Robust Petri Fuzzy-Neural-Network Control for Linear Induction Motor Drive

This study focuses on the development of a robust Petri-fuzzy-neural-network (PFNN) control strategy applied to a linear induction motor (LIM) drive for periodic motion. Based on the concept of the nonlinear state feedback theory, a feedback linearization control (FLC) system is first adopted in order to decouple the thrust force and the flux amplitude of the LIM. However, particular system information is required in the FLC system so that the corresponding control performance is influenced seriously by system uncertainties. Hence, to increase the robustness of the LIM drive for high-performance applications, a robust PFNN control system is investigated based on the model-free control design to retain the decoupled control characteristic of the FLC system. The adaptive tuning algorithms for network parameters are derived in the sense of the Lyapunov stability theorem, such that the stability of the control system can be guaranteed under the occurrence of system uncertainties. The effectiveness of the proposed control scheme is verified by both numerical simulations and experimental results, and the salient merits are indicated in comparison with the FLC system     

FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

Development of Lyapunov-Based Genetic Algorithm Control for Linear Piezoelectric Ceramic Motor Drive

In general, the role of genetic algorithm (GA) is operated offline as a minor compensator or tuner in the control engineering because the systematic design and the latent stability problem of a GA-based control scheme are required to be solved. This paper originally designs a Lyapunov-based GA control (LGAC) scheme, and it applies for a practical control engineering example of the online motion control of a linear piezoelectric ceramic motor driven by a hybrid resonant inverter. In this control scheme, a GA control system via backstepping design technique is utilized to be the major controller, and adaptation laws derived from Lyapunov stability analyses are manipulated to adjust appropriate evolutionary steps. As a result, the system stability can be guaranteed directly without strict constraint conditions and detailed system knowledge. The effectiveness of the proposed drive and control system is verified by experimental results in the presence of uncertainties. From the measured results, the LGAC system performs superior high-precision motion control under wide operation range than conventional backstepping control system.     

A Hybrid-Type Variable-Structure Instantaneous Torque Control With a Robust Adaptive Torque Observer for a High-Performance Direct-Drive PMSM

This paper describes a novel instantaneous torque control scheme for a high-performance direct-drive permanent-magnet synchronous motor. The scheme consists of a robust adaptive instantaneous torque observer and a hybrid-type variable-structure instantaneous torque controller. First, to robustly obtain the instantaneous electromagnetic torque information, a robust adaptive torque observer is designed by considering all possible current model uncertainties. The observation gains and uncertainties prediction rules are derived in the sense of Lyapunov theory so that the stability of the proposed estimation scheme is fulfilled. Second, to ensure perfect tracking of the output torque and providing means in eliminating torque ripples, the frequency modes of the disturbances to be eliminated should be included in the stable closed-loop system. To achieve this objective, a hybrid-type variable-structure controller with internal model, for the flux harmonics and system uncertainties, is adopted. The hybrid controller shows better disturbance rejection without control chattering. Comparative evaluation results are presented to demonstrate the validity and effectiveness of the proposed instantaneous torque control scheme.     

A Permanent Magnet Linear Synchronous Motor Drive for HTS Maglev Transportation Systems

A permanent magnet linear synchronous motor （PMLSM） for a high temperature superconducting （HTS） maglev system has been studied, including the motor structure, control strategy, and analysis techniques. Finite element analysis （FEA） of magnetic field is conducted to accurately calculate major motor parameters. Equivalent electrical circuit is used to predict the drive＇s steady-state characteristics, and a phase variable model is applied to predict the dynamic performance. Preliminary experiment with a prototype has been made to verify the theoretical analysis and the HTS-PM synchronous driving technology.     

具有输入饱和的纯反馈系统有限时间跟踪控制

针对具有输入饱和及输出约束的非线性纯反馈系统,提出了有限时间自适应神经跟踪控制方法.利用有限时间控制理论、Barrier Lyapunov函数以及径向基函数(RBF)神经网络设计出新颖的虚拟和实际输入信号,解决了具有输入饱和及输出约束的非线性纯反馈系统的有限时间控制问题,同时,确保系统在满足输入饱和及输出约束的条件下,...     

