MEMS近红外光谱仪微镜驱动控制系统研发

针对微机电系统（MEMS）近红外光谱仪中MEMS微镜驱动系统的耦合与复杂扰动问题,提出了一种基于扰动观测器（DOB）与模型预测控制（MPC）的复合控制结构。通过分析MEMS微镜的驱动工作原理,建立MEMS微镜偏转角与驱动电压的传递函数模型,设计了MPC以消除系统耦合,通过分析系统扰动模型,设计了DOB实现对系统内部与外部扰动的集中监测。仿真研究与实验测试结果表明：基于DOB MPC复合结构的MEMS微镜驱动控制系统,既可以有效抑制系统的外部扰动,又可以抑制由模型失配和变量耦合导致的内部扰动。     

Neural network approximation for periodically disturbed functions and applications to control design

This paper addresses the approximation problem of functions affected by unknown periodically time-varying disturbances. By combining Fourier series expansion into multilayer neural network or radial basis function neural network, we successfully construct two kinds of novel approximators, and prove that over a compact set, the new approximators can approximate a continuously and periodically disturbed function to arbitrary accuracy. Then, we apply the proposed approximators to disturbance rejection in the first-order nonlinear control systems with periodically time-varying disturbances, but it is straightforward to extend the proposed design methods to higher-order systems by using adaptive backstepping technique. A simulation example is provided to illustrate the effectiveness of control schemes designed in this paper.     

Neural network based iterative learning predictive control design for mechatronic systems with isolated nonlinearity

The paper presents a new nonlinear predictive control design for a kind of nonlinear mechatronic drive systems, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. The nonlinear system is first treated into an equal linear time-variant system plus a nonlinear part using a neural network, then an iterative learning linear predictive controller is developed with a similar structure of PI optimal regulator and with setpoint feed forward control. Because the overall control law is a linear one, this design gives a direct and also effective multi-step prediction method and avoids the complicated nonlinear optimization. The control law is also an accurate one compared with traditional linearized method. Besides, changes of the system state variables are considered in the objective function with control performance superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is compared with conventional state space predictive control method and classical PI optimal control method. Tracking performance, robustness and disturbance rejection are enlightened.     

基于神经网络观测器的船舶轨迹跟踪递归滑模 动态面输出反馈控制

针对三自由度全驱动船舶速度向量不可测问题,考虑船舶模型参数和外部环境扰动均未知的情况,提出一种基于神经网络观测器的船舶轨迹跟踪递归滑模动态面输出反馈控制方法.该方法设计神经网络自适应观测器估计船舶速度向量,且利用神经网络逼近模型参数不确定项,综合考虑船舶位置和速度误差之间关系构造递归滑模面,再采用动态面控制技术设计轨迹跟踪控制律和参数自适应律,并引入低频增益学习方法消除外界扰动导致的高频振荡控制信号.选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

基于定量反馈理论的时滞系统自抗扰控制参数整定

工业过程对象普遍存在时滞、模型参数不确定性和外部扰动多等特点,传统Smith预估控制方法难以设计出满足期望性能的鲁棒控制器.针对模型参数不确定性和外部扰动,本文采用自抗扰控制技术进行估计和补偿.针对系统存在时滞的特点,本文提出改进Smith预估器结构,提升扩张状态观测器对于扰动估计的实时性.在此基础上,本文以一阶时滞系统为例提出了控制器参数整定方法.首先根据最优参数选取准则确定预估器模型,然后在等效模型框架下采用定量反馈理论整定自抗扰控制器参数,确保控制系统达到预期性能指标.在仿真实验中,将所提出方法与几种常见时滞系统控制方法进行比较,通过设定值跟踪、抗扰及蒙特卡罗实验验证了所提出方法具有良好抗扰能力与鲁棒性.     

