Abstract
Problems of trajectory tracking for a class of free-floating robot manipulators with uncertainties are considered. Two neural network controls are designed. The first scheme consists of a PD feedback and a dynamic compensator which is an RBF neural network controller. The second scheme syncretizes neural networks with variable structures using a saturation function. Neutral networks are used to adaptively learn about and compensate for the unknown system. Approach errors are eliminated as disturbances by using the variable structure controller. The shortcomings of local networks are considered. The control is based on dividing aspects into three sections with classification and integration: state dimensional, neural network and variable structure separate control. When invalidations of the neutral network appeared, the controller was able to guarantee good robustness as well as the stability of the closed-loop system. The simulation results show that the methods presented are effective.
Highlights
Considering the dangers and the economy of the space environment, space robots should be designed to assist or replace astronauts in order to complete a large number of arduous, risky tasks [1,2,3]
A new adaptive tracking control law for uncertain robots based on neural networks should be designed as τ =τ1+K pe+Kd e+τ NN +Δτ where τ NN is a neural network controller, Δτ is the variable structure compensator which is designed to eliminate the effects of the network approximation error
If variable structure control can be utilized to compensate for the nonlinear error outside the approximation region, the controller can improve the control precision, and can still ensure the system has good robustness under conditions of neural network invalidation
Summary
Considering the dangers and the economy of the space environment, space robots should be designed to assist or replace astronauts in order to complete a large number of arduous, risky tasks [1,2,3]. Newton [20] proposed a neural network control strategy, which successfully implement the proposed feed-forward dynamic model by learning and training, and obtained an adaptive control of a space robot. In the initial stages of the control and the outside approximation region of the neural network, this new controller, compensating the control by implementing a variable structure with good robustness, improves the dynamic responses of the system. This controller can act as a compensator, overcoming the chattering of the variable structure and improve control precision This integrated controller can speed up the convergence velocity of the tracking error and ensure that the system has good robustness in case of a failure in the neural network. The adoption of the PD feedback strategy makes the scheme presented by this paper easier to implement
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