Robust adaptive neural network control for switched reluctance motor drives

Abstract

This article presents a robust adaptive neural network controller for switched reluctance motor (SRM) speed control with both parameter variations and external load disturbances. The radial basis function neural network with the technology of minimal learning parameters is employed to approximate an ideal control law which includes the parameter variations and external disturbances. Furthermore, a proportional control term is introduced to improve the transient performance and chattering phenomena of the SRM drive system. The asymptotic stability of the proposed controller is guaranteed through rigorous Lyapunov analysis. A main advantage of the proposed control scheme is that it contains only one adaptive parameter that needs to be updated on-line. This advantage result in a much simpler adaptive control algorithm, which is convenient to implement in switched reluctance drives. Finally, the simulations and experiments are carried out to demonstrate the effectiveness of the proposed control scheme.

Self-tuning state-feedback control of a rotary pendulum system using adjustable degree-of-stability design

“…Several mechanisms have been proposed in the literature. The fuzzy and neural inference schemes are computationally expensive because they either require a large set of empirically-defined logical rules or a large set of training data to accurately update the critical controller parameters, respectively [4][5][6]. The online iterative-learning algorithms put an excessive recursive computational burden on the embedded processor [31].…”

“…The imprecise empirical construction of the fuzzy approximation method inevitably degrades the control signal quality under parametric variations [5]. The neural controllers require large sets of training data to devise an accurate inverse model [6]. Despite their robustness, the slidingmode controllers render highly discontinuous control behaviour which injects chattering in the response [7].…”

Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions

“…They are chosen such that; Q = Q T ≥ 0 and R = R T > 0. The penalty matrices used in this work are given by (11).…”

“…The sliding mode controllers, despite their robustness, inevitably inject highfrequency chattering in the actuator response [9,10]. The neural-fuzzy schemes generally require large training data or empirically defined elaborate rule-bases, respectively, to realize a robust control system [11,12]. The model-based linear-quadratic-integral (LQI) controllers are also preferred for speed control applications because of their optimal control yield [13].…”

Control of variable reluctance machine (8/6) by artificiel intelligence techniques

“…Structure of the fuzzy controlWe retained for the controller[29]: -Input variables whose member ship functions of the fuzzy sets are triangular and trapezoidal.-The quantities and are standardized in a discourse universe -5, + 5,  -2.5, + 2.5refer Figure 8. -The output variable C em is normalized in a speech universe  -40, + 40, refer Figure 9.…”