Observer-based optimal and robust output regulation for nonlinear systems

Output Feedback Higher-Order Sliding Mode Controller for a Class of Nonlinear Systems

Output Feedback Adaptive Performance Control

In this paper, we consider uncertain high-order nonlinear systems performing dynamic tracking tasks under hard actuator constraints, where only the output error is available for measurement, while the system states and the desired trajectory derivatives are unavailable for feedback. We propose a robust output-feedback controller that guarantees adaptive performance specifications in this framework. The proposed scheme employs a novel Prescribed Performance Observer (PPO) with dynamic gains, which enhances estimation accuracy while avoiding large fixed observer gains. In addition, we introduce an adaptive mechanism that dynamically adjusts the output performance specifications according to the actuator limitations, ensuring bounded closed-loop signals. We establish a separation principle showing that the output-feedback scheme recovers the performance of its state-feedback counterpart. Comparative simulations demonstrate accurate tracking and smoother applied control under actuator limitations, uncertainties, and measurement noise.

Robust Projected Adaptive High-Gain Observer for Feedback Control of Buck Converters: Practical ISS

Observer-Based Finite-Time Adaptive Fuzzy Control for Nonlinear Systems with Full-State Constraints

This paper investigates practical finite-time adaptive fuzzy control for uncertain nonlinear systems subject to full-state constraints and unmeasured state variables. To deal with the inaccessibility of some state variables, an observer is constructed. A state-dependent nonlinear mapping is introduced to ensure that all system states remain within their prescribed bounds. In contrast to typical barrier Lyapunov function (BLF)-based schemes, the presented method manages full-state constraints without imposing any extra prerequisites on virtual control signals. Fuzzy logic systems (FLSs) act as estimators for the unknown nonlinearities emerging in the control law design, while the integration of dynamic surface control techniques helps circumvent the “complexity explosion” characteristic of conventional backstepping approaches. Subsequently, a practical finite-time adaptive fuzzy tracking controller is constructed, which guarantees the semi-global practical finite-time stability of the closed-loop system, with all closed-loop signals remaining bounded for all time and the tracking error converging to a residual set within finite time. Simulation results demonstrate that the tracking error enters a small residual set within finite time and that all system states remain within their prescribed constraints.

Adaptive Compensation and Adaptive Optimal Control of a Single Link Manipulator

This paper presents an online solution to the finite-horizon optimal tracking control problem for continuous-time nonlinear systems with partially unknown dynamics, based on an Adaptive Dynamic Programming (ADP) approach. The method employs a dual-approximation identifier–critic neural network (NN) architecture, with both networks tuned simultaneously during online implementation. The unknown weights of the identifier and critic activation functions are estimated using a filter-based adaptive algorithm, which provides a simple online validation of the persistence of excitation (PE) condition required for convergence of the control parameters. The controller is evaluated in simulation on an ideal single-link robotic manipulator with partially unknown dynamics and is compared against two classical adaptive nonlinear control strategies: an adaptive Lyapunov-based nonlinear (ALN) controller and an adaptive backstepping (ABS) controller. Performance is assessed in terms of adaptive parameter convergence, tracking accuracy, control input smoothness, and tuning complexity. The ADP-based controller demonstrates the most intuitive tuning process, as its parameters are directly linked to observed system behaviour, and achieves superior tracking performance with the smoothest control input among the three controllers.

UDE-Based Adaptive Asymptotic Tracking Control for a Dual-Arm Robot with Input-and-Output Constraints

In this paper, an adaptive control scheme is proposed to tackle the tracking problem of an input-and-output constrained dual-arm robot (DAR) with uncertainties and external disturbance. A time-synchronized stable estimator is designed to compensate for the adverse effect of the system uncertainties and unknown disturbance, and guarantees that estimation error of each dimension can achieve convergence at the same time. Furthermore, an input saturation auxiliary variable and an integral barrier Lyapunov function (iBLF) are utilized to ensure the input and output remain within the pre-specified bounds and normal status of the system can be guaranteed. Meanwhile, considering the estimation performance, trajectory tracking, and constraint handling, an analysis based on the Lyapunov direct method is given to illustrate the asymptotic stability of the DAR tracking system. Finally, numerical simulations are conducted to verify the effectiveness and feasibility of the proposed control scheme.