双向二维快速伺服刀架的前馈控制研究

以压电陶瓷驱动器作为动力输入的快速伺服刀架具有输出力大和高频率响应的优点。压电陶瓷驱动器固有的迟滞现象严重影响了快速伺服刀架的输出定位精度。为解决此问题,通过引入归一化Bouc-Wen模型建立前馈控制补偿器,归一化Bouc-Wen模型解决了经典Bouc-Wen模型中存在的参数冗余问题。获得模型参数后,基于其逆模型搭建了前馈补偿器,并在搭建的实验平台上进行了单/双自由度轨迹跟踪性能测试。实验结果表明,对于等幅正弦波信号,经前馈控制环节补偿下快速伺服刀架的最大轨迹跟踪误差为1.18%,最大轨迹跟踪偏差为2.61%,证明该文所提出的前馈控制补偿器能提高快速伺服刀架的定位精度。     

A neural-network-based controller for a single-link flexiblemanipulator using the inverse dynamics approach

This paper presents an intelligent-based control strategy for tip position tracking control of a single-link flexible manipulator. Motivated by the well-known inverse dynamics control strategy for rigid-link manipulators, two feedforward neural networks (NNs) are proposed to learn the nonlinearities of the flexible arm associated with the inverse dynamics controller. The redefined output approach is used by feeding back this output to guarantee the minimum phase behavior of the resulting closed-loop system. No a priori knowledge about the nonlinearities of the system is needed and the payload mass is also assumed to be unknown. The network weights are adjusted using a modified online error backpropagation algorithm that is based on the propagation of output tracking error, derivative of that error and the tip deflection of the manipulator. The real-time controller is implemented on an experimental test bed. The results achieved by the proposed NN-based controller are compared experimentally with conventional proportional-plus-derivative-type and standard inverse dynamics controls to substantiate and verify the advantages of our proposed scheme and its promising potential in identification and control of nonlinear systems     

Dual Servo Control of a High-Tilt 3-DOF Microparallel Positioning Platform

This paper presents a precise positioning control of a microparallel positioning platform using a dual-stage servo system. The result of the research can be applied to dual-stage-type parallel machines for improving the positioning accuracy. The proposed platform adopts a dual-stage system that consists of three coarse actuators and three fine actuators to realize 3 degrees of freedom (DOF) motion. The 3-DOF motion of the end-effector is measured by a set of three linear sensors. Dynamic models for the coarse and fine actuators are derived by the system identification approach. The gain-scheduled multi-input multi-output (MIMO) controllers are synthesized based on the modeling. The MIMO controller is designed with a mixed-sensitivity criterion on tracking performance and positioning capability, and the design of the gain scheduler is based on the kinematics change. By integrating the controllers for two kinds of actuators, a dual servo controller can be developed based on the master-slave with decoupling structure. An antiwindup controller and a feedforward compensator are adopted to improve the performance. The successful performance of the synthesized dual servo controller is validated through experiments on tracking to guarantee submicrometer accuracy.     

Nonlinear identification and optimal feedforward friction compensation for a motion platform

In this study, we present a method of nonlinear identification and optimal feedforward friction compensation for an industrial single degree of freedom motion platform. The platform has precise reference tracking requirements while suffering from nonlinear dynamic effects, such as friction and backlash in the driveline. To eliminate nonlinear dynamic effects and achieve precise reference tracking, we first identified the nonlinear dynamics of the platform using Higher Order Sinusoidal Input Describing Function (HOSIDF) based system identification. Next, we present optimal feedforward compensation design to improve reference tracking performance. We modeled the friction using the Stribeck model and identified its parameters through a procedure including a special reference signal and the Nelder–Mead algorithm. Our results show that the RMS trajectory tracking error decreased from 0.0431 deg/s to 0.0117 deg/s when the proposed nonlinear identification and friction compensation method is utilized.     

基于Canny检测算法实现的目标跟踪

为了设计一种实时高效、稳定可靠的图像目标跟踪系统平台,避免因图像边缘提取效果差而引起跟踪失败,采用自适应Canny边缘检测算法。该自适应算法能够很好的确定平滑参数以及高、低两个阈值,更好的获得图像边缘图。经Canny算法处理图像目标后,获得目标的单像素边缘图,根据边缘图计算得到目标质心。利用最小二乘法拟合出目标的运动轨迹,同时可根据时间间隔预测出目标质心的下一位置,控制伺服机构,实现目标跟踪。实验表明,采用Canny算法的目标跟踪系统,能够满足实时跟踪的需要。     

High-gain observer-based pump/valve combined control for heavy vehicle electro-hydraulic servo steering system

