Design of a decentralized model reference adaptive controller for a mobile robot
Control systems for robotic manipulators have been investigated for several years. The difficulty in designing a controller for a robotic manipulator is due to the highly nonlinear, time-varying dynamics. Closed-loop constant gain controllers are effective when the robot is expected to perform a limited range of operations. In the case of a mobile robot, the commanded tasks are not likely to be repetitive. Thus, another method of control is desired to overcome the effects of the nonlinear time-varying dynamics. Several adaptive control methods have been applied to robotic manipulators. The adaptive controllers are successful at trajectory tracking in the presence of the nonlinear time-varying dynamics. Some of these methods are computationally demanding, therefore, most of the current research focuses on efficient adaptive control methods. In particular, the area of decentralized adaptive control is gaining popularity. This method involves reducing a dynamic system into subsystems, each with an individual controller.
This method is more efficient since the controllers can operate simultaneously. In this study, a decentralized model reference adaptive controller (MRAC) was designed for a four-degree-of-freedom mobile robot. The performance of the decentralized MRAC controller was compared to that of a constant gain state feedback controller. The decentralized MRAC control strategy proved to be an efficient method of control for a mobile robot that is superior to state feedback control when the robot is performing highly nonlinear time-varying tasks. Also, the computational load for each subsystem of the decentralized controller was less than the computational load of the state feedback controller.