Chaotic Newton-Raphson Optimization Based Predictive Control for Permanent Magnet Synchronous Motor Systems with Long-Delay

Composite control based on FNTSMC and adaptive neural network for PMSM system

In this paper, a nonlinear state observer control strategy is developed for projective self-synchronization of the fractional-order chaotic attractors of a permanent magnet synchronous motor (PMSM) system. The mathematical model of PMSM system in a smooth fractional-order form is derived by using the fractional derivative theory. A state observer control design can achieve the full-state projective synchronization of the fractional-order PMSM (FO-PMSM) system without the limitation of partial-linearity. Global stability and asymptotic synchronization between the outputs of drive system and response system can be obtained. Simulation results are provided to demonstrate the effectiveness of the approach.

This work deals with the development of double closed loop control based on explicit model predictive control (MPC) for permanent magnet synchronous motor (PMSM) system with constraints. The aim of the proposed controller is to solve the constrained optimization problem for fast-sampling time PMSM system and improve the robustness of the system. The recreated mathematical model of PMSM is introduced at first. Then the proposed explicit MPC controller is designed based on the reformed model. The explicit controller is employed to control the mechanical speed and the q-axis current, and the proportional-integral (PI) control is employed to control d-axis current. The simulation results demonstrate that the proposed double closed loop control method is effective when the working condition of PMSM changes and the performance of the speed and current is satisfactory.

Background: In previous developments of permanent magnet synchronous motor tests, physical experiments provided results closest to actual working conditions. However, these experiments had long development cycles, increased costs, poor repeatability, and potential risks of damage to equipment and casualties if not handled carefully. The introduction of hardware-in-the-loop real-time simulation technology has effectively addressed these challenges in motor testing development. Therefore, the development of hardware-in-the-loop simulation technology holds significant importance. Objective: The existing real-time simulation platforms summarize specific examples of the current use of real-time simulation, and classify the hardware-in-the-loop simulation of permanent magnet synchronous motors to predict its development trend. Methods: The study analyzes the characteristics of existing real-time simulation platforms and their suitability for different environments. It also examines the modeling methods and real-time simulation techniques for permanent magnet synchronous motors under various circumstances, assessing their feasibility and accuracy, control strategies for permanent magnet synchronous motors are compared and expounds and compares the latest patented technologies. Results: The hardware-in-the-loop simulation system of the permanent magnet synchronous motor is analyzed and compared, and its typical features are summarized. Additionally, the problems in the hardware-in-the-loop simulation system are analyzed, and the future development trend is discussed. Conclusion: Compared to the simulation results of actual motors, the results obtained from hardware- in-the-loop simulation are highly accurate. The utilization of hardware-in-the-loop simulation platforms can effectively decrease development costs, shorten the research and development cycle, and minimize the risks associated with experiments in specific cases when testing permanent magnet synchronous motor systems. Additionally, it enhances the reproducibility of experiments.

To solve the problems of slow response speed of the permanent magnet synchronous motor (PMSM) system when the external interference occurs and the poor anti-interference ability, this paper proposes a double model predictive control (MPC) strategy for PMSM to control speed and current. To be specific, firstly, the mathematical model of PMSM is established under the two-phase synchronous rotation coordinate system. Secondly, we propose the double MPC strategy where MPC controller is used in the speed outer loop and deadbeat prediction controller is used in the current inner loop. Finally, By comparing the traditional control strategy (speed outer loop PI controller and current inner loop deadbeat prediction controller) with the double MPC strategy, the simulation results show that the double MPC has no overshot, better steady-state performance, faster dynamic response and stronger anti-load disturbance capability, which proves the effectiveness of the proposed strategy.

In order to optimize the speed-control performance of the permanent-magnet synchronous motor (PMSM) system with different disturbances and uncertainties, a nonlinear speed-control algorithm for the PMSM servo systems using sliding-mode control (SMC) and load torque compensation techniques is developed in this paper. A SMC strategy is designed based on field oriented control (FOC) theory, which is adopted in the speed loop of PMSM system. Meanwhile, considering the SMC need providing large gain to restrain disturbances resulting in large chattering, an adaptive extended sliding mode torque observer (ESMO) is proposed to estimate and compensate load torque. Through torque estimation for feed-forward compensation, one new composite SMC algorithm has been proposed to take a smaller value of switching gain without large chattering. The control scheme is verified by comprehensive simulation and experimental results.

Permanent magnet synchronous motors (PMSMs) have been widespread used in high-performance automation due to their superior control precision and energy efficiency. With the development of digital processors, deadbeat predictive control (DPC) has promising control strategy for PMSM drive systems. However, the performance of DPC is highly sensitive to inaccuracies in system parameters, while the system delay and total harmonic distortion can also significantly affect its control effectiveness. This paper presents a comprehensive review of the advantages of DPC strategies applied to PMSMs, as well as their limitations in practical applications. Several improvement approaches are also discussed, including parameter identification methods, robust control methods, and delay compensation methods. Finally, the paper explores future research trends in this field, highlighting potential directions for the development of predictive control in PMSM drive systems.

