Steady-state Control Performance Research Articles

Prescribed Performance Active Braking Control with Reference Adaptation for High-Speed Trains

Active braking control systems are vital for the safety of high-speed trains by leading the train operation at its maximum adhesion state. The train adhesion is a nonlinear function of the slip ratio and varies with the uncertain wheel-rail contact conditions. A nonlinear active braking control with rapid and accurate tracking performance is highly required for train braking systems. This paper proposes a novel prescribed performance active braking control with reference adaptation to obtain the maximum adhesion force. The developed feedback linearization controller employs a prescribed performance function that specifies the convergence rate, steady-state error, and maximum overshoot to ensure the transient and steady-state control performance. Furthermore, in the designed control approach, a continuous-time unscented Kalman filter is introduced to estimate the uncertainty of wheel-rail adhesion. The estimation is utilized to represent uncertainty and compensate for the prescribed performance control law. Finally, based on the estimated wheel-rail adhesion, an on-line optimal slip ratio generation algorithm is proposed for the adaptation of the reference wheel slip. The stability of the system is provided, and experiment results validate the effectiveness of the proposed method.

Composite Vector Model Predictive Control With Time-Varying Control Period for PMSM Drives

In this article, a composite vector model predictive control (MPC) with a time-varying control period for the permanent magnet synchronous motor (PMSM) drives is proposed. First, the conventional vector selection modes in the MPC method are analyzed. Then, a novel selection mode that selects two nonzero-voltage vectors as optimal voltage vector and has a time-varying control period is developed. Different from the conventional methods, this proposed method can adjust the control period to adapt to the reference voltage vector that is outside the linear modulation range and improve the steady-state control performance. Compared with the conventional three-vector method, this method has the advantages of reducing the switching frequency and suppressing the common-mode voltage due to the reduction use of the zero-voltage vector. Finally, experimental comparison results of different methods validate the effectiveness of the proposed method.

Practical Control Performance Index with Weighting Factors of Controller

MV‐index that quantitatively evaluates the steady state control performance is a method of setting the minimum variance as a benchmark, and it is widely used mainly for process control. However, since the minimum variance is intended to make the output of the dead time ahead coincide with the reference signal, realizing this is not realistic. This research proposes a new control performance index considering with the weighting factors of controller. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

PD-Based Optimal ADRC with Improved Linear Extended State Observer.

Taking dead-zone nonlinearlity and external disturbances into account, an active disturbance rejection optimal controller based on a proportional-derivative (PD) control law is proposed by connecting the proportional-integral-derivative (PID) control, the active disturbance rejection control (ADRC) and particle swarm optimization (PSO), with the purpose of providing an efficient and practical technology, and improving the dynamic and steady-state control performances. Firstly, in order to eliminate the negative effects of the dead-zone, a class of 2-order typical single-input single-out system model is established after compensating the dead-zone. Following that, PD control law is introduced to replace the state error feedback control law in ADRC to simplify the control design. By analyzing the characteristics of the traditional linear extended state observer, an improved linear extended state observer is designed, with the purpose of improving the estimation performance of disturbances. Moreover, employing PSO with a designed objective function to optimize parameters of controller to improve control performance. Finally, ten comparative experiments are carried out to verify the effectiveness and superiority of the proposed controller.

Duty VV-MPTC for Post-Fault Eight Switch Three-Phase Inverter Fed Induction Motor Drives With Reduced Neutral Point Voltage Fluctuation

This article proposes a novel duty ratio regulated virtual voltage vector (VV) model predictive torque control (MPTC) strategy for post-fault eight switch three-phase inverter fed induction motor drive with reduced neutral point (NP) voltage fluctuation. The proposed method contributes to substantially improve the steady-state torque control performance under post-fault conditions. Furthermore, compared with the conventional MPTC, it reduces the torque ripples as well as the NP voltage fluctuations. The mathematical model of eight-switch three-phase post-fault inverter fed induction motor is presented. Based on this, the synthesis of virtual VV is analyzed. Simulation and experimental results are presented to validate the effectiveness of the proposed method.

