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This paper presents a new approach for the design of variable structure control (VSC) of nonlinear systems. The approach is based on estimation of joint acceleration signals with introduction of load estimation with the asymptotic observer. The control system is insensitive to parameter variations for a chosen switching hypersurface in conditions when it is reached by the dynamic motion with the required dynamics. The parameter insensitive response provided by this control method is demonstrated on the model of the SCARA robot. Simulation results confirm the validity of accurate tracking capability and the robust performance.
In this work another perturbation estimation sliding
mode based control algorithm is introduced for a class of
robotic systems in the presence of structured and unstructured uncertainties
and external disturbances. The effects of these uncertainties are combined
into a single quantity. A full order device with the
actuator voltages as control inputs is assumed in control design.
The decentralized control scheme with only a partial state feedback
is applied. A modification of the switching functions with perturbation
estimation is introduced. The salient features of this approach is
that the perturbations are effectively treated by a computationally straightforward
procedure. The proposed controller is applied to a minimal configuration
direct drive robot mechanism.
This paper develops a method for neural network control design with sliding modes in which robustness is inherent. Neural network control is formulated to become a class of variable structure (VSS) control. Sliding 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 condition for sliding modes.
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