基于T-S 模型和小世界优化算法的广义非线性预测控制

提出一种新型的基于T-S模糊模型和小世界优化算法的广义非线性预测控制策略.采用基于混沌遗传算法的T-S模糊模型描述复杂非线性系统的动态特性,构成模糊多步预报器.同时,针对现有基于二进制和十进制编码小世界优化算法运行时间长等缺点,提出一种新型的基于实数编码的小世界优化算法,函数测试和应用于非线性预测控制的滚动优化反映了其较强的寻优能力.最后,将其应用于基于实际数据的T-S模糊模型的广义非线性预测控制,满足了系统实时性和快速稳定性的要求.     

基于混沌优化的非线性预测控制器

针对非线性系统的控制问题,本文将神经网络辨识、混沌优化和预测控制思想有机结合,提出了一种新型非线性预测控制器.该控制器以神经网络作为预测模型,混沌优化算法作为滚动优化策略,避免了非线性预测控制中复杂的梯度计算和矩阵求逆问题.另外在训练神经网络过程中,采用了带混沌机制的自适应学习率的BP算法,以提高神经网络的收敛能力和收敛速度.仿真研究说明了该非线性预测控制器的有效性及实时性.     

基于集结策略的非线性稳定预测控制器

针对有约束非线性系统预测控制在线计算量大的问题，引入集结策略降低其在线计算量并重点讨论了系统的稳定性问题．指出当控制器的终端状态处于某集合内且集结衰减系数的上界满足一定条件时，其最优目标函数递减．进而提出了一个双模控制律，可使系统渐近稳定．最后，通过仿真对该结论进行了验证．     

基于高速收敛粒子群优化算法的非线性系统无模型预测控制

基于无模型控制、粒子群优化和预测控制的思想,提出一种新型非线性无模型预测控制器,并对该控制器的收敛性进行了分析.该控制器以带误差修正的泛模型为预测模型,以高速收敛的粒子群优化算法为滚动优化策略,不仅避免了非线性预测控制中复杂的矩阵求逆运算,而且提高了算法的收敛速度,增强了实时性.仿真研究表明了该控制器的有效性.     

Globally optimal nonlinear model predictive control based on multi-parametric disaggregation

Nonlinear model predictive control is appropriate for controlling highly nonlinear processes, particularly when operating conditions change frequently. If the problem is nonconvex, the controller must lead the process to a global, rather than a local optimum. This work deals with computation of the control actions which lead to the global optimum via the normalized multi-parametric disaggregation technique. The continuous process model is transformed into a nonlinear programming (NLP) problem via discretization which uses an implicit integration method. The NLP problem is relaxed into a mixed integer linear programming (MILP) model. Iterations between solving MILP (lower bound) and using its solution as a starting point for a local nonlinear optimizer (which computes the upper bound) continue until the gap is closed (an l₁-norm objective function is used). Controller performance is illustrated by several examples. Relative simplicity of the algorithm makes it possible to be implemented by a wide audience.     

改进教与学优化算法的LQR控制器优化设计

为了快速有效地确定线性二次最优控制(linear quadratic regulator,LQR)问题中的加权矩阵Q和R,针对主动悬架LQR控制器权系数设计问题,提出一种改进的教与学优化算法进行LQR优化设计。算法对基本教与学优化算法中的"教"与"学"阶段进行了进一步的改进,同时提出一种"自我学习"策略。通过仿真实验表明,和基本教与学算法、粒子群算法、遗传算法相比,本文算法在对主动悬架LQR控制器优化时,具有收敛速度快,求解精度高和稳定性强等优势。     

采用dsDNA-MC优化的非线性系统预测控制

将基于DNA双链结构的膜计算优化方法（dsDNA-MC）用于输入受限的非线性预测控制器设计,提出了基于dsDNA-MC优化的非线性系统预测控制算法。在对单输入单输出非线性系统预测控制分析的基础上,将非线性系统预测控制问题归结为具有输入约束的非线性系统优化问题,并采用dsDNA-MC算法来求解这一问题。仿真结果表明该算法可行、有效。     

基于神经网络模型的非线性多步预测学习控制器

构造出一种建模网络,通过对它的学习来辨识过程动态,通过对广义预测控制目标函数的在线优化求得控制律.仿真结果验证了该算法的有效性.     

基于遗传算法的非线性模型预测控制方法

介绍了非线性模型预调控制算法结构，提出了基于遗传算法的非线性模型预测控制方法，将遗传算法作为优化技术用于受限非线性模型预测控制器的设计。算法采用双模控制策略，将保证预测控制算法稳定性的终点等式约束转化为终点不等式约束，以利于遗传算法的实施。基于不变集理论，给出了非线性模型预测控制算法的稳定性定理。仿真结果表明了所提出控制算法的可行性和有效性。     

Distributed nonlinear model predictive control based on contraction theory

A novel distributed model predictive control algorithm for continuous‐time nonlinear systems is proposed in this paper. Contraction theory is used to estimate the prediction error in the algorithm, leading to new feasibility and stability conditions. Compared to existing analysis based on Lipschitz continuity, the proposed approach gives a distributed model predictive control algorithm under less conservative conditions, allowing stronger couplings between subsystems and a larger sampling interval when the subsystems satisfy the specified contraction conditions. A numerical example is given to illustrate the effectiveness and advantage of the proposed approach.     

