基于启发式遗传算法的非线性神经网络预测控制器

本文提出一种以小脑模型（CMAC）网络为多步预测模型的非线性预测控制算法，并将启发式遗传算法引入到滚动优化中，以提高优化过程中的收敛速度和求解精度。仿真结果表明该算法是有效可行的。     

Robust model predictive control design with input constraints

The paper addresses the problem of designing a robust output/state model predictive control for linear polytopic systems with input constraints. The new predictive and control horizon model is derived as a linear polytopic system. Lyapunov function approach guarantees the quadratic stability and guaranteed cost for closed-loop system. The invariant set and an algorithm approach similar to Soft Variable-Structure Control (SVSC), ensures input constraints for the model predictive plant control system. In the proposed control scheme, the required on-line computation load is significantly less than in MPC literature, which opens the possibility to use these control design schemes not only for plants with slow dynamics, but also for faster ones.     

A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making

Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.     

基于MATLAB动态矩阵控制算法设计仿真

动态矩阵控制是预测控制中一种重要的算法。本文详细介绍了动态矩阵控制算法的特征及其优缺点。动态矩阵控制算法主要包括预测模型、滚动优化、误差校正和闭环控制形式。通过对动态矩阵控制的MATLAB仿真并将其与传统的PID控制比较。发现其对直接处理带有纯滞后、大惯性的对象,有良好的跟踪性和有较强的鲁棒性。因此有广泛的应用前景。同时还基于MATLAB软件平台设计出关于动态矩阵控制算法的界面。输入已知的控制模型。通过对参数的选择,来取得良好的控制效果。     

基于模糊集相似度量的装载机臂方案选择方法

描述了模糊集之间的相似度量与加权相似度量方法,根据模糊集之间的相似度量提出了装载机臂方案的选择方法。在臂部机构方案选择中,模糊集之间的相似度量是评价机构设计方案接近期望方案（理想方案）的度量,相似度量值越大,机构设计方案越接近期望方案。通过实例阐明模糊集之间的相似度量在装载机臂的方案选择中是可行的。     

Iterated non-linear model predictive control based on tubes and contractive constraints

This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.     

基于模型预测控制的灯光调节系统设计

高压钠灯是人工温室常用的光源，控制比较困难。结合模型预测控制策略，设计了高压钠灯连续调光二级控制系统。实验结果表明，在存在干扰情况下，系统仍能获得满意的控制效果。     

基于虚拟仪器的化学激光器测控系统

本文针对燃烧驱动的连续波HF化学激光器的特点,通过先进的虚拟仪器技术和LabVIEW 8.0编程,设计研究了一套测控系统。该系统能控制激光器的气流压力,实时地采集和分析相关测试数据,并能显示,保存最终测试结果。     

A support vector machine based control application to the experimental three-tank system

This paper presents a support vector machine (SVM) approach to generalized predictive control (GPC) of multiple-input multiple-output (MIMO) nonlinear systems. The possession of higher generalization potential and at the same time avoidance of getting stuck into the local minima have motivated us to employ SVM algorithms for modeling MIMO systems. Based on the SVM model, detailed and compact formulations for calculating predictions and gradient information, which are used in the computation of the optimal control action, are given in the paper. The proposed MIMO SVM-based GPC method has been verified on an experimental three-tank liquid level control system. Experimental results have shown that the proposed method can handle the control task successfully for different reference trajectories. Moreover, a detailed discussion on data gathering, model selection and effects of the control parameters have been given in this paper.     

基于T-S模糊模型的辨识与控制

针对一类非线性系统,采用模糊辫识的方法建立系统的T-S模型,结合广义预测控制对模糊预测控制进行研究.应用模糊聚类和递推最小二乘法辫识T-S模糊模型的前提结构和结论参数.对于非线性系统来讲,T-5模糊模型有良好的描述特性,并结合广义预测控制的滚动优化,实现对非线性系统的有效控制.仿真结果说明了该算法的有效性.     

基于频域分析的预测控制鲁棒性设计

从分析预测控制的内模机理出发，把系统稳定鲁棒性和品质鲁棒性要求作为内模控制器设计的两个约束条件，利用控制理论中频域分析的方法把离散控制系统的控制器设计转化到频域进行分析，推导出满足系统稳定鲁棒性和品质鲁棒性条件的内模控制器，从而得出预测控制鲁棒性设计的条件，使控制系统能较好的抑制外部扰动所产生的影响。仿真实验结果表明该设计比传统的CHR和Cohen-Coon整定算法有更优异的控制效果。     

基于模型预测的P2混合动力AMT换挡后离合器结合控制策略

AMT换挡后的离合器结合过程控制好坏对系统冲击有较大的影响,针对P2型单轴并联混合动力汽车离合器的结合过程中,离合器接合阶段离合器尽快接合和冲击度尽可能小这两个控制目标,分析了不同驱动模式下换挡离合器接合阶段动力学过程,设计了基于模型预测控制的离合器接合控制策略,搭建了Amesim-Simulink联合仿真模型对控制策...     

Model predictive control based on reduced order models applied to belt conveyor system

In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system.     

基于模糊PID控制器的流量控制设计

现今流量控制系统很多都使用常规PID控制器,这样虽然比较方便,但不能在线整定参数,因而不能很好地控制非线性等复杂系统.在本系统中,采用模糊PID控制器来设计控制流量.模糊PID控制器将PID控制和模糊控制两种方法结合起来,既能消除稳态误差,又能对复杂系统进行简单有效的控制,从而达到控制要求.     

A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions

This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods.     

基于遗传算法的动态矩阵控制器参数设计

动态矩阵控制(DMC)采用阶跃响应序列作为对象模型,它结构清晰、模型获取简便、算法实现简单、鲁棒性强,因此得到广泛的应用。通常用试凑结合仿真的方法设计DMC控制器参数,但往往不能达到最佳控制效果。中以某药物在血液中的循环吸收过程作为仿真对象,将基于浮点数编码的遗传算法(GA)应用于DMC控制器参数设计,获得了满意的控制效果;中还给出了DMC动态参数的设计方案。     

IMC-PID design based on model matching approach and closed-loop shaping

Motivated by the limitations of the conventional internal model control (IMC), this communication addresses the design of IMC-based PID in terms of the robust performance of the control system. The IMC controller form is obtained by solving an H-infinity problem based on the model matching approach, and the parameters are determined by closed-loop shaping. The shaping of the closed-loop transfer function is considered both for the set-point tracking and for the load disturbance rejection. The design procedure is formulated as a multi-objective optimization problem which is solved by a specific optimization algorithm. A nice feature of this design method is that it permits a clear tradeoff between robustness and performance. Simulation examples show that the proposed method is effective and has a wide applicability.     

基于改进模型预测控制的加热炉温控系统研究

模型预测控制具有鲁棒性强、跟踪快速性好等特点,能够解决复杂工业过程控制中的大时滞问题。该文提出了一种改进的模型预测控制器,基于事先设定的期望响应,实现参考轨迹在预测时域内最大限度地渐进期望轨迹,使系统响应能够准确地跟踪期望轨迹。该文以硅单晶体加热炉为控制对象,分别采用PID、DMC和改进DMC三种控制方法进行预测控制。仿真结果表明改进的DMC控制方法比传统的预测控制方法具有更好的动态响应性能和跟踪效果。     

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy.     

Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input constraints

This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi–Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler–turbine system demonstrate the effectiveness of the proposed control scheme.