基于PMAC的角编码器检测控制系统

光杠杆角编码器检测装置用于角编码器的误差检测,可提高天文望远镜的运动控制精度.该装置需要较高精度的转动控制,并实现自动检测.根据该检测装置的功能要求,以可编程多轴运动控制器(PMAC)为核心,设计了角编码器检测装置的控制系统,搭建了控制系统的硬件平台,编写了控制系统的应用软件,实现了CCD曝光与码盘数据采集的同步.实验结果表明:该装置转台的重复定位误差绝对值小于4″,满足角编码器检测系统的要求.     

A rule-based system for verifying engineering specifications in industrial visual inspection applications

An important step in automatic visual inspection is verifying whether a part is good or bad, by comparing a list of inspection specifications to a list of extracted and measured defects. Our goal is to provide a general, flexible, and efficient solution to this problem. We present a solution following a rule-based approach for the case of specs for visual inspection of disk heads. However, due to the generality of our approach (within the realm of visual inspection), it is easily extendible to verification of specs in other visual inspection applications. While flexibility comes naturally with the rule-based approach, efficiency is still an issue. Therefore, we implemented two techniques to increase the efficiency of our system: one at the rule level, and one at the rule-matching level. We describe our implementation and show experimental results from applying our approach in an experimental system for automatic visual disk head inspection.     

基于神经网络与多模型的非线性自适应广义预测解耦控制

针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性.     

Voltage driven hard disk drive with voice coil model-based control

In this paper, we describe a new solution for driving the voice coil motor (VCM) in hard disk drives (HDDs), in which the usual current driver for the voice coil motor has been replaced by a voltage driver. When used in conjunction with PWM power stages, this solution becomes fully digital and has proven advantages in terms of cost, power dissipation and silicon area. To achieve the same performance of the current driver, the voltage driver requires a pre-filter placed at its input, to cancel out the electrical pole of the VCM. In the paper, we describe the voice coil model-based control (VCMC), a solution for implementing the pre-filter, which replicates the behavior of a current loop by using a model of the voice coil motor and its driver, including the saturation of the power stage. It is worth noticing that VCM resistance may change by 30% during HDD operations, increasing when the disk is performing repetitive seeks or decreasing when the disk cools down during track following. Such variation may lead to an unsatisfactory performance of the VCMC, so an adaptation mechanism, capable of tracking variations of VCM coil resistance, must be set up. This paper presents a pair of on-line estimation procedure, used to get the value of the VCM coil resistance during seek and track following, respectively. The first one is based on a standard LMS approach, aimed at identifying a simplified model of the plant with the least computational effort. The second exploits the presence of repetitive disturbances (RROs) to detect variations of plant gain and, in turn, of VCM resistance. It will be shown that both procedures are quite accurate in estimating variations of VCM resistance and their output can be used to perform on-line tuning of the VCMC. Experimental results show that the servo performance with the adaptive VCMC is not affected by resistance variation and equivalent to that of the standard current driver.     

基于三相旋转变换与极坐标变换的电机矢量控制

就异步电机旋转变换和实现方案进行了分析,采用了3相同步旋转与极坐标变换的方法,建立了新的电机旋转变换和极坐标矢量电压控制模型.由于这个模型具有直流电流调节器提供的矢量电压模和旋转角,显然方便SVPWM进行变频变压控制.分析变换使得电流跟随控制器为直流调节器,可在直流线性电压电流模型条件下设计电流反馈环,为交流异步电机矢量控制的实现,提供了新方法和新途径.     

同步发电机励磁非线性预测控制技术

以功角、有功功率、角速度这些可测量作为反馈量,基于非线性预测控制理论,设计出具有闭合解析形式控制律的励磁控制器．该控制器的设计参数只有滚动预测时间T和控制阶r,便于工程实现和调试．仿真结果表明,该控制器能使系统提高稳定性,具有良好的动态品质,较好地维持了发电机机端电压的静态调节精度．     

A model predictive control framework for constrained uncertain positive systems

In this paper, a new model predictive control framework is proposed for positive systems subject to input/state constraints and interval/polytopic uncertainty. Instead of traditional quadratic performance index, simple linear performance index, linear Lyapunov function, cone invariant set with linear form and linear computation tool are first adopted. Then, a control law that can handle the constraints and robustly stabilise the systems is proposed. The advantages of the new framework lie in the following facts: (1) an equivalent linear problem is formulated that can be easily solved than other problems including the quadratic ones, (2) simple linear index and linear tool can be used based on the essential property of positive systems to achieve the desired control performance and (3) a general model predictive control law without sign restriction is designed. Finally, an attempt of application on mitigating viral escape is provided to verify the effectiveness of the proposed approach.     

