Robust motion controller design for high-accuracy positioningsystems

This paper presents a controller structure for robust high speed and accuracy motion control systems. The overall control system consists of four elements: a friction compensator; a disturbance observer for the velocity loop; a position loop feedback controller; and a feedforward controller acting on the desired output. A parameter estimation technique coupled with friction compensation is used as the first step in the design process. The friction compensator is based on the experimental friction model and it compensates for unmodeled nonlinear friction. Stability of the closed-loop is provided by the feedback controller. The robust feedback controller based on the disturbance observer compensates for external disturbances and plant uncertainties. Precise tracking is achieved by the zero phase error tracking controller. Experimental results are presented to demonstrate performance improvement obtained by each element in the proposed robust control structure     

Robust H∞ control with multiple constraints forthe track-following system of an optical disk drive

In this paper, the authors design a tracking controller which satisfies transient response specifications and maintains tracking error within a tolerable limit for the uncertain track-following system of an optical disk drive. To this end, a robust H_∞ control problem, with regional stability constraints and sinusoidal disturbance rejection is considered. The internal model principle is used for rejecting the sinusoidal disturbance caused by eccentric rotation of the disk. The authors show that a condition satisfying the regional stability constraints can be expressed in terms of a linear matrix inequality (LMI) using the Lyapunov theory and S-procedure. Finally, a tracking controller is obtained by solving an LMI optimization problem involving two LMIs. The proposed controller design method is evaluated through an experiment     

Adaptive gain scheduling with feedforward control system based on system model for PMSM

A feedforward controller for permanent magnet synchronous motor (PMSM) has been proposed in this study, and proportional and integral gain could be self-adaptive under different operating conditions. The control structure used in the feedforward system is the same as in the feedback control system. This control structure could guarantee independence of the speed command input to output with the disturbance input to output, which makes the system have better reference trajectory tracking and disturbances rejection. In order to obtain optimal control performance when the parameters are uncertain, a gain scheduling adaptive controller is used in the feedforward system. The proposed controller has been verified by the experimental and simulation results with less steady-state error and better dynamic response than the controllers without it under the condition of external load torque disturbance and PMSM parameter uncertainties.     

Random vibration test control of inverter-fed electrodynamic shaker

The random vibration control of an inverter-fed electrodynamic shaker is presented in this paper. First, the dynamic model of the shaker is found and a current-controlled pulsewidth modulation inverter is designed and implemented. The feedback controller is augmented with a command feedforward controller and a disturbance feedforward controller to let the armature exciting current have low harmonic content and possess excellent waveform tracking performance. Then, an acceleration controller and its random vibration command are arranged. In the proposed acceleration control scheme, a command feedforward controller and a robust disturbance feedforward controller are also employed to let the shaker have close random acceleration command waveform tracking control performance, and the performance be insensitive to the system parameter variations. It follows that the acceleration control with desired frequency response in a vibration test could be achieved through properly setting the command signal. The effectiveness of the proposed control scheme is verified by simulation and measured results     

Dual-stage HDD head positioning using an H∞ almost disturbance decoupling controller and a tracking differentiator

We adopt in this paper a novel control scheme to achieve fast and accurate head positioning for dual-stage actuated hard disk drive (HDD) servo system with actuator saturation and disturbances. This control scheme consists of a tracking differentiator (TD) to avoid actuator saturation as large as possible and an H_∞ almost disturbance decoupling controller to deal with disturbances and improve the tracking performance. More specifically, the TD aims to provide a smooth reference signal in a feedforward way so as to reduce the system error, and further decrease the control inputs to both the VCM actuator and the micro-actuator such that the saturation problem can be effectively avoided. The H_∞ almost disturbance decoupling controller, when it is applied to control the micro-actuator, is able to almost decouple the disturbance and the controlled output such that satisfactory tracking performance can be achieved. Furthermore, the VCM actuator is controlled by a notch filter in series with a lead compensator so as to stabilize the servo loop. Finally, simulation results in time domain and frequency domain verify the effectiveness of the proposed control scheme.     

Adaptive positioning control for a LPMSM drive based on adapted inverse model and robust disturbance observer

The adaptive robust positioning control for a linear permanent magnet synchronous motor drive based on adapted inverse model and robust disturbance observer is studied in this paper. First, a model following two-degrees-of-freedom controller consisting of a command feedforward controller (FFC) and a feedback controller (FBC) is developed. According to the estimated motor drive dynamic model and the given position tracking response, the inner speed controller is first designed. Then, the transfer function of FFC is found based on the inverse model of inner speed closed-loop and the chosen reference model. The practically unrealizable problem possessed by traditional feedforward control is avoided by the proposed FFC. As to the FBC, it is quantitatively designed using reduced plant model to meet the specified load force regulation control specifications. In dealing with the robust control, a disturbance observer based robust control scheme and a parameter identifier are developed. The key parameters in the robust control scheme are designed considering the effect of system dead-time. The identification mechanism is devised to obtain the parameter uncertainties from the observed disturbance signal. Then by online adapting the parameters set in the FFC according to the identified parameters, the nonideal disturbance observer based robust control can be corrected to yield very close model following position tracking control. Meanwhile, the regulation control performance is also further improved by the robust control. In the proposed identification scheme, the effect of a nonideal differentiator in the accuracy of identification results is taken into account, and the compromise between performance, stability, and control effort limit is also considered in the whole proposed control scheme.     

