Predictive Speed Control Research Articles

Enhanced Model-Free Predictive Speed Control Without Weighting Factors for PMSM Based on Newly Designed Cost Function

Enhanced Model-Free Predictive Speed Control Without Weighting Factors for PMSM Based on Newly Designed Cost Function

Research on Predictive Speed Control Scheme for Surface-Mounted Permanent Magnet Servo Systems

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications.

Continuous Control Set Predictive Speed Control of SPMSM Drives With Stability Improvement

Continuous Control Set Predictive Speed Control of SPMSM Drives With Stability Improvement

Antisaturation Extended State Observer for Speed Predictive Control of Servo System

Antisaturation Extended State Observer for Speed Predictive Control of Servo System

Improved Model Predictive Speed Control of a PMSM via Laguerre Functions

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response.

Predictive controllers for synchronous reluctance motor drive systems

This paper proposes the design and implementation of predictive controllers for synchronous reluctance motor drive systems to enhance their dynamic responses. The predictive speed and current controllers in this paper are designed in systematic procedures. The predictive speed controller is implemented by using Laguerre function procedure. The Laguerre function is used to simplify the algorithm and to minimize the execution time of the digital signal processor. For predictive current controller, a finite control set method improves the current tracking ability. The measured currents are used to predict the future phase-current based on the motor model. The optimal control inputs of both predictive controllers are determined by using a cost function minimization method. Experimental results show the proposed drive system provides a wide adjustable speed range, from 2 r/min to 1800 r/min. It has better performance than a proportional-integral (PI) controller including fast rise time, which is 0.9 second, small steady state error, which is 0.32 r/min, and small current ripples. A 32-bit floating-point digital signal processor, TMS-320-F-28335 DSP, is employed to implement the control algorithms.

Linear Matrix Inequality-Based Robust Model Predictive Speed Control for a Permanent Magnetic Synchronous Motor with a Disturbance Observer

In this paper, a linear matrix inequality (LMI)-based robust model predictive speed control (RMPSC) with a disturbance observer (DOB) is proposed to guarantee stability and control performance against the parameter uncertainty and disturbance of a permanent magnetic synchronous motor (PMSM). All external torques applied to the PMSM are defined as disturbance, estimated by the DOB, and used to construct the RMPSC method. The proposed DOB and RMPSC are determined by a multiple LMI-based optimization approach. Furthermore, parameter uncertainty within a certain range, due to manufacturing errors or aging deterioration, is considered and a systematic tuning method is proposed to obtain the optimal gains. Finally, an offline optimization method is developed that ensures a low computational load to enable real-time processing. Simulation results demonstrate the effectiveness and validity of the proposed control method.

Research on speed predictive control system of intelligent connected vehicle based on V2X

To enhance the efficiency of urban road traffic and reduce waiting time at traffic lights, this paper proposes a V2X based speed prediction control method for intelligent connected vehicles. Firstly, this paper uses PreScan to build an intelligent network environment model based on V2X communication technology. Then, the traffic signal and target vehicle information transmitted by V2X are analyzed and processed, leading to the design of an intelligent connected vehicle speed prediction control system. Finally, comparative experiments were conducted using HIL between the conventional vehicle speed decision control system and the V2X-based intelligent connected vehicle speed prediction control system under various operating conditions. The experimental results demonstrate that the proposed speed prediction control system can reduce waiting time for traffic lights and enhance road traffic efficiency. Therefore, this paper’s designed speed prediction control system is both effective and feasible, providing a reference for future research on intelligent connected vehicle speed decision control.

Multi-vector-based Model-Free Predictive Speed Control with Disturbance Suppression and Weighting Factor Elimination for SPMSM

Multi-vector-based Model-Free Predictive Speed Control with Disturbance Suppression and Weighting Factor Elimination for SPMSM

Modulated Model Predictive Speed Controller for PMSM Drives Employing Voltage-Based Cost Function

Modulated Model Predictive Speed Controller for PMSM Drives Employing Voltage-Based Cost Function

An Improved Robust Model Predictive Speed Control With Inertia Identification for PMSM Drives in the Electrohydrostatic Actuator

An Improved Robust Model Predictive Speed Control With Inertia Identification for PMSM Drives in the Electrohydrostatic Actuator

Improved cascaded model-free predictive speed control for PMSM speed ripple minimization based on ultra-local model

This paper presents an improved cascaded model-free predictive speed and current control with the periodic and aperiodic disturbances suppression to achieve a smooth speed. The cascaded structure has an external speed loop and an internal current loop, both implemented with model-free predictive control to enhance the robustness of the controller. The current loop is designed based on the finite control set model-free predictive current control (FCS-MFPCC) strategy with an ultra-local model to regulate the stator currents, and the speed loop uses the proposed continuous control set model-free predictive speed control (CCS-MFPSC) to make full use of the excellent dynamic performance of the current-loop controller. To suppress the periodic disturbance that exists in the PMSM system, an improved parallel quasi-resonant controller (QRC) with an error limitation is embedded into the CCS-MFPSC, which can generate the compensated current. Based on the stability condition, the stability of the proposed MFPSC-QRC strategy is directly analyzed in the z-domain. Finally, the effectiveness and feasibility of the proposed cascaded model-free predictive speed and current strategy are validated on a PMSM test platform.

A Novel Low-Complexity Cascaded Model Predictive Control Method for PMSM

A novel low-complexity cascaded model predictive control method for permanent magnet synchronous motors is proposed to achieve a fast dynamic response to ensure the system’s steady-state performance. Firstly, a predictive speed controller based on an extended state observer is designed in the outer speed loop to improve the anti-interference ability of the system; then, a low-complexity three-vector predictive control algorithm is adopted in the current inner loop, taking into account the steady-state performance of the system and lower computational burden. Finally, a comparative analysis is conducted between the proposed method and traditional methods through simulation and experiments, proving that the proposed method performs well in dynamic and static performance. On this basis, the computational complexity of the current inner loop three-vector prediction algorithm is effectively reduced, indicating the correctness and effectiveness of the proposed method.