Model Predictive Control for PMSM Drives With Variable Dead-Zone Time

A double vector model predictive control (DV-MPC) with variable dead-zone time is proposed in this article, in order to improve the current steady-state control performance. It breaks the default rule of fixed dead-zone time in the traditional method and equates dead-zone to a voltage vector with adjustable action time. First, the dead-zone effect is analyzed in DV-MPC, and the judgment principle of dead-zone voltage vector is introduced. Then, the dead-zone duration of each control period is calculated and allocated, and the optimal combination of voltage vector and action time is selected by cost function. Finally, experimental results show that the proposed method can not only reduce the current harmonic content, but also reduce the switching frequency, compared with conventional method.

Simplified model predictive current control strategy for open-winding permanent magnet synchronous motor drives

For a semi-controlled open-winding permanent magnet synchronous motor, to improve the steady-state control performance of the traditional predictive control system and to concurrently lessen the computation burden, a novel model predictive current control strategy is proposed in this paper. First, to achieve simultaneous control of the d-axis current, q-axis current, and current error-based cost functions. Then, to further reduce the calculation burden and complexity of the prediction algorithm, the predicted voltage of the converter is used as the reference voltage. In addition, the judgment of the sector where the reference voltage is located, and the selection method of the voltage vector are also given. Finally, the effectiveness of the proposed prediction method is tested by the simulation and experimental results.

Flux-Trajectory-Optimization-Based Predictive Flux Control of Permanent Magnet Synchronous Machines

This article proposed a predictive flux control strategy for permanent magnet synchronous machines based on flux trajectory optimization. Instead of minimizing the instantaneous flux tracking error at the end of each sampling period, the effect of different control sets on flux trajectories within the entire sampling interval is taken into account in the cost function. As a result, steady-state torque and flux ripples can be suppressed. Besides, inaccurate sector determination found in the conventional predictive control strategies can be successfully avoided. As well as highly dynamic performances inheriting from the predictive characteristics, enhanced steady-state torque control performances can be guaranteed under different operating conditions. Although the extended control set is utilized to augment the control freedom and further reduce torque ripple, the control effect of the proposed algorithm does not rely on the increase of switching frequency. Compared with the traditional predictive control strategies based on either finite control set or an extended control set, a superior control effect can be obtained while keeping a low switching frequency. Moreover, duty cycle signals are directly obtained in the prediction process, and no additional space vector pulsewidth modulation method is needed. Experimental validations and comprehensive comparison are conducted to demonstrate the effectiveness of the proposed strategy.

3-D Vector-Based Model Predictive Current Control for Open-End Winding PMSG System With Zero-Sequence Current Suppression

In this article, 3-D vector-based model predictive current control (MPCC) for the open-end winding permanent magnet synchronous generator (OEW-PMSG) system driven by a dual inverter with a common dc bus is proposed to suppress the zero-sequence current. First, the 3-D voltage vector distribution of this system is analyzed and their zero-sequence voltages are investigated. Then, in order to reduce the computation burden of prediction control caused by vector enumeration, an improved current prediction control is developed, in which candidate voltage vectors for the dual inverter are determined according to the position of the zero-sequence reference voltage vector and the sector location of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula> -axis reference voltage vector. Moreover, to further improve the steady-state control performance of the whole generation system, a separate vector-selection method for the dual inverter is proposed. In this method, the first inverter applies one voltage vector in a control period and the voltage vector of the second inverter is synthesized by two vectors and selected in 3-D space. Finally, experiments are carried out to validate proposed methods, and the comparison experiments indicate that the proposed method has better control performance compared to conventional methods.

Improved Back-Stepping Control for Nonlinear Small UAV Systems With Transient Prescribed Performance Design

This paper proposes an improved back-stepping control approach and its application to small nonlinear UAV control systems with uncertainties such as external disturbance. Unlike traditional back-stepping control methods, the idea of prescribed performance function (PPF) is incorporated into the control design, such that both the transient and steady-state control performance can be strictly guaranteed. Moreover, we design a novel tracking differentiator to avoid the “differential expansion” problem well caused by the calculation of derivative. Significantly, the function approximators (e.g. neural networks) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. Finally, the numerical simulation verifies the convergence and robustness of the system, and the results show that the control strategy can obtain better transient and steady-state performance.

