Adaptive RISE control for asymptotic rigid-body attitude tracking with additive disturbances

Composite RISE control for vehicle-mounted servo system with unknown modeling uncertainties and unknown time-varying disturbances

In light of the problem of trajectory tracking control in vehicle servo systems with system model uncertainty and external time-varying disturbance, an effective trajectory tracking control method that can handle system model uncertainty and external time-varying disturbances is proposed. To achieve this goal, a novel composite robust integral of the sign of the error (RISE) control method is introduced that combines a multi-layer neural network and an extended state observer. Specifically, multi-layer neural networks are utilized to approximate the uncertainty of the system model, while an extended state observer is employed to estimate the fitting near-error and the external time-varying interference, which are used as feedforward compensation. Finally, the RISE controller is implemented as a robust feedback controller. By applying Lyapunov theory for stability analysis and conducting experiments, the results demonstrate that the proposed approach exhibits excellent performance and robustness in addressing the uncertainties and disturbances involved in trajectory tracking control for vehicle servo systems.

Output-feedback asymptotic tracking control for rigid-body attitude via adaptive neural backstepping

In this note, a novel neural adaptive output-feedback control (NAOC) with asymptotic tracking performance for rigid-body attitude is investigated subject to inertia uncertainty, unavailability of the angular velocity and unknown external disturbance. First, by virtue of the combination of the first-order filter and the coordinate transformation, the original output feedback system with immeasurable angular velocity and unknown dynamics is converted into the full-state strict feedback system with mismatched disturbance. Second, aided by infinitely integrable inequality with saturation function, an innovative neural network (NN) based adaptive control scheme is proposed via backstepping technique. By adopting the model transformation and proposed algorithm, the asymptotic tracking performance of the transformed system and the attitude tracking system without angular velocity can be achieved simultaneously. Finally, comparative numerical simulations illustrate the efficacy of the developed algorithm.

RISE-based adaptive tracking control for Euler-Lagrange mechanical systems with matched disturbances

The core idea of traditional adaptive control is to reconstruct parameter estimation errors with known signals and damping injection based on tracking error, while the formulation of desired damping in controller designs is usually a nontrivial task. The main contribution of this paper lies in the development of the classic RISE result and a constructive damping injection procedure for the adaptive tracking control of Euler-Lagrange mechanical systems. By utilizing generalized dynamic scaling function, scalar filtering, the improved RISE method and analyzing the existence of finite escape time of the closed-loop system, a globally asymptotically stable result is obtained with facilitative damping injection, significant order reduction and improved design efficiency when compared with the existing results. Simulations on a fully actuated 2-DOF planar robot manipulator model demonstrate the effectiveness of the proposed methods.