Composite Adaptive Control Research Articles

Composite adaptive neural control for automatic carrier landing system with input saturation and output constraints

This paper investigates the automatic carrier landing control problem in the presence of model uncertainty, airwake disturbances, input saturation, and output constraints. Considering the performance requirements of the carrier-based aircraft, a composite adaptive neural controller is proposed based on the time-varying barrier Lyapunov function and backstepping control techniques. The radial basis function neural network is used to approximate the model uncertainty, where the neural network weight update law incorporating prediction and tracking errors further improves the convergence rate of the neural network and mitigates high-frequency oscillations. Furthermore, an adaptive disturbance compensation model is established to mitigate the adverse effects of airwake disturbances and estimation errors in the neural network. Based on the Lyapunov stability theory, it is proven that the proposed controller maintains the aircraft trajectory within the prescribed constraints and also ensures that all signals in the closed-loop control system are semiglobally uniformly ultimately bounded. Finally, comparative simulations are performed to demonstrate the effectiveness and superiority of the proposed composite adaptive neural control method.

Composite Learning Adaptive Intelligent Self-Triggered Fault-Tolerant Control With Improved Performance Assurance for Autonomous Surface Vehicle

Composite Learning Adaptive Intelligent Self-Triggered Fault-Tolerant Control With Improved Performance Assurance for Autonomous Surface Vehicle

Homogeneous domination-based composite adaptive control strategy for perturbed chain of integrators with mismatched disturbances

Homogeneous domination-based composite adaptive control strategy for perturbed chain of integrators with mismatched disturbances

Neural network-based adaptive optimal tracking control for hypersonic morphing aircraft with appointed-time prescribed performance

In this paper, an adaptive optimal attitude tracking controller for hypersonic morphing aircraft with appointed-time prescribed performance is presented. A proper error transformation methodology is developed to ensure the performance constraints are met without knowing initial tracking conditions. Subsequently, an adaptive composite control scheme is devised for the transformed system, comprising a feedforward neuro-backstepping controller and a feedback optimal controller. The former is employed to convert the optimal tracking problem into an equivalent optimal regulation problem, while the latter is constructed using adaptive dynamic programming (ADP) with a single critic network to achieve optimality. A novel weight-tuning law for the critic network is introduced, incorporating a stability indicator and the concurrent learning technique. This approach relaxes the underlying restrictive conditions in ADP, improving the robustness and availability of the system. The stability of the closed-loop system is analyzed using the Lyapunov direct method. Finally, the effectiveness and improved performance of the proposed control scheme are validated through numerical simulations.

Fixed-Time Dynamic Surface-Based Adaptive Composite Control for Nonlinear Systems and Its Application to the RLC Circuit

Fixed-Time Dynamic Surface-Based Adaptive Composite Control for Nonlinear Systems and Its Application to the RLC Circuit

Composite adaptive exponential tracking control for large-scale nonlinear systems with sensor faults

The issue of composite adaptive exponential tracking control for a class of large-scale nonlinear systems under model uncertainties, external disturbances, along with multiplicative and additive time-varying sensor faults is considered in this paper. The command filtered backstepping approach is employed to address the “explosion of terms” issue inherent in standard backstepping method. A novel compensating system is incorporated to mitigate the effects of filtering errors and enhance the convergence of tracking errors. The composite estimation laws are designed by integrating the compensated tracking errors, prediction errors stemming from output estimators, along with the proportional and integral estimation errors of faulty terms. This on-line estimation framework enables achieving fast, robust and accurate estimation, even when employing low learning and modification gains. By incorporating modification terms with appropriate time-varying gains, it is demonstrated that the resulting system is globally exponentially stable. Finally, the effectiveness of the presented FTC approach is illustrated through two simulation examples.

Adaptive composite learning control of a flexible two‐link manipulator with unknown spatiotemporally varying disturbance

AbstractThis article presents a novel adaptive composite learning (ACL) control strategy combining reinforcement learning and a disturbance observer (DOB) to address vibration issues in a flexible two‐link manipulator (FTLM) system affected by unknown spatiotemporally varying disturbances. Based on the assumed mode method, the FTLM system is initially transformed into an ordinary differential equation model, while effectively capturing the elastic deformation and vibration characteristics of the flexible link. A composite learning controller, based on the actor‐critic algorithm and DOB, is then developed to achieve trajectory tracking and vibration suppression in the FTLM system. The DOB in the controller compensates for unknown disturbances resulting in reduced system error. It is noting that the proposed optimal control strategy is continuously gathering system experience and evaluating the current policy's effectiveness. The stability and robustness of the closed‐loop system incorporating the composite controller are analyzed using Lyapunov's direct method, and the semi‐global uniform ultimate boundedness of the tracking and vibration errors are also demonstrated. To validate the effectiveness and superiority of the proposed ACL controller, comparative simulations and experiments are conducted on the Quanser experimental platform.

Composite adaptive fuzzy bipartite consensus of fractional-order multiagent systems with a switched event-triggered mechanism

This paper focuses on online recorded-data-based composite adaptive fuzzy bipartite consensus control for uncertain fractional-order multiagent systems with interconnected terms and external disturbances by employing a switched-threshold-based event-triggered mechanism (ETM) under the backstepping structure. Fuzzy logic system is used as a universal function approximation to deal with function uncertainties that are not prone to model in the system. A new composite learning adaptive parameter design scheme that synthesizes both prediction error and tracking error is developed to enhance the tracking performance, where the prediction error is raised from the utilization of online recorded data and instantaneous data. A unique switched-threshold-based ETM is introduced, in which the information transmission between the sensor and the controller is imposed on one of the individuals. One merit of this work consists in that it can automatically and rapidly switch and adjust between the fixed threshold and relative threshold ETM according to the amplitude of input signals to balance the network resources and impede the occurrence of pulse phenomenon. In addition, it is theoretically proven that the proposed scheme can ensure that all internal signals of the closed-loop system are bounded and achieve local bipartite consistent errors through the fractional Lyapunov stability criterion. Finally, a numerical example is provided to confirm the feasibility of the proposed approach.

