Adaptive iterative learning control of robotic system based on particle swarm optimization

In this paper, an adaptive iterative learning control algorithm for robotic manipulators is proposed. A simplified robot manipulator model with 3 degrees of freedom is used as control object for verification purposes. The mathematical model is obtained via Rodriguez approach for modeling differential equations of motion for multi-body systems. The model itself is a simple open-chain kinematic structure. The proposed control system design consists of two layers of controllers. In the inner loop, feedback linearization is applied to deal with the model nonlinearities. Post feedback linearization advanced iterative learning control (ILC) algorithm of sign-D (signum-Derivative) type is introduced as feed-forward compensation with classical PD (Proportional-Derivative) controller in feedback closed loop. A particle swarm optimization (PSO) algorithm is used to optimize ILC gain parameters while gains for PD controller are set by trial and error. Suitable cost function based on position error is chosen for PSO algorithm in order to ensure convergence. Numerical simulation is carried out in two cases – case with constant learning gains and case with PSO optimized learning gains. It is observed that the proposed control law converges to some steady-state error value in both cases

Hybrid iterative learning control of a flexible manipulator

This paper presents an investigation into the development of a hybrid control scheme with iterative learning for input tracking and end-point vibration suppression of a flexible manipulator system. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller using hub angle and hub velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate a non-collocated proportional-integral-derivative (PID) controller with iterative learning for control of vibration of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid iterative learning control scheme is assessed in terms of input tracking and level of vibration reduction in comparison to a conventionally designed PD-PID control scheme. The effectiveness of the control scheme in handling various payloads is also studied

Multi-Parametric Extremum Seeking-based Auto-Tuning for Robust Input-Output Linearization Control

We study in this paper the problem of iterative feedback gains tuning for a class of nonlinear systems. We consider Input-Output linearizable nonlinear systems with additive uncertainties. We first design a nominal Input-Output linearization-based controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model-free multi-parametric extremum seeking (MES) control to iteratively auto-tune the feedback gains. We analyze the stability of the whole controller, i.e. robust nonlinear controller plus model-free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example.Comment: To appear at the IEEE CDC 201