Predictive Speed Control Research Articles

Robust Cascaded Deadbeat Predictive Control for Dual Three-Phase Variable-Flux PMSM Considering Intrinsic Delay in Speed Loop

In this article, a robust cascaded deadbeat predictive speed control method (PSC) is proposed with speed and disturbance observer for the dual three-phase variable-flux permanent-magnet synchronous motor (VF-PMSM) drives. Considering the intrinsic delay in speed loop, an improved predictive speed model has been derived in discrete domain. In turn, a speed observer is proposed for the cascaded PSC to mitigate the issue of this intrinsic delay. In order to improve the system robustness, the torque disturbance observer including the generalized proportional integral observer and the sliding-mode observer has been proposed for parameter variation and load torque perturbation. Furthermore, the stability analysis indicates that the margin of parameter mismatch is determined by the coefficient in the speed observer, which can be expressed as the equivalent gain in discrete domain. The performance comparison between the conventional PSC method and the proposed PSC method has been investigated for parameter robustness. The experimental results are presented to verify the improvements of the proposed PSC method for the dual three-phase VF-PMSM drives under model uncertainty and magnetization manipulation conditions.

High-Order Band-Pass Active Damping Control and Predictive Control for Three-Phase Small-Film DC-Link Capacitor IPMSM Drive Systems

Traditional three-phase rectifier DC-link inverters have been used in industry for more than 40 years. However, electrolytic capacitors, which are widely used in traditional inverters, have very large volumes and can only be used for five years. To solve this problem, a three-phase small-film DC-link capacitor interior permanent-magnet synchronous motor drive system is investigated in this paper. This small-film capacitor not only has a longer life and smaller size than an electrolytic capacitor, but it can also improve the input harmonic currents and power factor on the grid side. A high-order band-pass filter active damping control is proposed here. In addition, a constrained predictive speed controller is designed to enhance the transient, load disturbance, and tracking speed performance. Furthermore, a constrained predictive current controller is implemented to reduce the three-phase harmonic currents of the motor. A digital signal processor, type TMS-320F-28035, manufactured by Texas Instruments, is employed as a control center to conduct the whole control algorithms. Several simulated and measured results are compared to demonstrate the practicability and correctness of the proposed control algorithms.

Multidisturbance Suppressed Model Predictive Direct Speed Control With Low Pulsation for PMSM Drives

Model predictive direct speed control (MPDSC), with a simple structure and without parameter tuning required, can use only one controller to constrain the speed and current of the motor at the same time. First, a direct voltage vector selection-based three-vector model predictive speed control, simplifying the optimization process, is proposed in this article, which reduces the pulsation of current and speed without increasing the computational burden compared with MPDSC. At the same time, because of the reasonable distribution of voltage vectors in the proposed three-vector method, the quality of the control and modulation has been significantly improved. Second, a multidisturbance Luenberger observer is designed to observe mechanical and electrical disturbances and to make further compensation, which reduced the influence of model mismatch, load torque, and unmodeled dynamics simultaneously and avoided the use of multiple observers, with fewer parameters and easier tuning. The experiment is presented to validate the effectiveness of the proposed method on the 7.5-kW experiment platform.

Model-Free Hybrid Parallel Predictive Speed Control Based On Ultralocal Model of PMSM for Electric Vehicles

Conventional model predictive direct speed control (MP-DSC) has received extensive attention because of its easy implementation, fast dynamic response, and simple structure. However, weighting factor and parameter mismatch are two key factors restricting the control performance of MP-DSC. In this article, a model-free hybrid parallel predictive speed control (MF-HPPSC) based on ultralocal model is proposed to solve the above-mentioned problems. First, the multiobjective parallel predictive speed control method realizes synchronous control of speed, torque, and flux linkage without weighting factor. The original excellent dynamic response capability is guaranteed, and the steady-state performance is simultaneously enhanced. Moreover, the linear extended state observer (LESO) designed by the unique ultralocal model makes the calculation process of future state prediction without any machine parameters, and the robustness of the system is significantly improved. Finally, the simulation and experiment comparison among the conventional MP-DSC, conventional model-free control, and the proposed MF-HPPSC verified the superiority of the proposed method.

