Robust Control of Robot Arms via Quasi Sliding Modes and Neural Networks

Abstract

This chapter presents a control approach for robotic manipulators based on a discrete-time sliding mode control which has received much less coverage in the literature with respect to continuous time sliding-mode strategies. This is due to its major drawback, consisting in the presence of a sector, of width depending on the available bound on system uncertainties, where robustness is lost because the sliding mode condition cannot be exactly imposed. For this reason, only ultimate boundedness of trajectories can be guaranteed, and the larger the uncertainties affecting the system are, the wider is the bound on trajectories which can be guaranteed. As a possible solution to this problem, in this chapter a discontinuous control law has been proposed, employing a controller inside the sector based on an estimation, as accurate as possible, of the overall effect of uncertainties affecting the system. Different solutions for obtaining this estimate have been considered and the achievable performances have be compared using experimental data. The first approach consists in estimating the uncertain terms by a well established method which is an adaptive on-line procedure for autoregressive modeling of non-stationary multivariable time series by means of a Kalman filtering. In the second solution, radial basis neural networks are used to perform the estimation of the uncertainties affecting the system. The proposed control system is evaluated on the ERICC robot arm. Experimental evidence shows satisfactory trajectory tracking performances and noticeable robustness in the presence of model inaccuracies and payload perturbations.

Sliding Mode Robust Active Disturbance Rejection Control for Single-Link Flexible Arm with Large Payload Variations

“…Refs. [6,7] are examples of using adaptive control to conquer model uncertainty produced by tip load variations. However, the deterioration of the tip transient response during the time from the start of motion to the estimation of the robot parameters and the return of the controller results in potentially unacceptable tracking errors.…”

Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

“…To overcome the tracking control problem of robot manipulators, various control strategies have been applied, as shown in the literature. [1][2][3][4][5][6][7][8][9][10] The adaptive control method has been widely used because it can continuously modify its control behavior according to the complex characteristics of the controlled object, environmental interference, and modeling errors. Thereby, it is easy to achieve the satisfied control performances by using adaptive control approach.…”

Navigation of Several Links Robot Arm based on an Intelligent System

“…Ranjbar et al, [10] presented an original solution and analytical comparison to path planning for manipulator arms. Corradini et al, [11] proposed control approach was based on a discrete-time sliding mode control where radial basis function neural networks were used to learn about uncertainties affecting the system. Dongbing Gu, et al, [12] presented a new path-tracking scheme for a mobile robot based on neural Predictive control, A multi-layer back propagation neural network to model non-linear kinematics of the robot estimator in order to adapt the robot to a big operating range.…”