基于信赖域二次规划的非线性模型预测控制优化算法

针对非线性预测控制如何在有限时域内有效的求解非凸非线性规划这一关键问题, 本文采用序列二次规划方法, 将非线性规划转化为一系列二次子规划求解. 首先根据非线性规划联立方法将系统状态和控制量同时作为优化变量, 得到以控制量步长为优化变量, 只包含不等式约束的子二次规划问题, 并用它取代原SQP子规划, 减小了子问题的规模; 随后采用基于信赖域二次规划的方法求解子规划问题, 保证每次迭代的可行性; 同时采用一种能够保持SQP问题Hessian矩阵稀疏结构的更新方法, 也在一定程度上降低了算法的复杂程度.最后的仿真结果表明了该方法的有效性.     

Adaptive control Lyapunov function based model predictive control for continuous nonlinear systems

A design of adaptive model predictive control (MPC) based on adaptive control Lyapunov function (aCLF) is proposed in this article for nonlinear continuous systems with part of its dynamics being unknown at the starting time. Specifically, to guarantee the convergence of the closed-loop system with online predictive model updating, a stability constraint is designed. It limits the aCLF of the system under the MPC to be less than that under an online updated auxiliary adaptive control. The auxiliary adaptive control which implements in a sampling-hold fashion can guarantee the convergence of the controlled system. The sufficient conditions that guarantee the states to be steered to a small region near the equilibrium by the proposed MPC are provided. The calculation of the proposed algorithm does not depend on the model mismatch at the starting time. And it does not require the Lyapunov function of the state of the real system always to be reduced at each time. These provide the potential to improve the performance of the closed-loop system. The effectiveness of the proposed method is illustrated through a chemical process example.     

基于灰色系统模型的预测函数控制方法研究

以灰色系统模型为基础，建立了一种预测函数控制方法．分析了灰色系统建模、模型预测输出和控制量计算方程的求解．通过仿真结果表明，该算法具有鲁棒性好、跟踪快速和抑制干扰能力强等特点。     

A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control

This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control.     

基于多模型的非线性系统广义预测控制

对于复杂的离散时间非线性系统，提出一种基于多模型的广义预测控制方法．通过在平衡点附近建立线性模型，并用径向基函数神经网络来补偿匹配误差，形成了非线性系统的多模型表示，然后采用模糊识别方法作为切换法则，并结合广义预测控制构成了多模型广义预测控制器．通过对连续发酵过程的计算机仿真，表明了该方法的有效性．     

Tube‐based robust nonlinear model predictive control

This paper extends tube‐based model predictive control of linear systems to achieve robust control of nonlinear systems subject to additive disturbances. A central or reference trajectory is determined by solving a nominal optimal control problem. The local linear controller, employed in tube‐based robust control of linear systems, is replaced by an ancillary model predictive controller that forces the trajectories of the disturbed system to lie in a tube whose center is the reference trajectory thereby enabling robust control of uncertain nonlinear systems to be achieved. Copyright © 2011 John Wiley & Sons, Ltd.     

Nonlinear model predictive control using deterministic global optimization

This paper presents a Nonlinear Model Predictive Control (NMPC) algorithm utilizing a deterministic global optimization method. Utilizing local techniques on nonlinear nonconvex problems leaves one susceptible to suboptimal solutions at each iteration. In complex problems, local solver reliability is difficult to predict and dependent upon the choice of initial guess. This paper demonstrates the application of a deterministic global solution technique to an example NMPC problem. A terminal state constraint is used in the example case study. In some cases the local solution method becomes infeasible, while the global solution correctly finds the feasible global solution. Increased computational burden is the most significant limitation for global optimization based online control techniques. This paper provides methods for improving the global optimization rates of convergence. This paper also shows that globally optimal NMPC methods can provide benefits over local techniques and can successfully be used for online control.     

采用预测控制的地铁节能优化控制算法

本文针对地铁列车自动运行系统(automatic train operation,ATO)一般运行情况以及晚点延迟发车情况下的节能问题,基于预测控制算法设计了地铁节能优化控制算法.利用预测控制算法的在线滚动优化特性,通过设计含有能量消耗趋势优化项的控制目标函数,控制算法能够针对节能目标实现快速动态调整.通过调节目标函数中各优化项权重的相对大小,节能算法可以在满足列车时间与路程运行指标的同时,达到降低能耗的目的.在MATLAB平台上利用真实车辆模型对提出的节能优化控制算法进行了仿真,在列车不延迟与延迟的情况下,算法都很好地平衡了跟踪目标与节能目标,为地铁能耗动态优化控制提供了可行方案.     

采用Brent优化的核学习单步预测控制算法

针对非线性SISO系统, 提出一种基于核学习辨识模型的单步预测控制算法(kernel learning one-step-ahead predictive control, KLOPC). 通过KL辨识模型得到系统的一步超前预报值, 并引入输出反馈和偏差校正以克服模型失配等因素引起的预测误差, 以此构造一步加权预测控制性能指标, 然后采用Brent一维搜索方法求取控制律. 该方法无需任何相关的导数信息, 需调整的参数少, 求解效率高. 在一非线性液位系统的仿真研究表明了KLOPC优于整定的PID和其它基于KL模型的控制方法, 对噪声和扰动等均具有更好的鲁棒性和自适应性.     

基于粒子群优化的有约束模型预测控制器

研究了模型预测控制(MPC)中解决带约束的优化问题时所用到的优化算法,针对传统的二次规划(QP)方法的不足,引入了一种带有混沌初始化的粒子群优化算法(CPSO),将其应用到模型预测控制中,用十解决同时带有输入约束和状态约束的控制问题.最后,引入了一个实际的带有约束的线性离散系统的优化控制问题,分别用二次规划和粒子群优化两种算法去解决,通过仿真结果的比较,说明了基于粒子群优化(PSO)的模型预测控制算法的优越性.