Predictive Control of Five-leg Inverter-Double Permanent Magnet Synchronous Motor System with Error Feedback Model

In this paper, the five-leg inverter-dual permanent magnet synchronous motor system is taken as the research object. To solve the problem of large steady-state speed fluctuation when applying the traditional model predictive control strategy, based on the mathematical models of the five-leg inverter and the dual permanent magnet synchronous motor, the causes of the fluctuation are analyzed, and then an improved model predictive control algorithm with error feedback is proposed. The algorithm makes full use of the high-speed operation ability of modern digital processor, and introduces the prediction value of error feedback correction model, so that the rolling optimization is not only based on the model, but also makes full use of the actual value of the feedback state variables. At the same time, the second-order Euler discrete method is used to further improve the model progress. Simulation and experimental results show that the model predictive control strategy with error feedback can effectively improve the steady-state speed control performance of the system while maintaining the dynamic performance of the original system and the decoupling control effect of the two motors.

An overview of prescribed performance control and its application to spacecraft attitude system

High-quality control method is of great importance for the attitude determination and maneuvering of spacecraft in the on-orbit servicing technology. Prescribed performance control methodology, as a potential way, has gained considerable attention due to its quantitative description for the transient and steady-state control performance in recent years. Thus, this article constructs a survey for the prescribed performance control methodology along with the detailed analysis of the attitude control methods in the existing reported works. Then, the basic structure of prescribed performance control methodology is discussed in theory, and application to the attitude control of postcapture spacecraft works as an example to further explain the procedure of prescribed performance control methodology. Finally, with consideration of some vital aerospace applications, potential open issues of the prescribed performance control methodology are analyzed.

Model predictive current control method for PMSM drives based on an improved prediction model

In motor drive systems based on model predictive control, a mathematic model of the motor is used to predict the future behavior of the system. However, the parameters in the motor model may not match their real values since these parameters may vary under different operation conditions. All parameter variations result in inaccurate predictions, and influence the steady-state control performance of the whole control system. In this paper, an improved model predictive control method is presented. Firstly, when parameter mismatches exist, the sources of the current prediction error are analyzed. It is revealed that current prediction error is directly affected by a prediction model with parameter mismatches and inaccurate one-step delay compensation. In particular, the influence form one-step delay compensation is discussed in this paper. Then a reaching-law-based sliding mode discrete observer is introduced to implement accurate one-step delay compensation and to observe all parameter variations. Finally, a predictive control method combined with sliding-mode discrete observation is presented to reduce parameter sensitivity. Simulation and experimental results show that the proposed method can increase the robustness of model predictive control systems.

Optimized Model Predictive Control With Dead-Time Voltage Vector for PMSM Drives

In order to improve the current steady-state control performance of the model predictive control (MPC), a dead-time voltage-vector based MPC method is proposed in this article. First, the effect of the dead-time on MPC is introduced, and the formation process of the dead-time voltage vector existed in MPC is analyzed. Furthermore, the beneficial dead-time voltage vector and nonbeneficial dead-time voltage vector for the current steady-state control performance of MPC is distinguished, and the advantage of the beneficial dead-time voltage vector is analyzed. Then, the MPC method based on dead-time voltage vector is presented, which optimizes the action time of dead-time. Finally, the experimental results prove that the current steady-state control performance of the proposed MPC method is better than the conventional MPC method without increasing the switching frequency.

Learning impedance control of robots with enhanced transient and steady-state control performances

This study proposes a learning impedance controller comprising a proportional feedback control term, a composite-learning-based uncertainty estimation term, and a robot-environment interaction control term. The impedance control problem is converted into a particular reference-trajectory tracking problem based on a generated reference trajectory. The proposed controller ensures the exponential convergence of the auxiliary tracking error and the uncertainty estimation error. The interaction control term improves the transient control performance through suppression/encouragement of the incorrect/correct robot movements. The composite-learning update law enhances the transient and steady-state control performances based on the exponential convergence of the uncertainty estimation error and auxiliary tracking error. Finally, the effectiveness and advantages of the proposed impedance controller are validated by theoretical analysis and simulations on a parallel robot.

