Linearisation control of AC permanent magnet synchronous motor servo system based on sensor technology

High dynamic response has always been one of the goals of the surface-mounted permanent magnet synchronous motor (SPMSM) servo system. To this end, this article proposes an anti-disturbance enhanced finite-time composite control strategy for the SPMSM servo system. First, a finite-time controller is designed to enhance the rapidity of the SPMSM speed loop. Second, an anti-disturbance enhanced finite-time disturbance observer is proposed, which optimizes the disturbance compensation channel and accelerates the disturbance compensation rate. Meanwhile, this method decouples the parameters of the controller and disturbance observer, making it easier to tune the parameters. Finally, combining the controller with the disturbance observer, the proposed composite control scheme has better dynamic performance than the traditional finite-time composite controller. The experimental evaluation results verify the superiority of the proposed method.

In this article, an improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to solve the optimization problem of controller parameters in the design of a permanent magnet synchronous motor (PMSM) servo system. The proposed algorithm presents the directional mutation operation to the particles, which fixes the position of some particular particles so as to enhance the searching ability to some remote regions. In order to cooperate with directional mutation operation, the updating formula of particles velocity is ulteriorly improved. Then, the proposed IHPSO algorithm is adopted to optimize parameters of the designed controller. A simulation and an experimental platform of the PMSM servo system are designed using a biological intelligence controller based on hormone regulation for the speed control and feedforward compensation for the position controller, where IHPSO is applied to the parameter optimization for speed and position controllers, which validate the effectiveness of the IHPSO algorithm and the designed control system.

This article presented an improved nonlinear sliding mode control based on disturbance observer for the permanent magnet synchronous motor servo system. The position transient response performances, such as fast response and non-overshoot, are enhanced by the introduction of novel nonlinear sliding surface in the design of sliding mode control law. In order to abolish the analysis necessity of changeable uncertainties and alleviate the system chattering, the nonlinear sliding mode control algorithm is improved by adaptive parameter estimation and saturation function. Since the improved nonlinear sliding mode control algorithm is sensitive to mismatched uncertainties which are mainly due to the load disturbance, a disturbance observer is put forward to estimate the mismatched uncertainties and is integrated into control law design to improve the positioning accuracy. The stability of improved nonlinear sliding mode control based on disturbance observer is proved based on the Lyapunov approach. The simulation results demonstrate the effectiveness and robustness of the proposed improved nonlinear sliding mode control based on disturbance observer for permanent magnet synchronous motor servo system with stationary and dynamic trajectory tracking.

In the permanent magnet synchronous motor servo system, as the motor nonlinearity, strong coupling, parameters variability, and hydraulic systems nonlinear factors such as leaks, Coulomb friction and the uncertainty of the load force, output torque of the motor is interfered with the large random disturbances, and the system suffers from serious dynamic nonlinearity. Adaptive observer is adopted to carry the load torque adaptive control by the estimation error, improve the dynamic performance, and increase disturbance restraint performance. Using Lyapunov stability theory analyses convergence conditions and proves the system stability. The system simulation experiment shows that the control method has strong robustness on parameters variation and random disturbance.

A finite-time control strategy is proposed to solve the problem of position tracking control for a permanent magnet synchronous motor servo system. It can guarantee that the motor’s desired position can be tracked in a finite time. Firstly, for the d-axis voltage, a first-order finite-time controller is designed based on the vector control principle. Then, for the q-axis voltage, based on a radial basis function (RBF) neural network, an integral high-order terminal sliding mode controller is designed. Theoretical analysis shows that under the proposed controller, the desired position can be tracked by the motor position in a finite time. Simulation results are given to show that the proposed control method has a strong anti-disturbance ability and a fast convergence performance.

This paper extends the existing Proximate Time-optimal Servomechanism (PTOS) control methodology to the more typical second-order servo systems with a damping element. A parameterized design of expanded PTOS control law with a speed-dependent linear region is presented for rapid and smooth set-point tracking using a bounded input signal. The control scheme uses the time-optimal bang-bang control law to accomplish maximum acceleration or braking whenever appropriate and then smoothly switches into a linear control law to achieve a bumpless settling. The control scheme is then applied to the position-velocity control loop in a permanent magnet synchronous motor servo system for set-point position regulation. Numerical simulation has been conducted, followed by experimental verification based on a TMS320F2812 digital signal controller board. The results confirm that the servo system can track a wide range of target references with superior transient performance and steady-state accuracy.

