NEURAL-NETWORK SLIDING MODE ROBOT CONTROL

This paper develops a method for neural network control design with sl iding modes in which robustness is inherent. Neural network control is formulated to become a class of variable structure (VSS) control. Sli ding modes are used to determine best values for parameters in neural network learning rules, thereby robustness in learning control can be improved. A switching manifold is prescribed and the phase trajectory is demanded to satisfy both, the reaching condition and the sliding co ndition for sliding modes. A major objective of the work described has been to develop neural network architectures which will provide fast and robust on-line learning of the dynamic relations required by robot controller at the executive hierarchical level. The approach to propo sed robot control involves using a neural network feedforward loop tog ether with a discrete time 'chattering-free' feedback loop. Such a use of the neural network with a sliding mode learning algorithm is consi dered to be a new approach to adaptive control of a non-linear robot s ystem. The advantage of the proposed control scheme prevails over thos e conventional model based control scheme since no precise knowledge o f mathematical model is necessary. The algorithm was verified by exper iments where inverted pendulum with the additional mass-spring-damper load was used.