Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance.     

Optimal controller design for a matrix converter based surface mounted PMSM drive system

This paper proposes a new control algorithm for a matrix converter permanent magnet synchronous motor (PMSM) drive system. First, a new switching strategy, which applies a backpropagation neural network to adjust a pseudo DC bus voltage, is proposed to reduce the current harmonics of the permanent magnet synchronous motor. Next, a two-degree-of-freedom controller is proposed to improve the system performance. The parameters of this controller are obtained by using a frequency-domain optimization technique. The controller design algorithm can be applied in an adjustable speed control system and a position control system to obtain good transient responses and good load disturbance rejection abilities. The controller design procedures require only algebraic computation. The implementation of this kind of controller is only possible by using a high-speed digital signal processor. In this paper, all the control loops, including current-loop, speed-loop, and position-loop, are implemented by a 32-b TMS320C40 digital signal processor. The hardware, therefore, is very simple. Several experimental results are shown to validate the theoretical analysis.     

Adaptive Fuzzy Control of Nonlinear in Parameters Uncertain Chaotic Systems Using Improved Speed Gradient Method

This paper presents an adaptive fuzzy controller for Nonlinear in Parameters (NLP) chaotic systems with parametric uncertainties. In the proposed controller, the unknown parameters are estimated by the novel Improved Speed Gradient (ISG) method, which is a modification of Speed Gradient (SG) algorithm. ISG employs the Lagrangian of two suitable objective functionals for on-line estimation of system parameters. The most significant advantage of ISG is that it is applicable to NLP systems and it results in a faster rate of convergence for the estimated parameters than the SG method. Estimated parameters are used to design the fuzzy controller and to calculate the Lyapunov exponents of the chaotic system adaptively. Furthermore, established on the well-known Takagi–Sugeno (T-S) fuzzy model, a LMI (Linear Matrix Inequality)-based fuzzy controller is designed and is tuned using estimated parameters and Lyapunov exponents. Throughout the controller design procedure, several important issues in fuzzy control theory including relaxed stability analysis, control input performance specifications, and optimality are taken into account. Combination of ISG parameter estimation method and T-S-based fuzzy controller yields an adaptive fuzzy controller capable to suppress uncertainties in parameters and initial states of NLP chaotic systems. Finally, simulation results are provided to show the effectiveness of the ISG and adaptive fuzzy controller on chaotic Lorenz system and Duffing oscillator.     

圆柱-球体三自由度超声电机模糊自适应控制

针对圆柱-球体三自由度超声电机难以建立精确数学模型的特点,设计了模糊自适应定位控制系统。该控制系统由模糊控制器和在线参数自调整环节构成,在线参数自调整环节使该系统的动态特性、稳态性能更好地兼顾,克服了固定量化因子控制性能不理想的缺点。应用该控制器,实现了电机精确定位控制。结果表明,参数自调整模糊自适应控制器的性能优于传统PID控制器及固定量化因子的模糊控制器,得到了满意的控制效果。     

Adaptive speed control for induction motor drives using neuralnetworks

In this paper, the adaptive speed control of induction motor drives using neural networks is presented. To obtain good tracking and regulating control characteristics, a digital two-degree-of-freedom (2DOF) controller is adopted and a design procedure is developed for systematically finding its parameters according to prescribed specifications. The parameters of the controller corresponding to various drive parameter sets are found off-line and used as the training patterns to estimate the connection weights of neural networks, Under normal operation, the true drive parameters are real-time identified and they are converted into the controller parameters through multilayer forward computation by neural networks. The parameters of the 2DOF controller can be adapted to match the desired specifications under various operating conditions     

基于DSP和FPGA的望远镜伺服控制系统设计

针对交流永磁同步电机驱动的大型望远镜的高精度、低速平稳运行问题,研制了一套基于浮点数字信号处理器（DSP）和现场可编程逻辑门阵列（FPGA）的驱动控制器。该控制器以DSP 作为主控制器,FPGA 作为协控制器,主控制器完成控制算法、接受指令等功能,协控制器实现PWM 产生、电流采集、速度检测等功能。根据永磁同步电机矢量控制原理建立了永磁同步电机的数学模型,进行了永磁同步电机控制器的硬件设计;在硬件设计的基础上,采用自适应PI 对望远镜的低速控制性能进行了研究。实验结果表明:当望远镜以32.4 （）/s 匀速运行时,速度波动范围为0.648 （）/s;当对望远镜做最大速度为1（）/s,最大加速度为1（）/s2 的正弦引导时,最大引导误差为9.72 ,引导误差RMS 值为3.24 ;该驱动控制系统能够实现望远镜的低速平稳运行,满足大型望远镜伺服控制系统的性能要求。     

