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Radial basis functional link network and Hamilton Jacobi Issacs for force/position control in robotic manipulation. (English) Zbl 1264.93159
Summary: This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional (RBF) neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an external interference of the system, and it is eliminated by the robust control method. Since the conventionally fixed structure of RBF network is not optimal, resource allocating network (RAN) is proposed in this paper to adjust the network structure in time and avoid the underfit. Finally the advantage of system stability and transient performance is demonstrated by the numerical simulations.

MSC:
93C85 Automated systems (robots, etc.) in control theory
70E60 Robot dynamics and control of rigid bodies
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