Disturbance Observer Based Tracking Control

A disturbance observer based tracking control algorithm is presented in this paper. The key idea of the proposed method is that the plant nonlinearities and parameter variations can be lumped into a disturbance term. The lumped disturbance signal is estimated based on a plant dynamic observer. A state observer then corrects the disturbance estimation in a two-step design. First, a Lyapunov-based feedback estimation law is used. The estimation is then improved by using a feedforward correction term. The control of a telescopic robot arm is used as an example system for the proposed algorithm. Simulation results comparing the proposed algorithm against a standard adaptive control scheme and a sliding mode control algorithm show that the proposed scheme achieves superior performance, especially when large external disturbances are present. @S0022-0434~00!00802-9# Tracking control for uncertain nonlinear systems with unknown disturbances is a challenging problem. To achieve good tracking under uncertainties, one usually needs to combine several or all of the following three mechanisms in the control design: adaptation, feedforward ~plant-inversion!, and high-gain, this paper is no exception. The tracking control of nonlinear systems under plant uncertainties and exogenous disturbances is studied in this paper. However, we will focus on the robotic examples for both literature review and numerical simulations. Many adaptive control schemes for robotic manipulators assume that the structure of the manipulator dynamics is known and/or the unknown parameters influence the system dynamics in an affine manner @1‐5#. There are several inherent difficulties associated with these approaches. First of all, the plant dynamic structure may not be known exactly. Second, it was demonstrated @6,7# that some of these designs may lack robustness against uncertainties. Recently, adaptive control algorithms requiring less model information were proposed @8‐11#. These algorithms adjust the control gains based on the system performance and thus are commonly referred to as performance-based adaptive control. These algorithms require little knowledge of system structures and parameter values. However, the control signal might become quite large. Plant-inversion based methods ~e.g., I/O linearization, backstepping!, roughly speaking, focus on the canceling of unwanted nonlinear dynamics. High-gain approaches such as sliding model controls could guarantee stability but, again, sometimes require very large control signals. While in some cases this may be a viable approach, in many other applications it may not be the best solution. In this paper, a disturbance-estimation based tracking control method is presented. Disturbance observer based control algorithms first appeared in the late 1980s @12#. Since then, they have been applied to many applications @13‐15#. Recently, the H‘ technique has been applied for the design of an optimal disturbance observer @16#. In this paper, we focus on the design for nonlinear systems. The magnitude of the disturbance is estimated based on the state estimation error in a two-step design. The estimated disturbance can then be used to improve the performance of literally any control algorithms. In this paper, a simple computed torque method is selected. The performance of the disturbanceobserver-enhanced method is then compared against those of a simple adaptive control and a simple robust control algorithm.