PROJECT TITLE :
Optimal Tracking Control of Motion Systems
Tracking management of motion systems usually needs correct nonlinear friction models, particularly at low speeds, and integral action. However, building correct nonlinear friction models is time consuming, friction characteristics dramatically amendment over time, and special care should be taken to avoid windup in an exceedingly controller using integral action. During this paper a replacement approach is proposed for the optimal tracking management of motion systems with vital disturbances, parameter variations, and unmodeled dynamics. The ‘desired’ control signal that can keep the nominal system on the required trajectory is calculated based mostly on the known system dynamics and is utilised in a very performance index to style an optimal controller. But, in the presence of disturbances, parameter variations, and unmodeled dynamics, the specified management signal should be adjusted. This is often accomplished by using neural network based observers to spot these quantities, and update the control signal on-line. This formulation allows for wonderful motion tracking while not the need for the addition of an integral state. The system stability is analyzed and Lyapunov based mostly weight update rules are applied to the neural networks to guarantee the boundedness of the tracking error, disturbance estimation error, and neural network weight errors. Experiments are conducted on the linear axes of a mini CNC machine for the contour management of 2 orthogonal axes, and the results demonstrate the excellent performance of the proposed methodology.
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