Intelligent position control of permanent magnet synchronous motor using recurrent fuzzy neural cerebellar model articulation network

Abstract

A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) control is proposed in this paper for position servo drive systems to track various periodical position references with robustness. The adopted position servo drive system is designed using a six-phase PMSM and equipped with a fault-tolerant control scheme. First, an ideal computed torque controller is designed for the tracking of the rotor position reference command. Since the uncertainties of the PMSM position servo drive system are difficult to know in advance, it is impossible to design an ideal computed control law for practical applications. Therefore, the RFNCMAN is proposed to mimic the ideal computed torque controller with a compensated controller to compensate the approximation error. In the RFNCMAN, a recurrent fuzzy cerebellar model articulation network (RFCMAN) is adopted in the first dimension to enhance the online learning rate and localisation learning capability. Moreover, a general recurrent fuzzy neural network (RFNN) is adopted in the second dimension to enhance the generalisation performance and to reduce the required memory and rule numbers. Finally, the proposed position control system is implemented in a 32-bit floating-point DSP. The effectiveness of the proposed RFNCMAN control system is verified by some experimental results.

A Review on Controlling Techniques for Permanent Magnet Synchronous Motor (PMSM) and Current State of the Art in the Research Area

“…The outcomes of [13] were compared with other existing approaches to fault tolerance. A design of recurrent fuzzy neural networkbased control mechanisms is found in Lin et al [14] were servo drive systems position is used to find different periodical position references offering robustness. This [14] [15] were analyzed in a high-speed region.…”

“…A design of recurrent fuzzy neural networkbased control mechanisms is found in Lin et al [14] were servo drive systems position is used to find different periodical position references offering robustness. This [14] [15] were analyzed in a high-speed region. The work towards controlling the speed of PMSM with the sensorless control system is found in Kim et al [16] where better speed control is achieved at a high-speed range.…”

A CMAC-Based Systematic Design Approach of an Adaptive Embedded Control Force Loading System

“…In summary, various CMAC control algorithms have been formed so far, such as CMAC feedforward control [9][10][11], CMAC feedback control [12][13][14], CMAC optimal control [15,16], CMAC fuzzy control [17][18][19][20][21][22], CMAC H-infinity control [23] and CMAC adaptive control [24][25][26]. The previous works can be divided into two categories: one is to improve the control structures of the CMAC, such as the CMAC feedforward control; the other is to improve the learning algorithms with other intelligent techniques, such as the fuzzy CMAC (FCMAC).…”

An Improved Predictive Functional Control with Minimum-Order Observer for Speed Control of Permanent Magnet Synchronous Motor

“…However, the control system of a PMSM makes it difficult to achieve a good control effect with the PI control scheme due to the presence of nonlinearity and uncertainty [1][2][3]. Scholars have put forward many advanced control methods to optimize the PMSM control system in recent years, e.g., adaptive control [4], fuzzy control [5], sliding mode control [6], feedback linearization [7], genetic algorithm control [8], neural network control [9], disturbance observer based (DOB) [10][11][12], and so on. These approaches have been successfully applied to PMSM control systems, and can improve the control performance of a motor from different aspects.…”