Near-minimum-time control of asymmetric rigid spacecraft using two controls

In general, spacecraft are designed to be maneuvered to achieve pointing objectives. To reorient the spacecraft with zero angular velocity at the end of the maneuver, a three-axis control design is usually used. When an actuator fails among three actuators, one must achieve these objectives using two control inputs, so that new control laws need to be considered. A simple and novel control law, which is based on the sequential Euler angle rotation strategy, is addressed. This paper explores a near minimum time control problem with constrained control input magnitudes. By introducing the three sequential sub-maneuvers with Euler-angle transformations, the governing nonlinear equations become rigorously linear, which permits a closed-form solution to be obtained for the switch times and final time, where the three sub-maneuvers are coupled through the unknown switch times. A numerical example demonstrates that the three-dimensional maneuver for an asymmetric spacecraft with two constrained control inputs can be successfully performed using the proposed closed-form solution.     

Theoretical analysis of unconstrained nonlinear model predictive control

Model predictive control (MPC) is a well-established controller design strategy for linear process models. Because many chemical and biological processes exhibit significant nonlinear behaviour, several MPC techniques based on nonlinear process models have recently been proposed. The most significant difference between these techniques is the computational approach used to solve the nonlinear model predictive control (NMPC) optimization problem. Consequently, analysis of NMPC techniques is often connected to the computational approach employed. In this paper, a theoretical analysis of unconstrained NMPC is presented that is independent of the computational approach. A nonlinear discrete-time, state-space model is used to predict the effects of future inputs on future process outputs. It is shown that model inverse, pole-placement, and steady-state controllers can be obtained by suitable selection of the control and prediction horizons. Moreover, the NMPC optimization problem can be modified to yield nonlinear internal model control (NIMC). The computational requirements of NIMC are considerably less than NMPC, but the NIMC approach is currently restricted to nonlinear models with well-defined and stable inverses. The NIMC controller is shown to provide superior servo and regulatory performance to a linear IMC controller for a continuous stirred tank reactor.     

Model predictive control of P-time event graphs

This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.     

Effective optimization for fuzzy model predictive control

This paper addresses the optimization in fuzzy model predictive control. When the prediction model is a nonlinear fuzzy model, nonconvex, time-consuming optimization is necessary, with no guarantee of finding an optimal solution. A possible way around this problem is to linearize the fuzzy model at the current operating point and use linear predictive control (i.e., quadratic programming). For long-range predictive control, however, the influence of the linearization error may significantly deteriorate the performance. In our approach, this is remedied by linearizing the fuzzy model along the predicted input and output trajectories. One can further improve the model prediction by iteratively applying the optimized control sequence to the fuzzy model and linearizing along the so obtained simulated trajectories. Four different methods for the construction of the optimization problem are proposed, making difference between the cases when a single linear model or a set of linear models are used. By choosing an appropriate method, the user can achieve a desired tradeoff between the control performance and the computational load. The proposed techniques have been tested and evaluated using two simulated industrial benchmarks: pH control in a continuous stirred tank reactor and a high-purity distillation column.     

基于滑模变结构的开关磁阻电机角度控制

针对开关磁阻电机(SRM)常规线性方法控制效果不理想以及高速运转时以相电压作为控制量的不合理性的问题,在分析开关磁阻电机开通、关断角对输出转矩的作用以及SRM数学模型的基础上,提出了基速以上时以关断角作为控制量的滑模变结构角度控制方法,并基于此方法设计了实验系统,实验结果表明:该系统具有良好的动静态性能,具有较强的鲁棒性。     

Modeling and robust discrete LQ repetitive control of electrically driven robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.     

Constrained multivariable control of a distillation column using a simplified model predictive control algorithm

Distillation columns are important process units in petroleum refining and need to be maintained close to optimum operating conditions because of economic incentives. Model predictive control has been used for control of these units. However, the constrained optimization problem involved in the control has generally been solved in practice in a piece-meal fashion. To solve the problem without decomposition, the use of a linear programming (LP) formulation using a simplified model predictive control algorithm has been suggested in the literature. In this paper, the LP approach is applied for control of an industrial distillation column. The approach involved a very small size optimization problem and required very modest computational resources. The control algorithm eliminated the large cycling in the product composition that was present using SISO controllers. This resulted in a 2.5% increase in production rate, a 0.5% increase in product recovery, and a significant increase in profit.     

