Desired compensation adaptive robust repetitive control of a multi-DoFs industrial robot

Acceleration control in a dual-stage inertial stabilization system using embedded-parallel-based repetitive-peak compensation

Acceleration control demonstrates high sensitivity to nonlinear vibrations in the inertial stabilized motion, usually serving as the preferred choice for observation systems in applications like space optical communications, enabling precise tracking and pointing. However, the stabilization performance is limited by the inadequate gain at mid- to high-frequencies in conventional acceleration control, as well as other nonlinear factors like flexibility and backlash. In this paper, an embedded-parallel-based stabilization methodology is proposed for an acceleration-based dual-stage inertial system to achieve additional notch shaping of the sensitivity function. The essence of this method involves implementing a repetitive controller with a low waterbed effect, paralleled with a peak compensator at a specific frequency to establish an efficient and rapid compensation structure. Relying exclusively on a repetitive controller proves effective in eliminating uncertain periodic vibrations, acting as a substitute for an infinite number of peak compensators. Incorporating an additional mid- to low-frequency peak compensator, designed based on the priori narrowband vibration spectrum, is considered an auxiliary supplement to the repetitive controller. Crucially, this parallel structure neither destabilizes the system nor imposes significant computational burden. Simulations and experiments were conducted at typical vibration frequencies of 1-4 Hz, and experimental results demonstrated a 35%-48% improvement in acceleration stabilization performance, enhancing the system's capability to reject non-strictly periodic vibrations beyond the control bandwidth.

Adaptive RISE-based tracking control of uncertain nonlinear systems: A FAS approach

A fully actuated system (FAS) approach integrated with adaptive robust integral of the sign of the error (ARISE) feedback control strategy is proposed for multi-input multi-output nonlinear systems in the presence of both external disturbances and parametric uncertainties. Owing to an inability to eliminate unmeasured disturbances and model inaccuracies simultaneously, the existing results based on the FAS approaches are typically limited to the uniformly ultimate boundedness of the tracking errors. To achieve the asymptotic tracking performance confronted with parametric uncertainties and time-varying disturbances, an ARISE feedback controller with desired compensation is synthesized to suppress the adverse effects arising from nonlinearity and uncertainty of the system. The improvements compared to the traditional RISE feedback control are attributed to two aspects: (i) the feedback gains in the RISE term are adjustable-online without having to know the prior bounds of disturbances and their time derivatives; (ii) a desired compensation-based adaptive feedforward term, primarily employing the desired trajectories in place of the measured states, could weaken the underlying interaction between the adaptive compensation and robustness part. A rigorous stability analysis is provided to demonstrate that the system state can asymptotically track a bounded desired trajectory in spite of bounded disturbances and parametric uncertainties. Comparative simulations on an under-actuated planar manipulator, possessing an equivalent multi-order FAS model, have been conducted to verify the effectiveness and merits of the developed controller. Experimental validation on a two-wheeled self-balancing robot is also provided to show the feasibility of the proposed approach.

Adaptive-Robust Controller for Smart Exoskeleton Robot

Rehabilitation robotics has seen growing popularity in recent years due to its immense potential for improving the lives of people with disabilities. However, the complex, uncertain dynamics of these systems present significant control challenges, requiring advanced techniques. This paper introduces a novel adaptive control framework integrating modified function approximation (MFAT) and double-integral non-singular terminal sliding mode control (DINTSMC). The goal is to achieve precise tracking performance, high robustness, a fast response, a finite convergence time, reduced chattering, and effective handling of unknown system dynamics. A key feature is the incorporation of a higher-order sliding mode observer, eliminating the need for velocity feedback. This provides a new solution for overcoming the inherent variations and uncertainties in robot manipulators, enabling improved accuracy within fixed convergence times. The efficacy of the proposed approach was validated through simulations and experiments on an exoskeleton robot. The results successfully demonstrated the controller's effectiveness. Stability analysis using Lyapunov theory proved the closed-loop system's uniform ultimate boundedness. This contribution is expected to enable enhanced control for rehabilitation robots and improved patient outcomes.

Model-free kinematic control of redundant manipulators with simultaneous joint-physical-limit and joint-angular-drift handling

Model-free tracking control methods have been developed for redundant robotic manipulators with unknown kinematic or dynamic models. However, most existing works have not considered the problems of joint physical limits and joint angular drift. Joint angular drift refers to the phenomenon that the joint angles of a redundant manipulator are different from their initial state when the end-effector of the manipulator returns to its initial position. Therefore, this article proposes a model-free scheme for the tracking control of redundant manipulators as well as the avoidance of joint physical limits and joint angular drift. The proposed method aims to overcome these challenges by formulating the inverse kinematics problem as a constrained optimization problem, which incorporates the avoidance of joint angular drift into the optimization objective and takes the joint physical limits as a constraint. At the same time, an online Jacobian estimator is designed to observe the state of the manipulator. Specifically, it can estimate the Jacobian matrix of a redundant manipulator in real-time during the operation by fully exploiting the sensory data, without knowing the analytic robot model. Then, the optimization problem is integrated with the Jacobian estimator and solved by a discrete-time algorithm. Theoretical analysis is conducted to prove the stability and convergence of the proposed method. Moreover, the efficacy, practicability and superiority of the proposed method are supported by simulations and experiments based on different redundant manipulators.