Robust Smooth-Trajectory Control of Nonlinear Servo Systems Based on Neural Networks

The electromagnetic torque introduces ripples into the electromechanical motion system due to nonlinearities, such as uncertain changes of magnet field, load, and friction, which generate speed oscillations and deteriorate the tracking performance of servo system. Furthermore, the minimum time response and smooth trajectory tracking are cruces in servo control. In this paper, an available method is proposed to solve them by using neural networks (NNs) and a nonlinear smooth trajectory filter (STF) for the robust smoothing feedforward control of a class of general nonlinear systems. First, the online weight-tuning scheme based on Lyapunov function can guarantee the boundedness of tracking error by good performance of NNs modeling nonlinear functions. Second, a feedforward controller based on the output of nonlinear STF is designed to guarantee minimum time response and smooth trajectory tracking. Finally, as a example, the motion system can be equivalent to the two-order system under the linear closed-loop current control in view of the (d,q) mathematic model for PM synchronous motor, so that this robust smoothing control method using neutral networks can be applied into position servo control. Moreover, the validity and effectiveness of this control method are verified through simulations and experiments     

Precise position control of shape memory alloy actuator using inverse hysteresis model and model reference adaptive control system

Position control of Shape Memory Alloy (SMA) actuators has been a challenging topic during the last years due to their nonlinearities in the governing physical equations as well as their hysteresis behaviors. Using the inverse of phenomenological hysteresis model in order to compensate the input–output hysteresis behavior of these actuators shows the effectiveness of this approach. In this paper, in order to control the tip deflection of a large deformation flexible beam actuated by an SMA actuator wire, a feedforward–feedback controller is proposed. The feedforward part of the proposed control system, maps the beam deflection into SMA temperature, is based on the inverse of the generalized Prandtl–Ishlinskii model. An adaptive model reference temperature control system is cascaded to the inverse hysteresis model in order to estimate the SMA electrical current for tracking the reference signal. In addition, a closed-loop proportional–integral controller with position feedback is added to the feedforward controller to increase the accuracy as well as eliminate the steady state error in position control process. Experimental results indicate that the proposed controller has great accuracy in tracking some square wave signals. It is also experimentally shown that the suggested controller has precise tracking performance in presence of environmental disturbances.     

Perfect tracking control based on multirate feedforward controlwith generalized sampling periods

In this paper, a novel perfect tracking control method based on multirate feedforward control is proposed. The advantages of the proposed method are that: (1) the proposed multirate feedforward controller eliminates the notorious unstable zero problem in designing the discrete-time inverse system; (2) the states of the plant match the desired trajectories at every sampling point of reference input; and (3) the proposed controller is completely independent of the feedback characteristics. Thus, highly robust performance is assured by the robust feedback controller. Moreover, by generalizing the relationship between the sampling period of plant output and the control period of plant input, the proposed method can be applied to various systems with hardware restrictions of these periods, which leads to higher performance. Next, it is shown that the structure of the proposed perfect tracking controller is very simple and clear. Illustrative examples of position control using a DC servomotor are presented, and simulations and experiments demonstrate the advantages of this approach     

永磁同步电机伺服系统自抗扰控制器设计

对永磁同步电机(PMSM)调速系统中的时变输入提出具有更高跟踪精确度的改进型自抗扰控制策略.传统的自抗扰控制主要针对阶跃信号进行快速和无静差追踪,对时变信号存在较大的跟踪误差,使自抗扰控制的应用受限.文中对稳态误差的存在原因进行了理论分析,进而设计带有微分前馈和并联线性扩张状态观测器(P-LESO)的改进型转速自抗扰控...     

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     

Robust position control and disturbance rejection of an industrial plant emulator system using the feedforward-feedback control

An important class of electromechanical system known as the industrial plant emulator system which represents the many practical systems used in the industry. The motion control of the emulator system is challenging and important one as it mimics the system class of conveyors, machine tools, spindle drives, and automated assembly machines. In this paper, a new quantitative feedback-feedforward approach is proposed to address both tracking and the disturbance (measurable, uncertain) rejection problem. The proposed methodology is centered on the design of feedback controller with the inversion based feedforward control. The proposed method converts the problem on the uncertain system into a nominal sensitivity problem which is simple, less conservative and easier to solve for a large number of uncertain parameter system such as the emulator plant. The unknown dynamic of the disturbance (motor) is identified practically by a step response (three parameter model) for integrating system. The proposed feedforward/feedback control is experimentally demonstrated to meet the tracking and reject the disturbance with the less demand on the feedback control as compared to the existing methods under different loading condition.     

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.      