Model predictive control of an industrial packed bed reactor using neural networks

This paper discusses an industrial application of a multivariable nonlinear feedforward/feedback model predictive control where the model is given by a dynamic neural network. A multi-pass packed bed reactor temperature profile is modelled via recurrent neural networks using the backpropagation through time training algorithm. This model is then used in conjunction with an optimizer to build a nonlinear model predictive controller. Results show that, compared with conventional control schemes, the neural network model based controller can achieve tighter temperature control for disturbance rejection     

时滞纳米运动系统的多速率采样预测自抗扰控制

电容型纳米位置传感器在纳米伺服系统中得到了越来越多的应用.这类超高精度模拟传感器用于反馈信号时因较长的模数转换时间将带来明显的测量时滞.而这类数字纳米伺服系统也因硬件使其采样频率在处理高频干扰时受到带宽限制.本文针对测量时滞和高频干扰的挑战,提出一种带采样预测功能的多速率自抗扰控制器设计方法.首先建立了电容式位移传感器的纳米运动平台带有时滞的动力学模型.其次,基于该模型设计多率采样预测线性扩张状态观测器和多率反馈控制器.通过设计预测型观测器,适当选取观测器增益,消除时滞对状态观测的影响.另外,将输出预估器加入预测扩张状态观测器中重构采样点间系统输出值,从而在时滞系统中更好地估计和消除高频干扰,并给出了系统的稳定性分析.最后通过压电驱动纳米运动平台的实时控制实验验证本文提出控制器的有效性.     

非线性系统多步预测控制的复合神经网络实现

提出一种基于神经网络的非线性多步预测控制,采用由线性网络和动态递归神经网络构成的复合神经网络。在此基础上将线性系统的广义预测控制器扩展为非线性系统的多步预测控制器。通过对非线性过程ＣＳＴＲ的仿真表明,该方法的稳定性和鲁棒性明显优于线性ＤＭＣ预测控制。     

Composite predictive flight control for airbreathing hypersonic vehicles

The robust optimised tracking control problem for a generic airbreathing hypersonic vehicle (AHV) subject to nonvanishing mismatched disturbances/uncertainties is investigated in this paper. A baseline nonlinear model predictive control (MPC) method is firstly introduced for optimised tracking control of the nominal dynamics. A nonlinear-disturbance-observer-based control law is then developed for robustness enhancement in the presence of both external disturbances and uncertainties. Compared with the existing robust tracking control methods for AHVs, the proposed composite nonlinear MPC method obtains not only promising robustness and disturbance rejection performance but also optimised nominal tracking control performance. The merits of the proposed method are validated by implementing simulation studies on the AHV system.     

A composite fractional order active disturbance rejection control method for a class of uncertain systems with multiple disturbances

The existing active disturbance rejection control (ADRC) method may not provide sufficient disturbance rejection to multiple mismatched disturbances for the fractional order systems. In this paper, a composite disturbance rejection approach is developed for a class of fractional order uncertain systems, by synthesizing the fractional order ADRC (FOADRC) approach and a disturbance observer (DO)-based compensation scheme. Taking advantage of more disturbance information and a filter structure, an improved DO is developed to achieve precise estimation of disturbances in the presence of sensor noises. In addition, a state transformation is developed to convert the system into a simple integral chain model with only matched disturbances. Then a composite control law is designed to compensate the disturbances and provide satisfying dynamic performance. The efficiency of the proposed method is demonstrated by a numerical simulation and an actual servo control simulation, as well as the comparison with two kinds of the existing ADRC methods and the commonly used integral sliding mode control (I-SMC) method.     

基于抗扰增强型广义预测控制的永磁同步电机伺服系统

为了提高永磁同步电机(permanent magnet synchronous motor,PMSM)伺服系统的响应速度及控制精度,提出一种连续时间域下基于抗扰增强型广义预测控制方法.首先,通过构造高阶扩张状态观测器(high-order extended state observer,HOESO)对模型参数摄动及外部不确定性负载扰动进行估计,同时将干扰以及转子角速度的估计信息引入至位置轨迹输出预测序列中,实现对预测模型偏差的修正.进一步,通过求解位置跟随误差性能指标的优化问题,得到最优控制序列的显示解析解,并从理论上给出控制参数的选择规则.最终,利用Lyapunov理论对闭环系统进行严格的稳定性分析,并在快速控制原型(rapid control prototype,RCP)对拖实验平台上进行所提控制算法的性能验证.实验结果表明,与串级PI控制和传统广义预测控制相比,所提方法提高了伺服系统的位置跟踪精度和抗干扰性能.     