Fuzzy adaptive speed control of a permanent magnet synchronous motor

A fuzzy adaptive speed controller is proposed for a permanent magnet synchronous motor (PMSM). The proposed fuzzy adaptive speed regulator is insensitive to model parameter and load torque variations because it does not need any accurate knowledge about the motor parameter and load torque values. The stability of the proposed control system is also proven. The proposed adaptive speed regulator system is implemented by using a TMS320F28335 floating point DSP. Simulation and experimental results are presented to verify the effectiveness of the proposed fuzzy adaptive speed controller under uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.     

Fuzzy logic based on-line efficiency optimization control of anindirect vector-controlled induction motor drive

Improvement of adjustable speed drive system efficiency is important not only from the viewpoints of energy saving and cooling system operation, but also from the broad perspective of environmental pollution. The paper describes a fuzzy logic based on-line efficiency optimization control of a drive that uses an indirect vector controlled induction motor speed control system in the inner loop. At steady-state light-load condition, a fuzzy controller adaptively decrements the excitation current on the basis of measured input power such that, for a given load torque and speed, the drive settles down to the minimum input power, i.e., operates at maximum efficiency. The low-frequency pulsating torque due to decrementation of rotor flux is compensated in a feedforward manner. If the load torque or speed command changes, the efficiency search algorithm is abandoned and the rated flux is established to get the best transient response. The drive system with the proposed efficiency optimization controller has been simulated with lossy models of the converter and machine, and its performance has been thoroughly investigated. An experimental drive system with the proposed controller implemented on a TMS320C25 digital signal processor, has been tested in the laboratory to validate the theoretical development     

薄膜材料的绕组处理的适应失真压力的控制系统

薄膜材料的绕组处理是在一个高度非线性的动态系统中维持应力不变.提出一个为薄膜材料的绕组处理在不同摩擦锟供料速度下的适应模糊应力的控制系统.该提出的适应模糊应力的控制系统包括一个模糊应力控制器和一个适应调谐器.模糊应力控制器是主进度控制器,一个平移宽度的概念和变化模式技术被包括在模糊推论中以矫正模糊现象,而且只有一个参数因素需要被调整.为了对抗在实际应用中的不确定,一个失真压力的控制系统占据着简单控制框架,无震颤的,稳定跟踪性能和对不确定性的鲁棒的优势.与传统的比例积分应力控制方法相比较可提出的这种控制方法有显著的优势.     

Design and implementation of a high-performance field-orientedinduction motor drive

The design and implementation of a high-performance controller for a field-oriented induction motor drive is presented. Dynamic modeling based on the stochastic technique is performed. Based on the estimated drive model, a two-degree-of-freedom controller is proposed so good dynamic responses in both the speed tracking and regulation characteristics can be achieved. The parameters of the controller are found using a proposed systematic design procedure according to the prescribed specifications. Having designed and tested the performance of the controller by simulation, the hardware implementation is successfully made, and some experimental results are given to demonstrate the effectiveness of the proposed controller     

LQG/LTR control of an AC induction servo drive

A new design method based on the linear-quadratic-Gaussian with loop-transfer-recovery (LQG/LTR) theory has been developed for the design of high performance AC induction servomotor drives using microcomputer-based digital control. The principle of field orientation is employed to achieve the current decoupling control of an induction motor. An equivalent model representing the dynamics of the decoupled induction motor has been developed. Based on the developed model with specified parameter uncertainties and given performance specifications, a frequency domain loop-gain-shaping method based on the LQG/LTR theory is proposed for the design of the servo loop controller. A microcomputer-based induction servomotor drive has been constructed to verify the proposed control scheme. Simulation and experimental results are given to illustrate the effectiveness of the proposed design method     

A nonlinear state observer for the sensorless control of apermanent-magnet AC machine

This paper presents a sensorless speed regulation scheme for a permanent-magnet synchronous motor (PMSM) based solely on the motor line currents measurements. The proposed scheme combines an exact linearization-based controller with a nonlinear state observer which estimates the rotor position and speed. Moreover, the stability of the closed-loop system, including the observer, is demonstrated through Lyapunov stability theory. The proposed observer has the advantage of being insensitive to rotation direction. It is shown how a singularity at zero velocity appears in the scheme and how it can be avoided by switching smoothly from the observer-based closed-loop control to an open-loop control at low velocity. The system performance is tested with an experimental setup consisting of a PMSM servo drive and a digital-signal-processor-based controller for both unidirectional and bidirectional speed regulation     

A supervisory fuzzy neural network controller for slider-crank mechanism

A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The supervisory FNN controller comprises a sliding mode FNN controller and a supervisory controller. The sliding mode FNN controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance.     