Robust learning control of a high precision planar parallel manipulator

End-point positioning accuracy and fast settling time are essential in the motion system aimed at semiconductor packaging applications. In this paper, a novel robust learning control method for a direct-drive planar parallel manipulator is presented. A frequency-domain system identification approach is used to identify the high frequency dynamic of the manipulator. A robust control design method is employed to design a stable, fast tracking response feedback controller with less sensitivity to high frequency disturbance and the control parameters are determined using genetic algorithm. A Fourier-series-based iterative learning controller is designed and used on the feedforward path of the controller to further improve the settling time by reducing the dynamic tracking error of the manipulator. Experimental results demonstrate that the planar parallel manipulator has significant improvements on motion performance in terms of positioning accuracy, settling time and stability when compared with traditional XY-stages. This shows that the proposed manipulator provides a superior alternative to XY-motion stages for high precision positioning.     

A novel AQM algorithm based on feedforward model predictive control

Developing feedforward model predictive controller as an active queue management (AQM) scheme is studied in this paper. MPC is an advanced control strategy for AQM. However, the conventional MPC is usually an implementable form of feedback MPC. In this paper, a feedforward and feedback optimal control law is presented. It is a clean, easily implementable, version of model predictive control that incorporates feedforward. Firstly, we use the nominal fluid model to design the feedforward control input so that the output tracks the given queue length with small error. Furthermore, in order to achieve robust performance and to reject the (unmeasured) disturbance, the feedback component is designed. In particular, a disturbance observer is incorporated into the prediction output in standard feedback MPC. This framework can significantly improve performance in the presence of measurement noise and certain types of model uncertainty. Finally, the simulation results show the effectiveness of FF‐AQM algorithm.     

压电微执行器在硬盘伺服系统的应用研究

介绍一个硬盘双伺服控制系统的设计和实现。双伺服系统是在硬盘固有的音圈电机上附加了一个压电微执行器,用来执行更快捷、精确的磁头定位。对音圈电机的控制设计采用了一种线性反馈控制技术,结合扰动估计和补偿实现快速、平稳和无静差的设定点跟踪。基于开环逆控制的思想,微执行器环路的控制采用定值比例及滤波器,使得合并的位移输出能更快地跟踪设定点(磁道)。仿真和实验结果证明双伺服控制系统可以实现更快速、精确的磁头定位。     

Position control of high performance hydrostatic actuation system using a simple adaptive control (SAC) method

This paper deals with the issue of position tracking control of a high performance hydrostatic actuation system using simple adaptive control. For energy-efficiency and savings, a speed-controlled fixed displacement pump is utilized to drive a symmetrical linear actuator instead of a directional control servo valve. The whole control system is composed of a pair of interconnected subsystems, that is, a feedback control system and a feedforward control system to enhance the tracking performance. The experiment using the proposed control scheme has been performed and a significant reduction in position tracking error is achieved compared to a conventional PID control.     

Discrete-time mode switching control with application to a PMSM position servo system

This paper presents a mode switching control (MSC) scheme in discrete-time domain for fast and precise set-point tracking in servo systems subject to control saturation and unknown disturbance. The basic idea is to combine the proximate time-optimal servomechanism (PTOS) and the composite nonlinear feedback (CNF) control, using the output position as the only measurable information for feedback. The PTOS is responsible for fast targeting in servo systems when the tracking error is large, and once the system trajectory enters into some specified region, the CNF will take over the control to ensure a smooth settling without compromising the fast transient performance. A reduced-order extended state observer is adopted to estimate the speed signal for feedback and the disturbance for compensation. The asymptotical stability of the proposed MSC scheme is analyzed and the switching conditions are provided. Simulation and experimental results on a permanent magnet synchronous motor (PMSM) servo system verify that the proposed control scheme is effective in improving the tracking performance for a wide range of target set-points.     

Robust model predictive control and observer for direct driveapplications

In this paper, robust position control of a direct drive using a state space model predictive control (MPC) algorithm is presented. The proposed controller consists of a state feedback regulator and a feedforward controller. Their gains are obtained by minimizing a cost function that is a sum of the position tracking errors and the control cost over some user defined time horizons. The effects of the controller parameters on the dynamic performance and the robustness of the direct drive are investigated. To provide good estimates of the state variables in the presence of load disturbance, a new observer based on the receding horizon concept is also formulated. Experimental results are presented to demonstrate the effectiveness of the approach     

Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive

A robust wavelet neural network control (RWNNC) system is proposed to control the rotor position of an induction servo motor drive in this paper. In the proposed RWNNC system, a wavelet neural network controller is the main tracking controller that is used to mimic a computed torque control law, and a robust controller is designed to recover the residual approximation for ensuring the stable control performance. Moreover, to relax the requirement for a known bound on lumped uncertainty, which comprises a minimum approximation error, optimal network parameters and higher order terms in a Taylor series expansion of the wavelet functions, an RWNNC system with adaptive bound estimation was investigated for the control of an induction servo motor drive. In this control system, a simple adaptive algorithm was utilized to estimate the bound on lumped uncertainty. In addition, numerical simulation and experimental results due to periodic commands show that the dynamic behaviors of the proposed control systems are robust with regard to parameter variations and external load disturbance.