Adaptive Vehicle Lateral-Plane Motion Control Using Optimal Tire Friction Forces With Saturation Limits Consideration

This paper presents an adaptive nonlinear control scheme aimed at the improvement of the handling properties of vehicles. The control inputs for steering intervention are the steering angle and wheel torque for each wheel, i.e., two control inputs for each wheel. The control laws are obtained from a nonlinear 7-degree-of-freedom (DOF) vehicle model. A main loop and eight cascade loops are the basic components of the integrated control system. In the main loop, tire friction forces are manipulated with the aim of canceling the nonlinearities in a way that the error dynamics of the feedback linearized system has sufficient degrees of exponential stability; meanwhile, the saturation limits of tires and the bandwidth of the actuators in the inner loops are taken into account. A modified inverse tire model is constructed to transform the desired tire friction forces to the desired wheel slip and sideslip angle. In the next step, these desired values, which are considered as setpoints, are tackled through the use of the inner loops with guaranteed tracking performance. The vehicle mass and mass moment of inertia, as unknown parameters, are estimated through parameter adaptation laws. The stability and error convergence of the integrated control system in the presence of the uncertain parameters, which is a very essential feature for the active safety means, is guaranteed by utilizing a Lyapunov function. Computer simulations, using a nonlinear 14-DOF vehicle model, are provided to demonstrate the desired tracking performance of the proposed control approach.     

一类不确定非线性系统变结构自适应鲁棒控制

本文针对一类不确定非线性系统,在假设系统模型参数和不确定性界都未知的情况下,提出了一个变结构自适应鲁棒控制算法,该算法在确保闭环系统稳定的前提下,在线对系统模型参数和不确定性界进行估计.最后以船舶减摇鳍非线性控制系统为例,进行了变结构鲁棒自适应控制器的设计,经仿真研究表明,所提出的算法是有效的.     

Control Synthesis of Discrete-Time Switched Linear Systems with Input Saturation Based on Minimum Dwell Time Approach

This paper investigates the control synthesis problem for discrete-time switched linear systems with input saturation. Based on minimal dwell time approach, state feedback controller and dynamic output feedback controller are respectively designed in terms of LMIs. The corresponding optimization problems are formulated to obtain a bigger attractive region. Compared with previous results, our proposed results can recover part of attractive region. Finally, numerical example is presented to illustrate the effectiveness and value of our obtained results.     

直线电机精密工作台运动控制器设计

为了提高精密工作台的轨迹跟踪精度和动态响应性能,基于辨识出的控制对象离散化模型,利用极点配置方法设计了精密工作台运动控制器的前馈环节和反馈环节,构成具有两自由度结构的精密工作台运动控制系统.通过实验,与PD＋加速度前馈的控制方式相比较,精密工作台静态定位误差提高了0.5 μm;当精密工作台以120 mm/s匀速运动时,轨迹跟踪精度提高了3 μm;定位建立时间缩短了10 ms.结果表明,采用极点配置方法设计的运动控制器具有较好的动态响应和轨迹跟踪性能.     

Dynamic Anti-Windup Control Design for Markovian Jump Delayed Systems with Input Saturation

This paper deals with the problem of robust stabilization for Markovian jump delayed systems with partially known transition rates subject to input saturation. The problem we address is the design of dynamic anti-windup compensators, which guarantee that the resulting closed-loop constrained systems are robustly mean-square stable. By employing local sector conditions and an appropriate Lyapunov-Krasovskii function, some sufficient conditions for the solution to this problem are derived in terms of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness of proposed method.     

交流电动机矢量控制变压变频调速系统（八） 第八讲 双馈异步电动机矢量控制系统

本文首先介绍了双馈异步电动机调速系统的基本结构，在此基础上，介绍了异步电动机双馈矢量控制系统的基本原理，并且对气隙磁链的观测方法进行了介绍，最后，介绍了一种新型的双馈电饥矢量控制实现方法。     

Adaptive Control with Guaranteed Contraction Rate for Systems with Actuator Saturation

The problem of adaptive control of linear discrete-time systems with actuator saturation and unknown parameters is investigated. A novel optimal control method is presented first for linear systems with known parameters and constant actuator saturation by introducing a Lyapunov function and a performance cost function that are both dependent on a contraction rate parameter. Based on the obtained guaranteed contraction-rate control method, an adaptive control algorithm is derived for systems containing unknown system parameters and time-varying actuator saturation. To show that the closed-loop system is stable and that the adaptive control algorithm is convergent, the Lyapunov function is supplemented by an additional part defined by the trace of a quadratic function of the controller gain. The effectiveness and potential of the presented method is demonstrated by a numerical example.     

