Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent

Abstract

This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistance Rs and combined inertia of rotor and load J) and changes in load torque TL can be mentioned. In this way, the performance of the control system can be maintained over a relatively wide range of variation in the types of parameters mentioned above. It also presents the synthesis of robust control, the implementation in MATLAB/Simulink, and an improved version using a reinforcement learning twin-delayed deep deterministic policy gradient (RL-TD3) agent, working in tandem with the robust controller to achieve superior performance of the PMSM sensored control system. The comparison of the proposed control systems, in the case of sensored control versus the classical field oriented control (FOC) structure, based on classical PI-type controllers, is made both in terms of the usual response time and error speed ripple, but also in terms of the fractal dimension (DF) of the rotor speed signal, by verifying the hypothesis that the use of a more efficient control system results in a higher DF of the controlled variable. Starting from a basic structure of an ESO-type observer which, by its structure, allows the estimation of both the PMSM rotor speed and a term incorporating the disturbances on the system (from which, in this case, an estimate of the PMSM load torque can be extracted), four variants of observers are proposed, obtained by combining the use of a multiple neural network (NN) load torque observer and an RL-TD3 agent. The numerical simulations performed in MATLAB/Simulink validate the superior performance obtained by using properly trained RL-TD3 agents, both in the case of sensored and sensorless control.

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent

“…r = ω re f ). By linearizing Equations (10) and (11), the linear forms given by relations (12) and ( 13) are obtained, and a graphical description of the LADRC control structure of a PMSM is given in Figure 4. The ESO-type observer (third order) block diagram for rotor speed PMSM estimation is shown in Figure 5.…”

“…The first four terms refer to the actual error due to the d-q reference frame terms, the id and iq currents, and the speed and position of the PMSM rotor ω and θ relative to their references. The last term The implementation of an RL-TD3 agent first requires a training phase, during which the RL-TD3 agent [11,36], based on the signals collected from the system and by maximizing a reward that can be considered an integral performance criterion, will provide a correction signal for the current reference i qref . Next, the inner current control loops are those used in the FOC control strategy and can be implemented as ON/OFF controllers with hysteresis for fast response.…”

“…To compare the performance of the proposed control systems, the DF calculation was also taken into account. It can be specified that the box-counting method is used to calculate DF according to [11,38,39].…”

“…Typically, the overall control strategy of a PMSM is of the FOC type [1][2][3], which includes two cascaded control loops, the inner loop providing current control and the outer loop providing PMSM rotor speed control. Due to the multitude of applications and domains, among which we can mention robotics, computer peripherals, the aerospace industry, and electric drives [4,5], in which PMSM is used, the controllers implemented in the global FOC strategy have also evolved from simple to complex, i.e., from classical PItype controllers [6,7] to adaptive [8,9], predictive [10], or robust [11] type controllers [12,13].…”

“…In terms of estimating the disturbances acting on the system, this paper proposes the use of a DO-type observer [30][31][32][33]. It is also proposed to use an RL-TD3 agent [11,[34][35][36], which, after the training period, will provide correction signals to the ESO-/DO-type observer to estimate the external disturbances as accurately as possible. In this way, the LADRC controller used together with the proposed control structure will ensure superior performance of the PMSM control system.…”