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

Abstract

Summary In 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.

Event-triggered boundary quantization control for flexible manipulator based on partial differential equations dynamic model

“…In essence, flexible manipulator is a distributed parameter system, which can be accurately described by partial differential equations (PDEs). Different control schemes have been developed for flexible systems based on PDEs, such as adaptive fault-tolerant control (Zhang et al, 2019b), boundary control (He et al, 2017; Liu et al, 2017; Xing and Liu, 2019b), disturbance observer design (Yang and Liu, 2019), robust adaptive iterative control (Xing and Liu, 2019a). In the design of these controllers, the problem of the capacity of the communication channel during the transmission of the input signal is not considered, and signals need to be delivered from time to time, which will increase the communication burden and even affect the performance of the system.…”

Iterative learning control for multi‐agent systems with noninstantaneous impulsive consensus tracking

“…After more than 30 years of development, ILC has been widely used in the tracking and control of intelligence systems, we can refer to References 2‐9 and the references therein. Recently, ILC has made relevant achievements in modeling a flexible manipulator, 10 self‐learning robust control synthesis and tracking design of general uncertain dynamical systems, 11 self‐learning optimal regulation for event‐driven discrete nonlinear systems, 12 and etc.…”

Iterative learning control with discrete‐time nonlinear nonminimum phase models via stable inversion

“…Compared to real-time-feedback and/or feedforward control techniques, many case studies of ILC have shown a substantial reduction in tracking error. Relevant applications include robotassisted stroke rehabilitation 1 , high speed train control 2 , laser additive manufacturing 3 , and vehicle-mounted manipulators 4 , all of which use nonlinear models. In fact, while the majority of ILC literature focuses on linear systems, the prevalence of nonlinear dynamics in real-world systems has motivated the development of numerous ILC theories for discrete-time nonlinear models [5][6][7][8][9][10] .…”