Nonlinear Adaptive Neural Control of Power Converter-Driven DC Motor System: Design and Experimental Validation

This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter-driven DC motor system. The proposed technique integrates an adaptive polynomial-neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network-based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady-state performance. The stability of the entire closed-loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start-up, step-reference tracking, and external step-load torque disturbances. The real-time experimentation is conducted on a laboratory prototype of power converter-driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to (Formula presented.) and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.