控制量离散的轮式小车轨迹跟踪研究

针对机器人小车控制过程中的轨迹跟踪问题,以控制量为离散值的轮式小车为研究对象,提出一种新的预测控制算法.建立小车在离散状态空间下的运动学模型,并根据此模型设计预测控制算法,以克服实际过程中的不确定性.然后,为解决传统预测控制算法在应用上出现的计算量指数增长问题,基于改进模拟退火的快速寻优算法,设计一种新的预测控制策略,以同时保证小车轨迹跟踪的精确性与实时性.通过仿真实验给出了该算法下小车对不同轨迹的跟踪情况及鲁棒性测试,在与传统预测控制算法计算量的比较结果中表明,该算法能够减少计算时间且实现对轨迹有效地跟踪,并保证较高的稳定性,同时,该算法可以推广到各类控制量为离散值的预测控制问题.     

基于Hammerstein模型预测控制的分析与研究

针对基于Ｈａｍｍｅｒｓｔｅｉｎ模型非线性预测控制中,由中间量经解方程求控制量出现的问题,深入地控制了用两各以方法处理该问题对系统控制质量的影响,得出了相应的结论。     

基于即时学习的MIMO系统滑模预测控制方法

针对MIMO非线性系统的控制问题,采用数据驱动的控制策略,将具有本质自适应能力的即时学习算法与具有强鲁棒性的滑模预测控制相结合,设计了一种基于即时学习的滑模预测（LL-SMPC）控制方法．该方法在在线局部建模的基础上,采用滑模预测控制律求取最优控制量,具有较强的自适应和抗干扰能力,并避免TDiophantine方程的求解,有效减少了计算量．通过仿真研究,验证了算法的有效性．     

模糊预测控制炉窑温度系统中应用

针对炉窑温度系统的大时滞、多扰动和非线性的特点,将T-S模糊状态空间模型作为预测控制的预测模型,并将T-S模糊表示的非线性系统转化为线性时变系统,给出了基于状态空间的多变量复杂系统的T-S模糊模型表达形式,设计出预测时域内多模型的非线性模糊预测控制器。根据实际控制中对控制量和输出的约束,将控制器输出求解转化为二次规划问题。     

基于时滞补偿器的航空发动机最优滑模预测控制

针对航空发动机控制系统中存在的时滞(即延迟)问题,提出了基于时滞补偿器的滑模最优预测控制.定义特殊线性变换,将原发动机中含状态量和控制量时滞环节的控制系统化为无时滞系统;在新的坐标系下采用时滞补偿器以最小修正误差建立最优二次型指标,求出最优的滑模预测控制量并进行了系统仿真.结果表明:该方法能够很好地补偿航空发动机控制系统中时滞环节带来的影响,对系统进行提前控制,系统响应速度较快,整体效果达到预想目标.     

比例–积分控制加广义预测控制算法及其应用

针对比例–积分(proportional-integral, PI)控制因不能预测未来输出而提前改变控制量使其用于光电稳定伺服系统时往往响应剧烈的问题,研究了光电稳定伺服系统的广义预测控制(generalized predictive control, GPC).首先通过证明受控自回归积分滑动平均(controlled auto-regressive integral moving-average, CARIMA)模型的直接递推预测与Diophantine方程预测等价,提出了预测较快的模型等价预测GPC算法,其预测复杂度比原GPC降低了一个阶次.其次通过对PI和GPC的特点进行分析,综合考虑两者的优缺点,提出了一种新型的基于PI增量和GPC增量加权的比例积分控制加广义预测控制(proportional-integral control plus generalized predictive control, PI+GPC)算法,实现了基于历史、当前和未来偏差计算控制量,并给出了算法设计流程和参数选取规则.最后通过仿真并在某光电稳定伺服平台上验证后得出, PI+GPC和PI相比稳定精度有所提高,且平稳性和快速性大为改善.     

多变量预测控制结构分解的图论方法

预测控制算法的计算复杂度主要由变量个数和控制时域决定, 而大型复杂系统中变量个数较多将导致计 算量大的问题, 尤其在有约束预测控制的优化求解中增加较重的计算负担. 本文针对此问题利用邻接矩阵、可达矩 阵和关联矩阵梳理系统传递函数模型中变量之间的关联, 将有关联的控制变量划分为一个子系统, 进而将一个大系 统分解成若干独立子系统, 即可将一个高维度的优化求解问题分解成多个维度较低的子优化问题, 降低计算复杂度 以达到减少计算量的目的. 最后将其应用在多变量有约束的双层结构预测控制算法中, 通过仿真进行验证.     

模型预测控制的新发展

随着模型预测控制（MPC）的广泛应用，其向智能化的方向发展成为必然，因此在近年来MPC发展的基础上，本文详细综述了预测控制包括多变量、约束、鲁棒、非线性等方面的工作，概述了预测控制与先进的控制算法的结合状况，并对存在的问题进行了探讨，从理论上分析了其智能化发展趋势和方向。     

基于多步控制集的鲁棒预测控制器设计

针对有约束多胞不确定系统, 本文提出多步控制集的概念, 并将其作为终端集进而设计鲁棒预测控制器. 由于设计了一系列可变的反馈律, 鲁棒预测控制器可以得到更好的控制性能和更大的初始可行域. 另外, 利用多步控制集的特性, 本文提出了一种将预测控制器的在线计算量转移到离线完成的算法. 通过该算法, 可以有效地平衡鲁棒预测控制器的控制性能、在线计算量和初始可行域. 仿真算例验证了这些算法的有效性.     