Generalized predictive speed control based on equivalent-input-disturbance for PMSM drive system

The speed control performance of permanent magnet synchronous motor (PMSM) drive system is degraded due to non-matching disturbances such as parameter perturbation and load torque mutation. This paper presents a nonlinear generalized predictive control method based on equivalent-input-disturbance (GPC-based-EID) to realize the fast response and strong robustness of the speed controller of the PMSM drive system. Firstly, the continuous time nonlinear system of a motor mechanical equation is established. A speed controller based on the generalized predictive theory rather than the PI controller of a traditional vector control is designed. Then, the drive system with disturbance is transformed into an EID system. An improved nonsingular fast terminal sliding-mode observer (NFTSMO) is introduced to accurately estimate the EID of total system non-matching disturbances. And the active compensation of non-matching disturbances is realized through feedforward method. This greatly enhances the robustness and the speed tracking performance of the drive system. Comparisons with PI control, traditional GPC and GPC-based-ESO methods show the effectiveness of the method.

Data-driven finite control set model predictive speed control of an autonomous surface vehicle subject to fully unknown kinetics and propulsion dynamics

This article investigates the speed control problem of an autonomous surface vehicle (ASV) suffering from fully unknown kinetics and propulsion dynamics. A data-driven finite control set model predictive control (FCS-MPC) strategy is presented for tracking a desired surge velocity. In the learning loop, a data-driven neural predictor is present to learn the unknown input gains, model nonlinearities, and environmental disturbances simultaneously. Filtered data of vehicle states are recorded and used to update the neural network parameters. In the control loop, a pulsewidth-modulation-driven speed controller is developed based on the FCS-MPC technique. The optimal control actions are selected based on the receding horizon optimization with a prediction model. The learning error system of the data-driven neural predictor is proved to be input-to-state stable via Lyapunov analysis. A major feature of the proposed method is that optimal performance can be provided for speed tracking without using any a priori knowledge of model parameters or external disturbances. Simulation results substantiate the effectiveness of the proposed data-driven FCS-MPC strategy for an ASV to track a reference surge velocity.

A MPSC strategy with adjustable C-rate of lithium-ion cell for PMSM driving EV

Battery is the core equipment of the electrical vehicle (EV), battery life, and performance are critical to EV. Only motor variables are considered in most model predictive control (MPC) strategy in which battery indexes are neglected. A model predictive speed control (MPSC) strategy with adjustable C-rate of battery is proposed in this paper on EV with permanent magnet synchronous machine (PMSM). The rest capacity of battery is involved in the cost function, and the operating speed is related to the rest capacity of battery which adjusts the C-rate of the discharging currents further to protect battery. As the EV runs, the C-rate is decreased step by step according to the improved cost function, and the energy is saved to prolong mileage. The benefits of the proposed method including discharging capacities and operating mileage are corroborated by the simulation and repetitive experimental results under same conditions.

Hybrid MPPT-based predictive speed control model for variable speed PMSG wind energy conversion systems

In this research, a predictive speed control (PSC) technique based on permanent magnet synchronous generators is proposed for variable-speed wind energy conversion systems (VS-WECS) (PMSG). The control approach that has been developed makes it possible to regulate mechanical and electrical variables concurrently within the context of a single cost function. The power converter will then use the optimum switching state that will result in the lowest possible cost function when it has been chosen. The maximum power point tracking (MPPT) algorithms used in the proposed control approach are combined in order to achieve optimum efficiency. As a direct result of this, the conventional cascade structure of proportional-integral (PI) controllers has been removed, which results in an improvement in the system's dynamic responsiveness. In addition, predictive current control, also known as PCC, is implemented on the grid-side converter, also known as the GSC, in order to accomplish decoupled grid current control. Using MATLAB/SIMULINK, we analyze the performance of the suggested control methods and compare it to the performance of a traditional PI speed controller. The findings demonstrated that the MPC controller is superior than the PI controller in terms of its ability to handle system dynamics.