Fuzzy controller-based predictive speed control of a permanent magnet synchronous motor

Fuzzy controller-based predictive speed control of a permanent magnet synchronous motor

Predictive Speed Control of a DC Universal Motor Applied to Monitor Electric Vehicle Batteries

This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection.

Recurrent Neural Network-Based Robust Adaptive Model Predictive Speed Control for PMSM With Parameter Mismatch

Due to the fast dynamic response and ability to handle the constraints, model predictive control (MPC) is becoming an exciting and widely applied approach for permanent magnet synchronous motor (PMSM). However, the control performance of conventional MPC is significantly affected by the model parameter mismatches. In addition, the high computational volume is also a limiting factor of MPC. This paper <inline-formula><tex-math notation="LaTeX">$^{\prime }$</tex-math></inline-formula> s main objective is to solve these problems by proposing an advanced control structure. First, the parameter mismatches are estimated online by a discrete-time mechanical parameter observer (DTMPO), then a robust adaptive model predictive speed control (RA-MPSC) is designed to suppress the influence of parameter mismatches. Secondly, a recurrent neural network (RNN) based algorithm is introduced to compute the RA-MPSC control law by solving an optimization problem in real-time. Lastly, the simulation and experimental results are presented to validate the performance of the proposed method.

Design and Implementation of Predictive Controllers for a 36-Slot 12-Pole Outer-Rotor SPMSM/SPMSG System with Energy Recovery

This paper investigates a drive system with energy recovery which uses a 3-phase 1-kW 36-slot 12-pole distributed winding outer-rotor surface-mounted permanent-magnet synchronous motor (SPMSM) and surface-mounted permanent-magnet synchronous generator (SPMSG), which can be used in indoor exercise bicycles. In order to extend drive system operating speed range, the constant torque control, flux-weakening control, and maximum torque/voltage control are used to extend its operation speed up to 1.75 times rated speed. In addition, a predictive speed controller and a predictive current controller are proposed to improve transient responses, load disturbance responses, and tracking responses. A digital signal processor, type TMS-320F-28035, manufactured by Texas Instruments, is used as a control center for the proposed SPMSM/SPMSG drive system. Experimental results validate the feasibility and correctness of the proposed methods.

A Robust Predictive Speed Control for SPMSM Systems Using a Sliding Mode Gradient Descent Disturbance Observer

This paper proposes a sliding mode gradient descent disturbance observer-based adaptive reaching law sliding mode predictive speed control (GD-SMPC+ARL) for surface-mounted permanent magnet synchronous motor (SPMSM) systems to achieve outstanding disturbance rejection ability and tracking performance. Firstly, an SPMSM model with disturbance is given by incorporating the lumped disturbances into the q-axis current's coefficient. Then, a stable sliding mode gradient descent disturbance observer (SM-GDDO) is designed to identify the q-axis current's coefficient that varies with the lumped disturbance. Finally, a GD-SMPC+ARL strategy, which utilizes an adaptive reaching law-based cost function, is developed on the basis of the proposed SPMSM model, where the q-axis current's coefficient is estimated by utilizing SM-GDDO and updated at each control period. Compared with conventional reaching laws, the proposed adaptive reaching law suppresses the chattering while maintaining a fast following speed. Simulation and experimental results verify the excellent robustness and tracking performances of the proposed method.

Full prediction cascade control of permanent magnet toroidal motor with time-varying parameters

This paper presents the modeling and predictive control of permanent magnet toroidal motor (PMTM). Based on the spatial structure characteristics and working principle of toroidal motor, the time-varying inductance parameters are derived. The nonlinear mathematical model of toroidal motor is established in rotating coordinate system. The output fluctuation of the toroidal motor is analyzed. To improve the output performance of toroidal motor, the full predictive cascade speed and current control (F-MPC) strategy for toroidal motor is proposed. For F-MPC of toroidal motor, the finite control set model predictive current control (FCS-MPCC) strategy with delay compensation is adopted in current inner loop, and the deadbeat model predictive speed control (DB-MPSC) strategy is adopted in speed outer loop. Meanwhile, to achieve DB-MPSC, the reduced-order Luenberger observer is designed to obtain load torque in real time. F-MPC is the combination of current-speed prediction and Luenberger observer; it can overcome the limitation of excessive parameter adjustment of traditional cascade PI (Proportional Integral) controller. The simulation results show that compared with FOC (Field Oriented Control) and FCS-MPCC, F-MPC can improve the dynamic response and anti-interference performance of toroidal motor, and it can also reduce the fluctuation of output speed and torque significantly.

Robust Predictive Speed Control of SPMSM Drives With Algebraically Designed Weighting Factors

Predictive speed control (PSC) is a promising control strategy for high dynamic performance electrical drives. However, its implementation faces two challenges: the selection of weighting factors and the elimination of steady-state errors (SSEs). The weighting factors are commonly selected through trial-and-error, which is time consuming. Additionally, SSEs are frequently observed due to model uncertainties and parameter mismatches. Focusing on the aforementioned issues, this article proposes a PSC strategy for surface-mounted permanent-magnet synchronous motor drives, where the weighting factors are designed algebraically and SSEs are eliminated. The weighting factor design process is illustrated through rearranging the equivalent speed tracking error. The SSEs are eliminated by incorporating integral terms into the cost function. The performance of the proposed strategy is validated through comparative experiments with field-oriented control and adaptive integral sliding-mode predictive control. The results demonstrate the effectiveness of the designed weighting factors and the robustness of the system.