Composite Learning Adaptive Tracking Control for Full-State Constrained Multiagent Systems Without Using the Feasibility Condition.

This article proposes a distributed consensus tracking controller for a class of nonlinear multiagent systems under a directed graph, in which all agents are subject to time-varying asymmetric full-state constraints, internal uncertainties, and external disturbances. The feasibility condition generally required in the existing constrained control is removed by using the proposed nonlinear mapping function (NMF)-based state reconstruction technology, and the Lipschitz condition usually needed in the consensus tracking is also canceled based on the adaptive command-filtered backstepping framework. The composite learning of the neural network-based function approximator (NN-FAP) and the finite-time smooth disturbance observer (DOB) provides a novel scheme for handling internal and external uncertainties simultaneously. One advantage of this scheme is that the use of online historical data of the closed-loop system strengthens the excitation of NN's learning. Another advantage is that the DOB with NN-FAP embedding realizes that the finite-time observation for external disturbance in the case of the system dynamics is unknown. A complete controller design, sufficient stability analysis, and numerical simulation are provided.

Adaptive Composite Fixed-time RL Optimized Control for Nonlinear Systems and Its Application to Intelligent Ship Autopilot

Adaptive Composite Fixed-time RL Optimized Control for Nonlinear Systems and Its Application to Intelligent Ship Autopilot

Adaptive Composite Observer-Based Global Finite Time Control With Prescribed Performance for Robots

Adaptive Composite Observer-Based Global Finite Time Control With Prescribed Performance for Robots

Predefined-Time Composite Adaptive Fuzzy Nonsingular Attitude Control for Multi-UAVs

Predefined-Time Composite Adaptive Fuzzy Nonsingular Attitude Control for Multi-UAVs

Collective Initial Excitation-Based Distributed Composite Adaptive Coverage Control With Application to a Network of Drones

Collective Initial Excitation-Based Distributed Composite Adaptive Coverage Control With Application to a Network of Drones

Guest Editorial to the Special Section on Composite Adaptive and Learning Control With Robotic Applications

Guest Editorial to the Special Section on Composite Adaptive and Learning Control With Robotic Applications

Composite Adaptive Control for Time-varying Systems with Dual Adaptation

Composite Adaptive Control for Time-varying Systems with Dual Adaptation

Composite adaptive control of submarine and parameter identification by integral regressor excitation

In this article, we develop a novel composite adaptive control system for the dive-plane maneuvers and parameter identification of a multi-input multi-output submarine model. The composite adaptive control system consists of (1) an input–output feedback linearizing control law and (2) an identifier formed by gradient algorithm-based two-parameter estimation laws. Unlike the existing adaptive systems for autonomous underwater vehicles and submarines, a novelty of this composite identifier lies in the use of regressor matrix integral feedback for enhancement in parameter excitation. By the Lyapunov analysis, asymptotic convergence of the depth and pitch angle tracking errors is established. Simulation results show precise depth and pitch angle control, estimation of all model parameters, and robustness to random and sinusoidal disturbance inputs.

Fixed-time command-filtered composite adaptive neural fault-tolerant control for strict-feedback nonlinear systems

The research investigates the fixed-time command-filtered composite adaptive neural fault-tolerant (FCCANF) control issue of strict-feedback nonlinear systems (SFNSs). There exist unknown functions and bounded disturbances in the considered systems. Radial basis function neural networks (RBFNNs) will be used in the estimate of the unknown functions. By the serial–parallel estimation models (SPEMs), the forecast biases and the track biases can change the weights of RBFNNs and the approximate characteristics of RBFNNs will be improved. Then, utilizing the novel fixed-time command filter and adaptive disturbance observers, the issue of complex explosion will be effectively solved and the external disturbance is effectively compensated. Subsequently, by utilizing the adaptive control technique, a novel FCCANF controller is developed. Additionally, we have that the system internal variables are bounded and the output variable inclines to a little interval around zero in fixed time which is not determined by the system initial variables. Eventually, numerical and practical examples are shown to prove the availability of the obtained control technique.

Locally Expanded Constraint-Boundary-Based Adaptive Composite Control of a Constrained Nonlinear System With Time-Varying Actuator Fault

Locally Expanded Constraint-Boundary-Based Adaptive Composite Control of a Constrained Nonlinear System With Time-Varying Actuator Fault

Modeling and composite adaptive neural fault-tolerant tracking control for tailless aircraft

Modeling and composite adaptive neural fault-tolerant tracking control for tailless aircraft

Fast and Smooth Composite Local Learning-Based Adaptive Control.

Model structure representation and fast estimation of perturbations are two key research aspects in adaptive control. This work proposes a composite local learning adaptive control framework, which possesses fast and flexible approximation to system uncertainties and meanwhile smoothens control inputs. Local learning, which is a nonparametric regression approach, is able to automatically adjust the structure of approximator based on data distribution from the local region, but it is sensitive to the outliers and measurement noises. To tackle this problem, the regression filter technique is employed to attenuate the adverse effect of noises by smoothing the output response and state features. In addition, the stable integral adaptation is integrated into local learning framework to further enhance the system robustness and smoothness of the estimation. Through the online elimination of uncertainties, the nominal control performance is recovered when the plant encounters violent perturbations. Stability analysis and numerical simulations are performed to demonstrate the effectiveness and benefits of the proposed control method. The proposed approach exhibits a promising performance in terms of rapid perturbation elimination and accurate tracking control.