Continuous Control Set Predictive Speed Control of SPMSM Drives With Short Prediction Horizon

This article proposes a continuous control set predictive speed control (CCS-PSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives, where the prediction horizon is short and the computational complexity is low. The optimization problem of the proposed CCS-PSC is formulated and analyzed. The current and voltage constraints are transformed into incremental voltage constraints, which can be updated in real-time. A reduced-order incremental model of SPMSM is adopted for the prediction, where integrators are naturally embedded. Hildreth’s quadratic programming algorithm is used to solve the optimization problem, where the active constraints are identified and the reference voltage is calculated. The performance of the proposed strategy is validated through comparative experiments with finite control set predictive speed control (FCS-PSC), cascaded CCS-PSC and field-oriented control. The results demonstrate the superb dynamic performance of the proposed CCS-PSC strategy, while the phase current total harmonic distortion is relatively low.

Design of High-Dynamic PMSM Servo Drive Using Nonlinear Predictive Controller with Harmonic Disturbance Observer

The high-dynamic permanent magnet (PM) motor servo system with high-bandwidth is the core equipment for industrial production, and the control bandwidth is also an important indexes to evaluate the performance of the servo system. The non-cascaded direct predictive speed control is an appropriate scheme to optimize the dynamic performance of the PM motor servo system. However, the high bandwidth of the non-cascaded control structure results in poor anti-interference ability, and it cannot effectively deal with the coupling relationship between current and speed, leading to poor control performance in the current limit region. Regarding the above problems, a nonlinear predictive speed control strategy combined with harmonic disturbance observer is proposed. In the proposed strategy, the disturbances of the servo system are separated from the mathematical model according to the nonlinear modeling theory, and the traditional disturbance observer is modified to estimate the harmonics. A nonlinear control law with strong disturbance suppression ability was designed. Furthermore, a complete current and speed prediction mechanism was introduced into the algorithm, in which the proportional differential (PD) controller is employed as the connection medium between the reference current and speed to solve the coupling problem of the non-cascaded control structure.

Hybrid regression model via multivariate adaptive regression spline and online sequential extreme learning machine and its application in vision servo system

To solve the problems of slow convergence speed, poor robustness, and complex calculation of image Jacobian matrix in image-based visual servo system, a hybrid regression model based on multiple adaptive regression spline and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error. In MOS-ELM, MARS is used to evaluate the importance of input features and select specific features as the input features of online sequential extreme learning machine, so as to obtain better generalization performance and increase the stability of regression model. Finally, the method is applied to the speed predictive control of the manipulator end effector controlled by image-based visual servo and the prediction of machine learning data sets. Experimental results show that the algorithm has high prediction accuracy on machine learning data sets and good control performance in image-based visual servo.

Computationally efficient direct predictive speed control of PMSMs fed by three-level NPC convertors with guaranteed stability

Computationally efficient direct predictive speed control of PMSMs fed by three-level NPC convertors with guaranteed stability

Non-Cascaded Model-Free Predictive Speed Control of SMPMSM Drive System

The cascaded structure is generally adopted in the permanent magnet synchronous motor drive system, however, the speed dynamic performance is always deteriorated due to the limited bandwidth of speed loop when the linear controller is employed. Therefore, in order to realize quick dynamic response, the speed loop is eliminated in the non-cascaded control structure, and the non-cascaded model-free predictive speed control is proposed in this paper. First, the ultra-local model of SMPMSM drive system is established with a compound variable which implicitly unifies the speed and current of different time scales, and then the Lyapunov function is designed to derive control law and generate the inverter reference voltage automatically by considering the delay of digital control. Moreover, the inverter reference voltage is further modified under voltage and current constraints to guarantee system safety and reliable operation. Finally, the feasibility and validity of the proposed method has been verified by system simulation and experimental researches.

Anti‐saturation GPC speed control of induction motor drives

One of the most critical issues in the fast and large step change in the speed control of an induction motor is the excessive generated current command, and consequently, if the speed controller does not have output magnitude limiter, this might end up damaging the power electronics converter and the motor itself. This paper is devoted to overcoming the speed controller saturation phenomenon. Firstly, predictive controllers in polynomial form are designed for the speed control loop and for the two inner currents control loops. Then, the outer predictive speed controller is retuned by convex optimization of the Youla parameter considering temporal and frequency constraints, so that the resulting controller assures that the speed response to the reference remains in an imposed template with a minimal current command in transient periods and, in other hand, it keeps the closed loop features obtained by the initial predictive controller. Simulations results show the speed control performance of the proposed control system for various operating conditions of the motor.