This article proposes a novel robust method for identifying the moment of inertia of rotor shaft in surface-mounted permanent magnet synchronous motor servo systems. Superior to conventional approaches, this method enables stable identification under current control loop or poorly tuned speed loop, and can identify the moment of inertia using low-amplitude speed injection as small as 0.5 rad/s (4.8 r/min), while maintaining minimal errors. The method is based on sinusoidal signal injection and identifies the moment of inertia by analyzing the phase difference between the estimated load torque and motor speed. This approach provides a reliable one-time parameter calibration, largely insensitive to the influence of unknown disturbances, including Stribeck friction, nonlinear load, and speed quantization errors. This method can be applied to rapid autotuning of speed loop and online moment of inertia identification with low-amplitude injection, providing a foundation for high-performance control algorithms. Experimental results validate these characteristics.

This article investigates the sliding mode control problem of uncertain nonlinear systems and its application in tracking periodic motion. Traditional sliding mode control fulfils perfect tracking at the cost of chattering, especially in the case of high-frequency scenarios. To address the limitations of this method, this article introduces the internal model of the reference signal in building the sliding manifold, which fully takes the advantages of two fundamental methodologies. Specifically, the internal model of the reference signal is involved in the closed-loop system dynamics after the sliding manifold is reached. Thence, all terms related to the reference signal in the tracking error can be eliminated and high-precision tracking can be achieved while reducing chattering phenomenon. Remarkably, the detailed expressions of the derivatives of the reference signal are unnecessarily required, which simplifies the controller design and implementation. It is rigorously proved mathematically that the closed-loop system is asymptotically stable. Finally, the proposed control algorithm is applied for solving the position tracking control problem for permanent magnet synchronous motor servo systems, and experimental results verify the effectiveness and superiority.

Robust output regulation for a class of MIMO nonlinear systems with uncertain exosystems

Background: The conventional method using one-degree-of-freedom (1DOF) controller for Permanent Magnet Synchronous Motor (PMSM) servo system has the trade-off problem between the dynamic performance and the robustness. Methods: In this paper, by using H∞ control theory, a novel robust two-degree-of-freedom (2DOF) controller has been proposed to improve the position control performance of PMSM servo system. Using robust control theory and 2DOF control theory, a H∞ robust position controller has been designed and discussed in detail. Results: The trade-off problem between the dynamic performance and robustness which exists in one-degree-of-freedom (1DOF) control can be dealt with by the application of 2DOF control theory. Then, through H∞ control theory, the design of robust position controller can be translated to H∞ robust standard design problem. Moreover, the control system with robust controller has been proved to be stable. Conclusion: Further simulation results demonstrate that compared with the conventional PID control, the designed control system has better robustness and attenuation to the disturbance of load impact.

Periodic adaptive learning control of PMSM servo system with LuGre model-based friction compensation

On the basis of analyzing the mathematical model of permanent-magnet synchronous motor, this paper uses vector control methods such as coordinate transformation and space vector pulse width modulation to put up nonlinear decoupling control for motor model. Using TI C2000 series dedicated motor control chip as the core of control system and combining with external testing and driver chips, it designs the control schematic of servo system. Finally, it verifies the feasibility of the project by simulating the control system in MATLAB/Simulink toolbox.

Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive

For permanent magnet synchronous motor speed and rotor position easily affected by the mechanical sensor signal transmission precision, to improve the performance of permanent magnet synchronous motor drive, the speed control system satisfies the requirement of fast response, and the complicated condition of frequent load disturbance has stronger resistance, this paper designed a small nerve network optimization of permanent magnet synchronous motor intelligent control system. The intelligent control system will be small neural networks applied to the model reference adaptive control of permanent magnet synchronous motor speed control system. On this basis, the structures, permanent magnet synchronous motor control system simulation, the simulation results show the system can accurately measure the rotational speed, rotor position and compared with the traditional model reference adaptive control, motor start-up speed no overshoot, has the stronger ability to resist load disturbance and load disturbance after speed restore faster (6 ms), in the motor control has strong practical application value.

Super-twisting sliding mode control (STM) offers advantages such as fast response and low demand on motor model accuracy, and is attractive for the permanent magnet synchronous motor (PMSM) servo systems. However, the inherent chattering issue and limited disturbance rejection capability degrade its servo control performance. To overcome these limitations, this paper proposes an improved STM approach using a variable reaching law super-twisting sliding mode (VRSTM) strategy, integrating a high-order disturbance observer (HODOB) to estimate all perturbations including motor parameter mismatch, to achieve a high-precision speed control for the servo systems. Compared to the conventional reaching law, the proposed VRSTM accelerates convergence during trajectory deviations from the sliding surface while decelerating near the surface, hence achieving a better dynamic response performance and chattering mitigation. Furthermore, by implementing the HODOB, the perturbations including motor parametric variations are estimated to enhance the disturbance rejection capability of the proposed VRSTM approach. Theoretical analysis confirms the validity of the proposed method in improving the dynamic performance, suppressing chattering phenomena, and enhancing disturbance rejection capabilities for PMSM servo drives. Simulation and experimental results confirm the effectiveness and robustness of the proposed method.