Robust Adaptive Iterative Learning Control Research Articles

Robust Adaptive Iterative Learning Control for a Generic Class of Uncertain Non-Square MIMO Systems

Robust Adaptive Iterative Learning Control for a Generic Class of Uncertain Non-Square MIMO Systems

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system.

A robust model-free adaptive iterative learning control (R-MFAILC) algorithm is proposed in this work to address the issue of laterally controlling an autonomous bus. First, according to the periodic repetitive working characteristics of autonomous buses, a novel dynamic linearized method used in the iterative domain is utilized, and a time-varying data model with a pseudo gradient (PG) is given. Then, the R-MFAILC controller is designed with a proposed adaptive attenuation factor. The proposed algorithm's advantage lies in the R-MFAILC controller, which solely utilizes the input and output data of the regulated entity. Moreover, the R-MFAILC controller has strong robustness and can handle the nonlinear measurement disturbances of the system. In simulations based on the Truck-Sim simulation platform, the effectiveness of the proposed algorithm is verified. A rigorous mathematical analysis is employed to demonstrate the stability and convergence of the proposed algorithm.

Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis.

Robust adaptive iterative learning control for nonrepetitive systems with iteration-varying parameters and initial state

This paper explores how to construct an adaptive iteration learning control (AILC) mechanism for a class of discrete-time nonrepetitive systems subject to iteration-varying unknown parameters and unidentical initial condition. Firstly, for the linear discrete-time nonrepetitive systems, by minimizing the discrepancy of the real system outputs from the estimated system outputs, a gradient-type adaptation law is designed to estimate the system lower triangular parameter matrix and the system initial state. Especially, the current parametric estimation is updated by virtue of the input-output data and the previous estimation. Secondly, an AILC mechanism is constructed based on the estimated system lower triangular parameter matrix, where the control input algorithm and the adaptation law are scheduled in an interactive mode. Thirdly, the boundedness of the estimation error between the real system matrix and the estimation one is derived by means of vector norm theory. Based on the boundedness of the estimation error, the robust condition of the proposed AILC is given. Finally, the proposed AILC is investigated for a class of nonlinear affine systems and the corresponding results are captured. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.

Robust Adaptive Iterative Learning Control for High-Precision Attitude Tracking of Spacecraft

AbstractIn this paper, a robust adaptive iterative learning control (ILC) scheme is developed for the high-precision attitude tracking control of spacecraft in the presence of parametric uncertaint...

Robust Model-Free Adaptive Iterative Learning Control for Vibration Suppression Based on Evidential Reasoning.

Through combining P-type iterative learning (IL) control, model-free adaptive (MFA) control and sliding mode (SM) control, a robust model-free adaptive iterative learning (MFA-IL) control approach is presented for the active vibration control of piezoelectric smart structures. Considering the uncertainty of the interaction among actuators in the learning control process, MFA control is adopted to adaptively adjust the learning gain of the P-type IL control in order to improve the convergence speed of feedback gain. In order to enhance the robustness of the system and achieve fast response for error tracking, the SM control is integrated with the MFA control to design the appropriate learning gain. Real-time feedback gains which are extracted from controllers construct the basic probability functions (BPFs). The evidence theory is adopted to the design and experimental investigations on a piezoelectric smart cantilever plate are performed to validate the proposed control algorithm. The results demonstrate that the robust MFA-IL control presents a faster learning speed, higher robustness and better control performance in vibration suppression when compared with the P-type IL control.

Modeling and robust adaptive iterative learning control of a vehicle‐based flexible manipulator with uncertainties

SummaryIn this brief, this paper deals with a robust adaptive iterative learning control (ILC) problem for a flexible manipulator attached to a moving vehicle with uncertainties. To begin with, considering the infinite dimensionality of the flexible distributed parameter system, a coupled ordinary differential equation and partial differential equation model is established without any discretization. Then, it is followed by a presentation of an adaptive ILC strategy, which can drive the vehicle and joint to the desired positions based on a proportional‐derivative feedback structure with unmodeled dynamics and external disturbances. The deformation of the flexible manipulator can also be suppressed simultaneously under the proposed control laws. By using Lyapunov's direct method, the stability of the closed‐loop system is demonstrated. The simulation results are provided to illustrate the effectiveness of the proposed control laws.