Electro-hydraulic servo steering system (EHSSS) has been widely used in multi-axle heavy vehicles. Noteworthy, the traditional EHSSS controlled only by servo solenoid valve has amounts of energy loss in throttling orifice. Although the steering control accuracy is ensured, it leads to low energy efficiency. In this paper, a novel pump/valve combined control (PVCC) EHSSS is proposed to increase the energy efficiency, which only uses one servo motor pump and one servo solenoid valve to drive the steering trapezoid mechanism. Based on the control objectives of low pressure difference in valve orifice and high steering tracking performance, a dual-input-dual-output control strategy is proposed. To guarantee the high steering tracking performance of PVCC steering system, a high-gain observer based sliding mode controller (HGO-SMC) is designed for controlling the spool displacement of servo solenoid valve. During the steering process, the servo motor pump is controlled by a simple speed feedforward and PID controller, so that the pressure difference in throttling orifice is kept at a low value to reduce the energy wasted. The experimental comparison results show that the proposed method can achieve the same tracking performance as valve control EHSSS with less energy consumption.     

Robust Control of PM Spherical Stepper Motor Based on Neural Networks

There are many uncertainties and disturbances in the real dynamic system of a spherical stepper motor that make traditional control methods with lower precision, such as uncertain changes of magnetic field, load, and friction that generate speed ripple and deteriorate the 3-D tracking performance of the spherical motor system. In this paper, an available method is proposed to solve them by using neural networks (NNs) and a robust control scheme for improving the performance. First, a simplified torque calculation model based on finite-element method results can guarantee quick prediction of electromagnetic torque with lower error. Thus, the system model considering the friction, load, and disturbances is developed. Second, a robust NN (RNN) control scheme is presented to eliminate uncertainties to improve the tracking robust stability and overcome the undesired influence of uncertainties based on the nonlinear system dynamic model under continuous-trajectory tracking mode. Finally, as an example, the step-response and continuous-tracking processes of the motor using an RNN controller are simulated, and experiments, including the tracking using RNN proportional–differential control, are carried out to confirm the usefulness of the proposed control scheme. The simulation and experimental results of the proposed control scheme on the spherical stepper motor system demonstrate the effectiveness on satisfactory tracking performance.      

Implementation of Artificial Neural Network-Based Tracking Controller for High-Performance Stepper Motor Drives

Two distinct multilayer perception neural networks (NNs) are implemented via laboratory experiment to simultaneously identify and adaptively control the trajectory tracking of a hybrid step motor assumed to operate in a high-performance drives environment. That is, a neural network identifier (NNI) which captures the nonlinear dynamics of the stepper motor drive system (SMDS) over any arbitrary time interval in its range of operation, and a neural network controller (NNC) to provide the necessary control actions as to achieve trajectory tracking of the rotor speed. The exact form of the control law is unknown, and must be estimated by the NNC. Consequently, the NNC is constructed as a nonlinear unknown function depending on the current state of the drive system supplies by the NNI and the reference trajectory we wish the outputs to follow. The two NNs are online trained using dynamic back-propagation algorithm. The composite structure is used as a speed controller for the SMDS. Performance of the composite controller is evaluated through a laboratory experiment. Experimental results show the effectiveness of this approach, and demonstrate the usefulness of the proposed controller in high-performance drives     

Joint input shaping and feedforward for point-to-point motion: Automated tuning for an industrial nanopositioning system

Feedforward control can effectively compensate for the servo error induced by the reference signal if it is tuned appropriately. This paper aims to introduce a new joint input shaping and feedforward parametrization in iterative feedforward control. Such a parametrization has the potential to significantly improve the performance for systems executing a point-to-point reference trajectory. The proposed approach enables an efficient optimization procedure with global convergence. A simulation example and an experimental validation on an industrial motion system confirm (i) the performance improvement obtained by means of the joint input shaping and feedforward parametrization compared to pre-existing results, and (ii) the efficiency of the proposed optimization procedure.     

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.     

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.     

Robust tracking servo system considering force disturbance for the optical disk recording system

This paper proposes a new robust tracking servo system for the optical disk recording system with feedforward controller based on the prediction of the tracking error. In optical recording systems, the feedback servo system must suppress the influence of force disturbance and parameter variation. To overcome this problem, this paper designs the robust feedback control system by using coprime factorization and disturbance observer. The detecting signal of the optical disk recording system is only a tracking error. Hence, the feedforward controller of the proposed tracking control system is constructed based on both the "zero-phase-error tracking" control theory and the prediction of the tracking error. The experimental results point out that the proposed tracking servo system has a quick and precise tracking response and keeps the residual tracking error below its tolerance.     