基于干扰精细估计与神经网络推力分配的载人潜水器控制

针对潜水器在水下运行时会受到洋流、参数摄动等多种干扰因素影响和潜水器的过驱动问题,设计一种基于干扰观测的反步控制器和基于神经网络二次规划的推力分配器的双层控制结构.首先,建立潜水器系统在洋流影响下的动力学模型;其次,将潜水器受到的干扰分为由洋流产生的干扰和由其他因素引起的干扰两部分,分别使用洋流观测器和非线性干扰观测器进行估计,并基于干扰观测信息利用反步法设计运动控制器;然后,针对潜水器的过驱动特性以及推进器的推力受限问题,提出一种基于神经网络二次规划的推力分配方法;最后,使用Matlab进行数值仿真,验证所提控制方法的有效性和优越性.结果表明,基于干扰精细估计与神经网络推力分配的潜水器运动控制系统具有干扰估计更加准确、推进系统的耗能最优,以及避免推进器的推力超限等优势.     

Disturbance observer based multi-variable control of ball mill grinding circuits

Ball mill grinding circuits are essentially multi-variable systems characterized with couplings, time-varying parameters and time delays. The control schemes in previous literatures, including detuned multi-loop PID control, model predictive control (MPC), robust control, adaptive control, and so on, demonstrate limited abilities in control ball mill grinding process in the presence of strong disturbances. The reason is that they do not handle the disturbances directly by controller design. To this end, a disturbance observer based multi-variable control (DOMC) scheme is developed to control a two-input-two-output ball mill grinding circuit. The systems considered here are with lumped disturbances which include external disturbances, such as the variations of ore hardness and feed particle size, and internal disturbances, such as model mismatches and coupling effects. The proposed control scheme consists of two compound controllers, one for the loop of product particle size and the other for the loop of circulating load. Each controller includes a PI feedback part and a feed-forward compensation part for the disturbances by using a disturbance observer (DOB). A rigorous analysis is also given to show the reason why the DOB can effectively suppress the disturbances. Performance of the proposed scheme is compared with those of the MPC and multi-loop PI schemes in the cases of model mismatches and strong external disturbances, respectively. The simulation results demonstrate that the proposed method has a better disturbance rejection property than those of the MPC and PI methods in controlling ball mill grinding circuits.     

基于QDRNN-ADRC的重力稳定平台控制研究

针对稳定平台系统存在系统模型不够精确或者参数变化,或者外部干扰未知等现象,以及采用自抗扰控制器存在参数众多且难以整理的问题,提出了一种基于准对角递归神经网络—自抗扰控制器(QDRNN—ADRC)的重力稳定平台控制算法.通过自抗扰控制器对系统的"总扰动"进行估计并补偿,同时引入神经网络的辨识功能对自抗扰控制器部分参数进行在线整定,基于自抗扰控制技术核心架构设计了QDRNN—ADRC.仿真结果表明:有效解决了重力稳定平台利用神经网络的辨识功能对自抗扰控制器部分参数进行在线整定外扰动的干扰以及参数自适应整定问题,相对于传统控制方法,其在稳定精度、快速性及抗干扰性方面均具有一定优势.     

Identification and predictive control of a multistage evaporator

A recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Input–output data from system identification experiments are used in training the network using the Levenberg–Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC–PI control scheme.     