Anti windup GPC speed controller for induction machine based on Youla parametrization

Abstract In the induction motor indirect vector control system, because of its physical limitations, the large step change in the speed command and/or load would eventually cause the so-called “integral windup phenomenon” which causes unexpected behavior of the system. To counteract this problem, an anti- windup generalized predictive speed control method is proposed by using the Youla parametrization. As first step, the design of an initial GPC controller based on its polynomial equivalent structure is required. Then, thanks to the Youla parametrization, this controller is retuned considering two specifications. The first is a frequency specification on the quadratic component of stator current response to the speed reference. And the second is a time domain constraint on the measured speed response to the speed reference. These constraints are formulated within a convex optimization framework. The simulation results proved the efficiency of the present design method.

A Novel Model Predictive Speed Controller for PMSG in Wind Energy Systems

A Novel Model Predictive Speed Controller for PMSG in Wind Energy Systems

Robust Continuous Model Predictive Speed and Current Control for PMSM With Adaptive Integral Sliding-Mode Approach

Model predictive control has been considered as a potential alternative method in electrical drives. High reliable predictive model design is a research hotspot. To further improve the performance, a robust continuous model predictive speed and current control for PMSM with an adaptive integral sliding-mode approach are put forward. The prediction model is directly derived from the integral slinging-mode surface. By using the self-regulation method control and the amplitude of the sliding-mode function, an adaptive integral sliding-mode predictive control (AISMPC) is designed. The proposed strategy solves the chattering problem of sliding-mode control and improves the dynamics of the system. AISMPC has been carried out on a digital signal processing hardware system. The experimental results validate the excellent tracking performance of the proposed strategy.

Study on Model Predictive Speed Control with Position Sensorless Control for PMSMs

Study on Model Predictive Speed Control with Position Sensorless Control for PMSMs

Multi-input model predictive speed control of lean-burn natural gas engine in range-extended electric vehicles

This study presents a multi-input model predictive speed control of lean-burn natural gas engine in a range extender to improve the responsiveness and anti-disturbance ability of the system. Lean combustion is recognized as an effective strategy to improve the economy and emission performance of the natural gas engine. However, the engine torque is insufficient compared with a normal combustion mode, which will result in a degraded anti-disturbance and low response performance in an engine speed tracking system in the range extender. In order to improve the performance of the lean-burn gas engine speed system, the air-fuel ratio (AFR) additional torque is introduced as another input of the system besides the throttle valve to increase the engine transient output torque, where the AFR additional torque is produced by setting AFR deviation from the nominal value. A multi-input model predictive control (MPC) controller is designed to handle the multiple inputs and constraints, guaranteeing that the AFR returns to the nominal value at steady state for efficiency and emissions, and an improved fast MPC algorithm is proposed to reduce the computation effort of the MPC strategy. Moreover, the proposed controller is evaluated by co-simulations on the platform of Matlab/Simulink with GT-Power and experiments on a range extender.

An Advanced Angular Velocity Error Prediction Horizon Self-Tuning Nonlinear Model Predictive Speed Control Strategy for PMSM System

In nonlinear model predictive control (NMPC), higher accuracy can be obtained with a shorter prediction horizon in steady-state, better dynamics can be obtained with a longer prediction horizon in a transient state, and calculation burden is proportional to the prediction horizon which is usually pre-selected as a constant according to dynamics of the system with NMPC. The minimum calculation and prediction accuracy are hard to ensure for all operating states. This can be improved by an online changing prediction horizon. A nonlinear model predictive speed control (NMPSC) with advanced angular velocity error (AAVE) prediction horizon self-tuning method has been proposed in which the prediction horizon is improved as a discrete-time integer variable and can be adjusted during each sampling period. A permanent magnet synchronous motor (PMSM) rotor position control system with the proposed strategy is accomplished. Tracking performances including rotor position Integral of Time-weighted Absolute value of the Error (ITAE), the maximal delay time, and static error are improved about 15.033%, 23.077%, and 10.294% respectively comparing with the conventional NMPSC strategy with a certain prediction horizon. Better disturbance resisting performance, lower weighting factor sensitivities, and higher servo stiffness are achieved. Simulation and experimental results are given to demonstrate the effectiveness and correctness.