Design of a PD Controller Combined with a MRAS-based LFFC for a Two – Link Robot Manipulator

In this paper we introduce a new control structure which consists of a PD controller and a Model Reference Adaptive System-based Learning Feed-Forward Controller to a two-link robot manipulator. The proposed approach seeks to benefit the multi-variable control of multi-input multi-output systems with variable parameters, nonlinear behavior, and significant coupling in the system dynamics. Conventionally, the function approximators with “standard” neural networks were used which enabled to approximate complex non-linear functions. The idea of LFFC is applied but without using the complex neural networks. Instead, we propose to use MRAS-based adaptive components. The well-known Lyapunov approach is used to find stable adaptive laws for the feed-forward parameters in such way that learning converges. The effectiveness of the proposed approach is demonstrated by simulation analysis.