Dynamic triangular neural controller for stepper motor trajectorytracking

We present a novel neural controller for a stepper motor. This controller is developed as follows. Modifying published results for nonlinear identification using dynamic neural networks (NNs), an NN identifier of triangular form is implemented. Then, based on this mode, a control law using sliding modes is derived. This neural identifier and the proposed control law allow trajectory tracking for stepping motors. Applicability of the approach is tested via simulations     

A learning scheme for low-speed precision tracking control of hybrid stepping motors

Servo control of the hybrid stepping motor is complicated due to its highly nonlinear torque-current-position characteristics, especially under low operating speeds. This paper presents a simple and efficient control algorithm for the high-precision tracking control of hybrid stepping motors. The principles of learning control have been exploited to minimize the motor's torque ripple, which is periodic and nonlinear in the system states, with specific emphasis on low-speed situations. The proposed algorithm utilizes a fixed proportional-derivative (PD) feedback controller to stabilize the transient dynamics of the servomotor and the feedforward learning controller to compensate for the effect of the torque ripple and other disturbances for improved tracking accuracy. The stability and convergence performance of the learning control scheme is presented. It has been found that all error signals in the learning control system are bounded and the motion trajectory converges to the desired value asymptotically. The experimental results demonstrated the effectiveness and performance of the proposed algorithm.     

Optimal Synchronization Control of High-Precision Motion Systems

Recently, in the motion control area, one of the most challenging problems has been synchronization control of multiple motion axes or drivers. Unfortunately, the majority of the previous approaches have not fully addressed the synchronization problem when the system performs a complex motion. In this paper, a novel synchronized design of the high-precision motion control system is presented. The basic idea is to introduce the coupling and synchronization factors into the definition of the synchronization error. Then, a new quadratic performance index incorporating the synchronization errors of the multiple motion axes is introduced so that the resulting control law generates the cross-coupling control action, and as a consequence, improved tracking and synchronization performance can be obtained. The key to the success of the new design is to ensure that each motor tracks its desired trajectory while synchronizing motion with others. Computer simulations and real-time experiments on a servo system with two permanent-magnet linear motors demonstrate the effectiveness of the method.     

Ein neues Vorfilter zur Verbesserung des Folgeverhaltens von Bewegungsregelungen

The tracking error of motion control systems which should guarantee high precision — for example considering machine tools — can be considerably reduced by the use of a feedforward filter. The feedforward filter G_v(z^?1) should carry out the compensation of the poles and zeros of the transfer function G_cl(z^?1) of the motion control system. As far as motion control is concerned this compensation is not feasible, since the transfer function G_cl(z^?1) of this kind of control systems has usually unstable or limit-stable zeros. Different methods of feedforward filter designs are known, which also achieve in the case of noncompensable zeros a remarkable performance improvement. Most commonly used is the so called zero phase error tracking (ZPET)-feedforward filter. ZPET-prefiltering leads to phase zero of the frequency response of the overall transfer function G_v(z^?1) G_cl(z^?1) within the whole frequency range. This article introduces a new feedforward concept, which is easy to design and is a result of time domain considerations. The reported investigations show that this feedforward concept leads not only in theory but also under realistic circumstances to a quite reduced tracking error compared to the ZPET-feedforward filter.     

四旋翼无人机的轨迹规划跟踪控制研究

针对四旋翼无人机的实际应用需求,开展轨迹跟踪控制研究。提出了一种基于快速扩展随机树（RRT）算法的Dubins航迹规划方法,同时搭建了四旋翼模型,并设计了轨迹跟踪控制方案对所提出的规划路径进行了轨迹跟踪仿真。该RRT-Dubins算法采用RRT算法对有障碍区域的无人机路径进行有效规划,然后利用Dubins路径对规划出的轨迹进行平滑处理,以形成一条无人机可飞行路径。仿真实验表明,采用所提出的轨迹规划方法及路径可以较好地规避障碍区域,且轨迹平滑更适合无人机飞行,同时验证了所提轨迹跟踪控制方案的有效性。     

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.     

基于改进的PID稳定平台伺服控制系统

传统PID控制在伺服系统高精度位置跟踪和改善系统品质方面已露出诸多不足,且系统中存在的控制干扰和测量噪声会在很大程度上影响伺服系统的跟踪精度。针对工作实际中的被控对象控制问题,对传统的PID控制算法进行改进。提出一种带有卡尔曼滤波器的重复控制补偿PID和前馈补偿相结合的控制方法。通过仿真证明,该控制方法能以较高的精度跟踪周期性输入信号,且有较好的抑制随机扰动和鲁棒性。     

A learning approach of wafer temperature control in a rapid thermalprocessing system

This paper presents a learning approach for wafer temperature control in a rapid thermal processing system (RTP). RTP is very important for semiconductor processing system and requires an accurate trajectory following. Numerous studies have addressed this problem and most research on this problem requires exact knowledge of the system dynamics. The various approaches do not guarantee the desired performance in practical applications when there exist some modeling errors between the model and the actual system. In this paper, iterative learning control scheme is applied to RTP without exact information on the dynamics. The learning gain of the iterative learning law is estimated by neural networks instead of a mathematical model. In addition, the control information obtained by the iterative learning controller (ILC) is accumulated in the feedforward neuro controller (FNC) for generalization to various reference profiles. Through numerical simulations, it is demonstrated that the proposed method can achieve an accurate output tracking even without an exact RTP model. The output errors decrease rapidly through iterations when using the learning gain estimated and the FNC yields a reduced initial error, and so requires small iterations