Prescribed performance fault tolerant control for uncertain nonlinear systems with input saturation

This paper investigates the problem of adaptive fault tolerant tracking control for uncertain nonlinear systems in the event of external disturbances, actuator faults and input saturation. In order to guarantee the transient and steady-state control performance, the prescribed performance function is employed to impose the anticipant performance criterion on the output tracking error. By using the error transformation, the original inequality error constraint is converted to an unconstrained issue. For the conversion systems, the negative effect resulting from the external disturbances is restrained via the adaptive control approach. The radial basis function neural network (RBFNN) is adopted to handle the unknown nonlinear functions. The Nussbaum gain technique is utilised to deal with the nonlinear terms arising from the actuator faults and input saturation. Combining with the backstepping control strategy, an adaptive neural fault tolerant control (FTC) scheme is developed for the uncertain nonlinear systems. It is rigorously proved that the proposed controller is capable of ensuring the boundness of all closed-loop signals as well as the specified tracking performance. Simulation results on two examples are given to illustrate the effectiveness of the presented control approach.

Vector-Based Model Predictive Hysteresis Current Control for Asynchronous Motor

Model predictive control, especially model predictive current control, has received a great deal of attention in the motor drive field in recent years, due to its ability to render fast dynamic response and to handle multiple variables, nonlinearities, and system constraints in an intuitive way. However, the conventional single-vector-based model predictive hysteresis current control brings about some problems, such as high sampling frequency and poor steady-state control performance. In this paper, a novel double-vector-based model predictive hysteresis current control (DV-MPHCC), which utilizes an arbitrary vector and a zero vector to form a voltage vector combination, is proposed. To verify the improved performance over a short predictive horizon, a series of comparisons are conducted between the two control algorithms by simulation and experiment. Both simulation and experiment results validate that the DV-MPHCC proposed has advanced steady-state control performance, and a lower sampling frequency.

Vector Shifted Model Predictive Power Control of Three-Level Neutral-Point-Clamped Rectifiers

Model predictive power control (MPPC) has been emerging as one of the most promising control schemes for three-level neutral-point-clamped (NPC) rectifiers. However, conventional MPPC (C-MPPC), which only selects one switching state during the entire sampling period, leads to high active and reactive power ripples. Moreover, the heavy computational burden and variable switching frequency limit the applications of MPPC. In this article, vector shifted MPPC (VS-MPPC) methods are investigated. With the shifted vectors, the constant-switching-frequency MPPC of three-level NPC rectifiers can be simplified as that of a two-level rectifier, and the balanced neutral capacitor voltage can be easily achieved by adjusting the duty cycle of the redundant switches without any weighting factor employed. Only eight voltage vectors are calculated and shifted based on the small hexagon selection. Consequently, the computational burden is significantly reduced, even 35% less than that of C-MPPC. Furthermore, the proposed VS-MPPC presents a constant switching frequency and better steady-state control performance without evaluating all the switching states. Simulation and experimental evaluations of the proposed VS-MPPC methods with C-MPPC have been conducted to validate the superiority of the proposed VS-MPPC methods.

Model Predictive Control of the Open-winding PMSG System Based on Three-Dimensional Reference Voltage-Vector

For the open-winding permanent-magnet synchronous generator system with a common dc bus, in order to improve the steady-state control performance of conventional prediction control and simultaneously reduce the calculation burden, a zero-sequence current suppression method based on the prediction of three-dimensional reference voltage is proposed in this article. First, the voltage vector used by prediction control is extended from the plane to the three-dimensional (3-D) space. The voltage vectors of two converters are analyzed and predicted under the 3-D space. Moreover, to improve steady-state performance, two voltage vectors are applied in one control period, and the voltage selection principle that determines candidate vectors is presented. Finally, experimental results test the effectiveness of this method.

Control Design of LCL Type Grid-Connected Inverter Based on State Feedback Linearization

Control strategy is the key technology of power electronic converter equipment. In order to solve the problem of controller design, a general design method is presented in this paper, which is more convenient to use computer machine learning and provides design rules for high-order power electronic system. With the higher order system Lie derivative, the nonlinear system is mapped to a controllable standard type, and then classical linear system control method is adopted to design the controller. The simulation and experimental results show that the two controllers have good steady-state control performance and dynamic response performance.