A Robust Adaptive Iterative Learning Control for Trajectory Tracking of Permanent-Magnet Spherical Actuator

This paper presents a robust adaptive iterative learning control (ILC) algorithm for 3-DOF permanent magnet (PM) spherical actuators to improve their trajectory tracking performance. The dynamic model of a PM spherical actuator is a multivariable nonlinear system with interaxis coupling terms. Uncertainties such as modeling errors, loads, and external disturbances exist in the model unavoidably, which will affect the performance, including the precision of the control system. Hence, to compensate for these uncertainties, a new hybrid control scheme that consists of a proportional–derivative (PD) feedback control with varying gains, a PD-type ILC with adjustable gains, and a robust term is developed. The new control law combines the advantages of simplicity and easy design of the PD control, the effectiveness of the ILC to handle model uncertainties and repetitive disturbances, and the robustness of the robust term to random disturbances. In addition, to expedite the convergence rate, the gains in the PD feedback control and the PD-type ILC are adaptively adjusted according to the iteration times. It is shown that the system tracking error approaches zero as the number of iterations increases. To demonstrate the proposed algorithm and verify the established theoretical results, both simulations and experiments are conducted. The results have shown that the proposed control algorithm can effectively compensate for various uncertainties and can thus improve the trajectory tracking performance of spherical actuators.

Robust adaptive iterative learning control for discrete‐time nonlinear systems with both parametric and nonparametric uncertainties

SummaryA new robust adaptive iterative learning control approach is proposed for discrete‐time nonlinear systems with both parametric and nonparametric uncertainties. By virtue of a well‐designed dead‐zone function, the learning of the parametric and nonparametric uncertainties can be performed concurrently. Rigorous Lyapunov function‐based analysis ensures that the effect of system uncertainties can be fully compensated, and the tracking error will converge to zero asymptotically in the iteration domain, even under random initial conditions and iteration‐varying reference trajectories. The efficacy of the proposed controller is demonstrated by simulating a single‐link robot manipulator with unknown frictions. Copyright © 2015 John Wiley & Sons, Ltd.

ON ADAPTIVE ITERATIVE LEARNING CONTROL ALGORITHM FOR DISCRETE-TIME SYSTEMS WITH PARAMETRIC UNCERTAINTIES SUBJECT TO SECOND-ORDER INTERNAL MODEL

The adaptive iterative learning control methods is used to control discrete-time systems with time-iteration-varying uncertainties. By exploiting second-order internal model, the time-iteration varying law of uncertainties are derived. A robust adaptive iterative learning control approach is presented for discrete-time systems with exogenous disturbance. The tracking error of the designed adaptive iterative learning control law converges to zero asymptotically as iteration number approaches to infinity even if reference trajectories varying from iteration to iteration. Numerical example is given to validate the adaptive iterative learning control technique.

Adaptive Iterative Learning Control for a Class of Uncertain Nonlinear Systems with Second-Order Sliding Mode Technique

This paper addresses the problem of robust adaptive iterative learning control for a chain of uncertain integral nonlinear systems, whose aim is to stabilize the tracking error of the system and improve convergence speed in the presence of uncertainties. In response to unknown bounded disturbances, a continuous second-order sliding mode adaptive iterative learning control scheme is proposed, in which an integral term is to attenuate the effects of the disturbances and achieve fast convergence performance. By designing a suitable controller and composite energy function, it is proved that the tracking error along iterative learning horizon will converge to a small neighborhood of zero. Numerical examples are provided to validate the efficacy of the proposed method.

Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

Dynamic Modeling and Robust-adaptive Iterative Learning Control of 3-RRR Spherical Parallel Mechanism

A dynamic model based on attitude parameters of moving platform for a 3-RRR spherical parallel robot is developed by using Lagrange method. Further,a dynamic model with parameter uncertainties is developed considering the parameters uncertainties caused by tiny changes such as approximately computing in linear density,thickness,and mechanism abrasion during working process. Aiming at its characteristic of acting repeatedly in vibration measuring and parameter uncertainties,a robust-adaptive iterative learning controller (ILC) is designed for this mechanism,and its stability is proved. The adaptive algorithm,which is powerful in leaning unknown constants,is used to compensate the uncertain parts of dynamics model,and the iterative learning algorithm is used to track the desired path without errors. Because of the utilization of certain information of dynamics model,the Lipschitz condition necessary for most unknown systems during iterative learning control is avoidable. The simulation results indicate that this robot can accurately track the desired path repeatedly.