A MEMS-based tracking milli-mirror for high-density optical disk drives

We present a new MEMS-based milli-mirror for precise tracking in high-density optical disk drives (ODDs). The device consists of a torsionally suspended mirror plate, one pair of torsion springs, which support the mirror plate and offer a restoring torque, and two pairs of electrodes attached to the mirror plate and glass substrate. The dimensions of mirror plate and torsion springs were determined so that a 5 V dc bias ±4.5 V ac drive voltage would provide the mirror with ±0.02° rotation to transmit laser beam spot on spinning disk. The MEMS-based milli-mirror was successfully fabricated using MEMS technology. Displacement–voltage linearization scheme was implemented by differential voltage driving. The static and dynamic performances of mirror prototype, such as capacitance versus driving voltage, rotation angle versus driving voltage, and resonant frequency were characterized and compared well with the simulation solutions. The mechanical resonant frequency of the mirror is expected to be high enough to satisfy the requirement of the servo bandwidth of precise tracking-control in high-density blue-laser optical disk drive.     

An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system

The purpose of this paper is to deal with a novel intelligent predictive control scheme using the multiple models strategy with its application to an industrial tubular heat exchanger system. The main idea of the strategy proposed here is to represent the operating environments of the system, which have a wide range of variation in the span of time by several local explicit linear models. In line with this strategy, the well-known linear generalized predictive control (LGPC) schemes are initially designed corresponding to each one of the linear models of the system. After that, the best model of the system and the LGPC control action are precisely identified, at each instant of time, by an intelligent decision maker scheme (IDMS), which is playing the so important role in realizing the finalized control action for the system. In such a case, as soon as each model could be identified as the best model, the adaptive algorithm is implemented on the both chosen model and the corresponding predictive control schemes. In conclusion, for having a good tracking performance, the predictive control action is instantly updated and is also applied to the system, at each instant of time. In order to demonstrate the effectiveness of the proposed approach, simulations are carried out and the results are compared with those obtained using a nonlinear GPC (NLGPC) scheme as a benchmark approach realized based on the Wiener model of the system. In agreement with these results, the validity of the proposed control scheme can tangibly be verified.     

Parametric simulation of piezoelectric flying height control slider using shear-mode deformation

The piezoelectric flying height control slider has recently been implemented in magnetic recording disk drives to reduce the flying height. This paper performs the electromechanical simulation and air-bearing simulation to investigate the effects of the shear-model deformation on the static flying attitude of PZT slider. The location of PZT sheet and air bearing surface of slider are investigated to achieve a low flying height and robust head-disk interface. The results show that a short distance of the PZT sheet to the trailing edge of the slider can help to achieve a low flying height. A small center-trailing pad of the slider can also help to achieve a low flying height, but cannot prevent the reduction in pitch angle. The depth of the center-trailing pad does not change the reduction ratio of the pitch angle when increasing the drive voltage. A big pitch angle value is needed to avoid the pitch angle falling below zero at a high drive voltage.     

一类不确定模糊模型的输出反馈鲁棒预测控制

针对一类满足扇形界条件的不确定模糊模型描述的非线性系统,提出一种输出反馈鲁棒预测控制方法．该方法将鲁棒预测控制的Min-Max优化问题转化为具有LMI约束的线性目标最小化问题,并且不需系统状态完全可测,仅仅利用系统测量输出和不可测状态的界限值来确定保证闭环系统鲁棒稳定的输出反馈控制器．仿真实验证明了该方法的有效性．     

基于L S-SVM 的非线性预测控制技术

探讨了利用最小二乘支持向量机(LS—SVM)进行非线性系统辨识的方法，LS—SVM用等式约束代替传统支持向量机中不等式约束，求解过程从解QP问题变成解一组等式方程，将得到的LS—SVM模型应用到非线性预测控制，提出了基于LS—SVM模型的非线性预测控制算法，通过CSTR过程仿真表明，最小二乘支持向量机学习速度快，在小样本情况下具有良好的非线性建模和泛化能力，基于LS—SVM的预测控制算法具有很好的控制性能。     

基于Vega的某型导弹瞄准训练仿真

通过对光学望远镜瞄准原理的分析，根据望远镜放大倍数与焦距以及视场角的数学公式得到了三者之间的联系，由计算机图形学透视投影原理建立了瞄准的仿真模型，提出了一种基于Vega的瞄准镜视景仿真的实现方法，介绍了该方法实现的关键步骤，最后给出了部分程序源代码和仿真效果图。该仿真模型与实际系统光学原理完全一致，增强了仿真系统视觉效果逼真性和瞄准原理的准确性。由于该方法在工程上极易实现，因此它对于此类视景仿真具有通用的借鉴作用。