Kleinman控制在GPC中的实现

解决了当控制量加权λ很小时广义预测控制增益的不可计算，以及增益计算中求逆矩阵维数随控制时域增加而增加的问题。     

Robust model predictive control for constrained continuous-time nonlinear systems

In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator.     

Constrained nonlinear MPC for accelerated tracking piece-wise references and its applications to thermal systems

In this paper, we propose a model predictive control (MPC) strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints. Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation. Then, recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions. Moreover, the local optimality of the tracking MPC is achieved for unreachable output reference signals. By comparing to traditional tracking MPC, the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here.     

基于多模糊模型的非线性预测控制

研究了基于多模糊模型的非线性预测控制问题 ,提出了基于多模型融合的非线性预测控制方法 .首先根据实际对象在不同运行点附近的状态建立了非线性系统的线性多模糊模型表示 ,然后给出了基于多模糊模型的预测控制原理结构框图 .非线性多模糊模型被用来作为预测模型 ,CSTR过程的仿真研究表明是一种有前景的非线性预测控制方法 .     

Model predictive control of continuous-time nonlinear systems with piecewise constant control

A new model predictive control (MPC) algorithm for nonlinear systems is presented. The plant under control, the state and control constraints, and the performance index to be minimized are described in continuous time, while the manipulated variables are allowed to change at fixed and uniformly distributed sampling times. In so doing, the optimization is performed with respect to sequences, as in discrete-time nonlinear MPC, but the continuous-time evolution of the system is considered as in continuous-time nonlinear MPC.     

Computation delay compensation for real time implementation of robust model predictive control

The implementation of model predictive control (MPC) requires to solve an optimization problem online. The computation time, often not negligible especially for nonlinear MPC (NMPC), introduces a delay in the feedback loop. Moreover, it impedes fast sampling rate setting for the controller to react to uncertainties quickly. In this paper, a dual time scale control scheme is proposed for linear/nonlinear systems with external disturbances. A pre-compensator works at fast sampling rate to suppress uncertainty, while the outer MPC controller updates the open loop input sequence at a slower rate. The computation delay is explicitly considered and compensated in the MPC design. Four robust MPC algorithms for linear/nonlinear systems in the literature are adopted and tailored for the proposed control scheme. The recursive feasibility and stability are rigorously analysed. Three simulation examples are provided to validate the proposed approaches.     

Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models

This paper describes a computationally efficient nonlinear Model Predictive Control (MPC) algorithm in which the neural Hammerstein model is used. The Multiple-Input Multiple-Output (MIMO) dynamic model contains a neural steady-state nonlinear part in series with a linear dynamic part. The model is linearized on-line, as a result the MPC algorithm requires solving a quadratic programming problem, the necessity of nonlinear optimization is avoided. A neutralization process is considered to discuss properties of neural Hammerstein models and to show advantages of the described MPC algorithm. In practice, the algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimization.     

A survey of industrial model predictive control technology

This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities.     

A nonlinear industrial model predictive controller using integrated PLS and neural net state-space model

Model predictive control (MPC) technology has been well developed and successfully applied in the refinery and petrochemical process industries over the last 20 years. Recent development has been focused on nonlinear MPC and robust MPC technologies because new challenges have been encountered in the polymer and chemical industries where many processes show strong nonlinearity and uncertainty. This paper presents a nonlinear industrial model predictive controller, recently developed by Aspen Technology Inc. This MPC controller uses a nonlinear, state-space, integrated partial least-squares (PLS) and neural net model (Zhao, Guiver and Sentoni, American control conference, Philadelphia, PA, USA, 1998), and a multi-step, constrained, Newton-type optimization algorithm (Oliveira and Biegler, Automatica, 31 (2) (1995) 281–286). It results in a robust and cost-effective industrial nonlinear MPC controller. A pH reactor example and a successful industrial application in NO_x emission control of a power plant are presented to demonstrate the capability of this controller.     

Nonlinear fuzzy model predictive iterative learning control for drum-type boiler–turbine system

Advanced control strategy is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. Model predictive control (MPC) has been widely used for controlling power plant. Nevertheless, MPC needs to further improve its learning ability especially as power plants are nonlinear under load-cycling operation. Iterative learning control (ILC) and MPC are both popular approaches in industrial process control and optimization. The integration of model-based ILC with a real-time feedback MPC constitutes the model predictive iterative learning control (MPILC). Considering power plant, this paper presents a nonlinear model predictive controller based on iterative learning control (NMPILC). The nonlinear power plant dynamic is described by a fuzzy model which contains local liner models. The resulting NMPILC is constituted based on this fuzzy model. Optimal performance is realized within both the time index and the iterative index. Convergence property has been proven under the fuzzy model. Deep analysis and simulations on a drum-type boiler–turbine system show the effectiveness of the fuzzy-model-based NMPILC     

The use of a cell filter for state estimation in closed-loop NMPC of low dimensional systems

Combining variants of the Kalman filter and moving horizon estimation (MHE) with nonlinear MPC has been studied before. The MHE is appealing due to its ability to impose constraints and demonstrated superiority over extended Kalman filter. However, nonlinear MPC based on MHE requires solutions to two back to back nonlinear programs. In this paper we propose to use the cell filter (CF) to provide state feedback to the MPC regulator. The cell filter is a piecewise constant approximation of the conditional probability density of the states, whose temporal evolution is modeled by an aggregate Markov chain. Since the CF is based on discretized state, input and output spaces, the curse of dimensionality limits its application to low dimensional and constrained systems. In this paper we present simulation examples of closed-loop MPC for a nonlinear reactor and agricultural pest control based on state feedback from both CF and MHE.