具有未知负载扰动的水井钻机电液伺服系统无模型自适应控制

针对电液伺服系统在水井钻机推进工况下存在的参数不确定以及未知负载扰动突变等非线性因素,提出了基于径向基(RBF)神经网络扰动观测器的无模型自适应控制方法.首先,通过改进的无模型自适应控制动态线性化方法,将被控系统线性化为与输入输出相关的增量形式,并将未知负载扰动合并到一个非线性项中;然后,设计了径向基神经网络扰动观测器对含有未知负载扰动的非线性项进行估计,作为对未知扰动的补偿;最后,设计了时变参数估计律,通过在线调整伪偏导数,给出了电液伺服系统的控制更新律.仿真结果表明,所设计的控制器能够对未知负载扰动突变进行补偿,并能确保跟踪误差有界收敛.     

Design of explicit model predictive control for constrained linear systems with disturbances

On-line model predictive control approaches require the online solution of an optimization problem. In contrast, the explicit model predictive control moves major part of computation offline. Therefore, eMPC enables one to implement a MPC in real time for wide range of fast systems. The eMPC approach requires the exact system model and results a piecewise affine control law defined on a polyhedral partition in the state space. As an important limitation, disturbances may reduce performance of the explicit model predictive control. This paper presents efficient approach for handling the problem of using eMPC for constrained systems with disturbances. It proposes an approach to improve performance of the closed loop system by designing a suitable state and disturbance estimator. Conditions for observability of the disturbances are considered and it is depicted that applying the disturbance’s estimation leads to rejection of the response error. It is also shown that the proposed approach prevents the reduction of feasible space. Simulation results illustrate the advantages of this approach.     

Modified Smith predictor with a robust disturbance reduction scheme for linear systems with small time delays

This paper presents a robust disturbance reduction scheme using an artificial neural network (ANN) for linear systems with small time delays. It is assumed that the nominal linear systems are stable, minimum phase and relative degree one systems. The proposed structure is an integration of a modified Smith predictor and an ANN‐based disturbance reduction scheme. Unlike other disturbance rejection methods, the proposed approach does not require information about unknown load disturbance frequencies. An ANN is used to approximate the unknown load disturbances and to enhance the robustness of the proposed disturbance reduction scheme against modelling errors in the estimated time delay and the process model. Connective weights of the ANN are trained on‐line using a back‐propagation algorithm until uncertainties resulting from unknown load disturbances and modelling errors are minimized. The simulation results show the effectiveness of the presented disturbance reduction scheme for controlling linear delay systems subjected to step or periodic unknown load disturbances.     

基于HOD的无模型四旋翼RBF滑模控制

为了探索解决在无模型控制算法中如何对系统的未知模型和扰动进行准确估计,提出一种基于高阶微分器(HOD)的无模型RBF神经网络滑模控制器(HODRBFSMC).引入HOD估计系统模型的各阶状态变量,并将系统模型的未知项和外界干扰统一归为总扰动,通过RBF神经网络对总扰动进行估计,并根据Lyapunov定理证明所设计控制器的闭环稳定性.为验证控制器的有效性,所设计的控制器被应用于四旋翼飞行器的轨迹控制,解决其模型参数复杂且飞行过程中易受外界干扰的问题.仿真实验表明,所提出方法能够有效估计并补偿总扰动,其轨迹跟踪能力和抗干扰性能相比PID和高阶微分反馈控制(HODFC)具有一定的优越性,能够很好地满足四旋翼飞行器控制的需求.     

浊度大时滞过程的预测自抗扰控制器设计

待滤水浊度过程涉及复杂的物理、化学反应,具有明显的大时滞、不确定性和干扰多等特点,一直是制水行业公认的难控系统.针对其干扰多和不确定性特点,采用自抗扰控制来主动实时估计扰动并进行补偿;针对其大时滞特点,采用预测控制对输出提前预报来弥补大时滞系统中的信息不及时,从而得到一种既具信息预估又具主动补偿总扰动的预测自抗扰控制器.本文重点分析了预测自抗扰控制器的性能,给出了时滞系统在该控制器作用下的开环频率参数求取方法及简单实用的参数整定公式,最后将其与几种常见控制器进行了仿真比较.仿真结果表明:预测自抗扰控制器具有良好的抗扰恢复能力和设定值跟踪能力,且参数整定容易,具有简单、好用且有效等特点,为该控制器的工业化应用提供了积极的指导作用.