A Repetitive Model Predictive Control Approach for Precision Tracking of a Linear Motion System

In this paper, a new model predictive control (MPC) approach suitable for high precision linear motion drive operating with repetitive tracking tasks is presented. For the proposed predictive controller, the feedforward controller of the conventional MPC has been modified to provide zero-phase learning property. This is achieved by augmenting the reference trajectory with a phase-compensated term that is updated with the historical tracking error. The proposed approach attempts to combine the merits of both the conventional MPC and repetitive control schemes. Experimental results have demonstrated that the system effectively reduces the tracking error from the periodic disturbance caused by the friction. Its performance under varying reference conditions and different loadings shows that the system is robust.      

多小区STBC系统中基于自干扰抵消的鲁棒线性接收算法

在多小区有未知共道干扰存在的STBC系统中,针对已有Capon类线性接收算法在有限样本情况下会造成STBC解码符号间自干扰的问题,该文提出了一种鲁棒的线性接收算法。该算法结合STBC的正交结构,首先通过正交投影的方法去除同一码字各符号间的自干扰,然后再利用Capon接收算法结构进行未知干扰的抑制。仿真结果表明,结合STBC正交结构和Capon接收结构的线性接收算法比传统的Capon接收算法有更好的干扰抑制性能。     

Quantized Output Feedback Control of Uncertain Discrete-Time Systems with Input Saturation

This paper deals with the problem of quantized output feedback control for uncertain discrete-time systems with input saturation. Input quantization case and output quantization case are studied, respectively. The purpose of the study was to design of dynamic output feedback controllers such that all the trajectories of the closed-loop system converge to a small ellipsoid for every initial condition starting from large admissible domain. By solving the optimization problem, the corresponding domains can be obtained. Finally, two simulation examples are provided to demonstrate the applicability of the proposed approach.     

High-Performance Control Strategy of Cycloconverter-Fed Induction Motor Drive System Based on Digital Control Theory

The field-oriented control, or the vector control, has emerged as a powerful tool for controlling squirrel-cage induction motors. The dynamic performance, however, depends on the controllability of the stator currents.     

Gain Scheduled State Feedback Control for Discrete-Time-Varying Polytopic Systems Subject to Input Saturation

In this paper, we study the stability and performance optimization problem of discrete-time-varying polytopic systems with actuator saturation. First, the homogeneous polynomially parameter-dependent matrix function (HPP-DMF for abbreviation) is proposed. Then, a set invariance condition is obtained by applying the HPP-DMF to the Lyapunov function and gain scheduled control law. Based on the set invariance condition, an algorithm is proposed to minimize the worst-case performance. The conditions in the algorithm are expressed in terms of linear matrix inequalities (LMIs). In addition, it is shown that as the degree of the HPP-DMF increases the conditions become less conservative. A numerical example is presented to demonstrate the effectiveness and superiority of the proposed technique.     

Desired Compensation Adaptive Robust Control of a Linear-Motor-Driven Precision Industrial Gantry With Improved Cogging Force Compensation

This paper proposes a new model for cogging forces of linear motor systems. Sinusoidal functions of positions are used to effectively capture the largely periodic nature of cogging forces with respect to position, while B-spline functions are employed to account for the additional aperiodic part of cogging forces. This model is experimentally demonstrated to be able to capture both the periodic and nonperiodic characteristics of cogging force while having a linear parametrization form, which makes the online adaptive compensation of cogging forces possible and effective. A discontinuous-projection-based desired compensation adaptive robust controller (DCARC) is then constructed, which makes full use of the proposed cogging force model for an improved cogging force compensation. Comparative experimental results with various cogging force compensations are obtained on both axes of a linear-motor-driven industrial gantry. The results show that DCARC with the proposed model compensation achieves the best tracking performance among all the three algorithms tested, validating the proposed cogging force model. The excellent tracking performances obtained in the experiments also verify the effectiveness of the proposed ARC control algorithms in practical applications. The proposed model and control algorithm can be applied for other types of motor control systems as well.      

一种改进的非线性MIMO系统鲁棒自适应反推控制

针对一类具有模型不确定性和未知外界干扰的严反馈非线性MIMO系统,提出一种基于RBF神经网络和反推控制的鲁棒控制律设计方法.应用RBF神经网络在线逼近模型的不确定性,引入低通滤波器消除反推设计方法中由于对虚拟控制反复求导而导致的复杂性问题.同时,在控制律设计中引入一个自适应鲁棒控制项来补偿神经网络逼近误差和未知外界干扰的影响,提高系统的鲁棒性,使整个系统获得更好的跟踪控制性能.基于Lyapunov稳定性定理证明了闭环系统的所有信号半全局一致终结有界;通过适当选择设计参数及初始化误差变量,跟踪误差可收敛到原点的一个任意小邻域内,且跟踪误差的L∞跟踪性能被保证.数值仿真验证了方法的有效性.