Design and implementation of an adaptive recurrent neural networks (ARNN) controller of the pneumatic artificial muscle (PAM) manipulator

This paper presents the design, development and implementation of an adaptive recurrent neural networks (ARNN) controller suitable for real-time manipulator control applications. The unique feature of the ARNN controller is that it has dynamic self-organizing structure, fast learning speed, good generalization and flexibility in learning. The proposed adaptive algorithm focuses on fast and efficient optimization by weighting parameters of inverse recurrent neural models used in the ARNN controller. This approach is employed to implement the ARNN controller with a view to controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time. The performance of this novel proposed controller was found to be superior compared with a conventional PID controller. These results can be applied to control other highly nonlinear systems as well.