Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Abstract

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure

“…In the past two decades, adaptive controllers based on BSNN have been used in many industrial, electronic, mechatronics, and electromechanical control systems to regulate output variables, such as parallel kinematic manipulators, electrical power systems, shunt DC motors, UPS inverters, quadrotor, power control of wind turbine and induction motor [16][17][18][19][20][21]. The typical structure of a BSNN is shown in Figure 2.…”

“…The adaptability feature of the neural network gives the ability to learn from previous events, by interconnecting the input data to output, as shown in Figure 2. However, the initial values of weights of the BSNN are often generated randomly or adjusted based on the designer's experience to get excellent performance for a specific controller [16,17,19].…”

“…The adaptability feature of the neural network gives the ability to learn from previous events, by interconnecting the input data to output, as shown in Figure 2. However, the initial values of weights of the BSNN are often generated randomly or adjusted based on the designer's experience to get excellent performance for a specific controller [16,17,19]. The BSNN can compute its response on-line, based on a modest structure and a short number of basic mathematical operations that are an important desirable feature for efficiently controlled physical systems.…”

“…Some groups of investigators try to overcome this drawback using hybrid PI controllers or novel tuning rules to improve their performance against faults and parameter variations. In [16] an adaptive control structure with neural data processing was designed for two converter-fed DC machines coupled by a flexible connection. The controller is based on a recurrent neural network and is inspired by the Elman model.…”

Modern Electrical Drives: Trends, Problems, and Challenges

“…Very often, they are multidisciplinary works. In this Special Issue, 10 manuscripts were published that deal with the following topics: control strategies for different types of electrical motors [1][2][3], estimations of non-measurable states and parameters [3], sensorless control of electrical drives [1,2], the application of advanced control methodologies for high performance control of electrical drives [4][5][6], diagnoses, monitoring, and prognoses in electrical drives [7][8][9][10], and control of complex mechatronic systems, while taking the mechanical part of the system into account [3,4,6].…”