Abstract
A B-spline neural networks-based adaptive control technique for angular speed reference trajectory tracking tasks with highly efficient performance for direct current shunt motors is proposed. A methodology for adaptive control and its proper training procedure are introduced. This algorithm sets the control signal without using a detailed mathematical model nor exact values of the parameters of the nonlinear dynamic system. The proposed robust adaptive tracking control scheme only requires measurements of the velocity output signal. Thus, real-time measurements or estimations of acceleration, current and disturbance signals are avoided. Experimental results confirm the efficient and robust performance of the proposed control approach for highly demanding motor operation conditions exposed to variable-speed reference trajectories and completely unknown load torque. Hence, laboratory experimental tests on a direct current shunt motor prove the viability of the proposed adaptive output feedback trajectory tracking control approach.
Highlights
Electrical machines are part of a wide variety of applications for use from domestic and industrial to remote research applications on land, air, water, and in space, each one with its own characteristics and specific protections [1]
The direct current (DC) shunt motor configuration discussed in this paper considers its nonlinear dynamics, which makes the design of some robust speed control scheme a non-simple task [12]
This paper presents the design and performance assessment of an adaptive tracking control approach based on B-spline neural networks with the capabilities required for real-time applications of DC shunt motors, without requiring a detailed mathematical model and values of the parameters of the nonlinear dynamic system
Summary
Electrical machines are part of a wide variety of applications for use from domestic and industrial to remote research applications on land, air, water, and in space, each one with its own characteristics and specific protections [1]. This paper presents the design and performance assessment of an adaptive tracking control approach based on B-spline neural networks with the capabilities required for real-time applications of DC shunt motors, without requiring a detailed mathematical model and values of the parameters of the nonlinear dynamic system. A high tracking performance refers to achieving small deviations from the speed reference trajectory planned for the closed-loop motor dynamics, in spite of endogenous and exogenous disturbances due to load torque and system parameters which are assumed to be unknown. Experimental results confirm the effectiveness of the proposed control approach for highly demanding motor operation conditions subjected to variable speed reference trajectories and completely unknown load torque. Laboratory experimental tests on a DC shunt motor prove the viability of the proposed adaptive output feedback trajectory tracking control approach
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