cooperative uniform ultimate boundedness under both actuator and sensor attacks. Given the whole state of information is unavailable, the adaptive dynamic system is designed based on receivable compromised or uncompromised output information. The effectiveness of the given control scheme is illustrated by two examples.

Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach.

The dynamics of a robot may vary during operation due to both internal and external factors, such as non-ideal motor characteristics and unmodeled loads, which would lead to control performance deterioration and even instability. In this paper, the adaptive optimal output regulation (AOOR)-based controller is designed for the wheel-legged robot Ollie to deal with the possible model uncertainties and disturbances in a data-driven approach. We test the AOOR-based controller by forcing the robot to stand still, which is a conventional index to judge the balance controller for two-wheel robots. By online training with small data, the resultant AOOR achieves the optimality of the control performance and stabilizes the robot within a small displacement in rich experiments with different working conditions. Finally, the robot further balances a rolling cylindrical bottle on its top with the balance control using the AOOR, but it fails with the initial controller. Experimental results demonstrate that the AOOR-based controller shows the effectiveness and high robustness with model uncertainties and external disturbances.

Observer-Based Adaptive Inverse Optimal Output Regulation for a Class of Uncertain Nonlinear Systems

This paper addresses the adaptive inverse optimal output regulation problem for a class of uncertain nonlinear systems driven by an exosystem. The unknown parameters, internal disturbances, and unmeasured states are contained in the nonlinear system. Firstly, the output regulation problem is decomposed into a feedforward control design problem which can be solved by the internal model based on the output regulation theory, and an adaptive inverse optimal stabilization problem. Then an auxiliary system is designed, and a new state observer related to the auxiliary system is given. By combining adaptive control technology and inverse optimal control method, a novel adaptive output feedback inverse optimal controller is developed to make the output of the system track the reference signal fast. With this control strategy, all the signals of the closed-loop system are uniformly ultimately bounded (UUB), and the newly well-defined cost functional which is connected with the auxiliary system and the controller can be minimized. Finally, a simulation case is put forward to verify the feasibility of the newly raised controller and the state observer.

Adaptive cooperative output regulation of general directed knowledge-based leader-following networks

In this study, an adaptive cooperative output regulation problem is solved for a knowledge-based leader-follower heterogeneous multi-agent system over a directed communication network. Only partial information on the leader's dynamics and output parameters is available. We use a distributed observer with an exponential convergence rate to estimate the leader's unknown system matrix and output parameter and we design an adaptive algorithm to compute iteratively the linear matrix regulator equation online. We synthesize a state feedback controller composed of the distributed algorithm and the adaptive algorithm. Finally, through theoretical analysis and numerical example, we show that our design solves the cooperative output regulation problem with the leader's output parameter, system matrix, and output tracking error converging to the origin exponentially.

Learning-Based Adaptive Optimal Output Regulation of Discrete-Time Linear Systems

In this paper, we address the problem of model-free optimal output regulation of discrete-time systems that aims at achieving asymptotic tracking and disturbance rejection without the knowledge of the system parameters. Insights from reinforcement learning and adaptive dynamic programming are used to solve this problem. An interesting discovery is that the model-free discrete-time output regulation differs from the continuous-time counterpart in terms of the persistent excitation condition required to ensure the uniqueness and convergence of the policy iteration. In this work, we carefully establish the persistent excitation condition to ensure the uniqueness and convergence properties of the policy iteration.