This paper proposes the neural network (NN) predictive controller that combines the advantages of NN and predictive control for the automatic voltage regulator (AVR). The NN predictive controller is suggested as a new intelligence controller rather than the conventional controllers for the AVR. This is the first application of the NN predictive controller for AVR. There are five parameters of the NN predictive controller which need a proper tuning to get a good performance by using the NN predictive controller. In recent papers, the parameters of NN predictive controller are typically set by trial and error or by the designer’s expertise. The imperialist competitive algorithm (ICA) is introduced in this paper as a new artificial intelligence technique instead of the trial-and-error or the designer’s expertise methods to get the optimal parameters of NN predictive controller in order to overcome the deviations of the voltage. The performance of the designed NN predictive controller based on the ICA is compared with the designed NN predictive controller based on the genetic algorithm and the conventional proportional–integral–derivative controller based on Ziegler–Nichols technique. The comparison emphasizes the superiority of the suggested NN predictive controller based on the ICA.

The fuzzy controller is a popular choice for permanent magnet synchronous motor (PMSM) control systems because of its advantages, such as straightforward design, and no reliance on the precise mathematical model of the motor. But the existing pure PI-like fuzzy control strategy still has some disadvantages, such as poor adaptive ability and large overshooting. This work redevelops the structure and rules of the adaptive fuzzy controller, and proposes and proves an improved adaptive PI-like fuzzy control algorithm for the PMSM system. Firstly, a parallel dual fuzzy controller structure is constructed to facilitate the adaptive adjustment of the “PI-like fuzzy controller”. Secondly, the error acceleration parameter rv(k), which contains the PMSM speed information, is set and normalized to accurately identify the dynamic response stages of the PMSM system. Lastly, an adaptive fuzzy rule table is designed based on the dynamic response waveform of the PMSM system, and the control characterization is analyzed. The simulation and experimental results of the PMSM system show that the improved adaptive PI-like fuzzy controller has a broad dynamic adjustment range, the PMSM can rapidly and smoothly reach the given speed during the startup stage with small overshooting, the speed drop is low when the load is abruptly added, the PMSM system can quickly return to the steady state with a strong adaptive ability, and its dynamic performance indicators surpass those of the PID controller and traditional PI-like fuzzy controller.

In order to take advantage of chaos in permanent magnet synchronous motor system, the permanent magnet synchronous motor systems with smooth and non-smooth air-gap models are studied. The chaos anti-control methods by the addition of linear feedback are dissertated and analyzed by numerical solution. The chaos in systems is anti-controlled. The allowable range controlling variables and the chaos orbits of the systems under anti-control are obtained. The systems obtain the new chaotic attractors with the completely different topology.

The work has developed an innovative speed loop controller for the permanent magnet synchronous motor (PMSM) system. The main advantage of the presented method lies in the fact that the boundary of the lumped disturbance is not required. Under this case, the severe chattering caused by the inappropriate choice of parameters can be avoided. Finally, the simulation and experimental consequences are given to demonstrate the efficiency of the proposed algorithm used to control a PMSM system subject to the lumped disturbance.

Open phase fault (OPF) is a common electrical fault of five-phase permanent magnet synchronous motor (PMSM) system. The OPF not only can occur in normal operation mode, but also in fault-tolerant operation mode. Hence, this paper proposes a unified OPF diagnosis method for five-phase PMSM system in multiple operation modes (normal operation and fault-tolerant operation modes). The proposed method includes two steps: fault detection and fault location. In multiple operation modes, fault detection can be uniformly achieved by a single fault indicator, and fault location can be uniformly implemented by space current vector, reducing the fault diagnosis complexity. Both the simulation and experimental results show that the proposed method can achieve unified OPF diagnosis of five-phase PMSM system in multiple operating modes. In addition, the proposed method has the strong robustness to the speed and load disturbance.

This paper investigates the multi-objective control of the nonlinear permanent magnet synchronous motor (PMSM) system. To this end, the T-S fuzzy approach is introduced to tackle the synthesis of the speed control under nonlinear dynamics. Then, a novel multi-objective bat algorithm (NMOBA) is proposed for optimizing the constructed T-S fuzzy controller. Specifically, the virtual desired variables are utilized for developing the fuzzy tracking control to trace the desired speed profiles. Theoretical analyses demonstrate that the proposed control strategy can simplify the nonlinear controller design. Furthermore, the challenge in the optimization of the T-S fuzzy controller is addressed, that is, complex adjustable parameters usually lead to multiple local optima. To avoid premature convergence without losing efficiency, the hybrid mechanism is designed for the proposed NMOBA to enhance convergence and diversity of the swarm. Experimental results on a real-world PMSM system illustrate that the proposed controller has a significant advantage over the traditional LMI-based T-S fuzzy controller.

To achieve high robustness and performance, this article proposes an enhanced observer based on a predictive error model for the deadbeat predictive current control method (DPCC-PEMO). First, the mathematical model of permanent magnet synchronous motor (PMSM), deadbeat predictive current control (DPCC) method, and disturbance analysis are presented. Second, to estimate the trend of current variation in advance under nonlinear disturbance effect of model mismatch, the predictive error model is designed based on the recursive least squares (RLS) algorithm. Furthermore, the enhanced observer combined with the predictive error model (PEMO) is proposed, and its stability and high convergence are proved. Finally, utilizing the PEMO, the predictive current model is established and the proposed DPCC method is developed. The experimental results validate the strong robustness and excellent dynamic tracking performance of the proposed method in PMSM systems.

In this paper, a class of smooth-air-gap permanent magnet synchronous motor systems which is stable originally are chaotified. The adaptive control idea is employed to generate chaos. A novel adaptive chaotification controller is designed to make the considered smooth-air-gap permanent magnet synchronous motor system to asymptotically track a known chaotic system. Based on the Lyapunov stability theory, the asymptotic stability of the tracking error system is proved. The effectiveness of the proposed chaotification method is verified via an illustrative example.