Evaluating generalized predictive control for a brushless DC drive

This paper proposes a new control approach for a brushless DC motor drive using the generalized predictive control (GPC) algorithm. Based on the same least-squares framework as in the controller design, we further develop the method to design an observer. The GPC algorithm uses the receding horizon approach whereby the control signals are determined by minimizing a quadratic cost function. Our study shows that the rise time and settling time of the servo system have an approximate linear relationship with the prediction horizon. Thus, it is used to tune the controller of the drive. Moreover, the control weighting factor can be used to smooth the controller output. The proposed algorithm has been implemented using a digital signal processor (DSP) and tested in real time with a prototype system. The performance and robustness of the algorithms have been evaluated both in simulation and experiment. The results show that the drive performs reasonably well despite load changes and step changes in the position setpoint. Furthermore, it is fairly robust against motor parameters change     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

Mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine

This paper presents pragmatic techniques for mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine. Practical rules are proposed to select specifications of key mechatronic components in the hydraulic servo system, in order to efficiently construct an industry-level machine. With reasonable assumptions, a mathematical model of the injection speed control system is established and open-loop experimental data are then employed to validate the system model. By the model, a gain-scheduling PI controller and a fuzzy PI controller are presented, compared and then implemented into a digital signal processor (DSP) using standard C programming techniques. Experimental results are conducted to show that the two proposed controllers are capable of achieving satisfactory speed tracking performance. These developed techniques may provide useful references for engineers and practitioners attempting to design pragmatic, low-cost but high-performance ultra high-injection speed controllers.     

Frequency Domain FIR Filter Design Using Fuzzy Adaptive Simulated Annealing

An alternative approach to digital filter design is presented. The overall technique is as follows: Starting from frequency domain constraints and a parameterized expression of the filter family under adaptation, a corresponding training set is created, an error function is synthesized and a global minimization process is executed. At the end, the point that minimizes globally the particular cost function at hand determines the optimal filter. The adopted numerical optimization algorithm is based upon the well-known simulated annealing paradigm and its implementation is known as fuzzy adaptive simulated annealing. Although it is used in this paper to fit FIR filters to frequency domain specifications, the method is suitable to application in other problems of digital filter design, where the matter under study can be stated as finding the global minimum of a numerical function of filter parameters. Design examples are shown to verify the effectiveness of the proposed approach.     

Robust control using neural network uncertainty observer for linearinduction motor servo drive

A robust controller, that combines the merits of integral-proportional (IP) position control and neural network (NN) observed technique, is designed for a linear induction motor (LIM) servo drive in this study. First, the secondary flux of the LIM is estimated using a sliding-mode flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust and the flux amplitude of the LIM. Then, the IP position controller is designed according to the estimated mover parameters to match the time-domain command tracking specifications. Moreover, a robust controller is formulated using the NN uncertainty observer, which is implemented to estimate the lumped uncertainty of the controlled plant, as an inner-loop force controller to increase the robustness of the LIM servo drive system. Furthermore, in the derivation of the online training algorithm of the NN, an error function is used in the Lyapunov function to avoid the real-time identification of the system Jacobian. In addition, to increase the speed and accuracy of the estimated flux, the sliding-mode flux observer is implemented using a 32 bit floating-point digital signal processor (DSP) with a high sampling rate. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results     

基于DSP的舰载搜索雷达稳定平台交流伺服系统的设计

给出了一种基于DSP的舰载搜索雷达交流伺服控制系统的设计方法。采用高速数字信号处理芯片TMS320LF2407A作控制主体，交流变频器和交流无刷电机作驱动，采用先进的数字PID控制算法，组成全数字交流伺服系统，保证系统具有很高的精度、可靠的稳定性和良好的可扩展性。仿真试验表明该设计满足舰载雷达伺服控制系统的要求。