基于混沌粒子群优化的约束状态反馈预测控制算法

提出一种基于混沌粒子群优化的约束状态反馈预测控制算法,用于解决带有输入约束和状态约束的控制问题.将混沌粒子群优化引入到约束状态反馈预测控制的滚动优化过程中,增强了算法在约束范围内的局部搜索和全局搜索能力.通过对一个实际的带有约束的线性离散系统控制优化问题的解决,验证了基于混沌粒子群优化的状态反馈预测控制算法的可行性和有效性,与传统的二次规划算法的比较结果说明了此算法的优越性,证明了状态反馈预测控制系统良好的鲁棒性.     

一种新的基于PSO的MPC及其硬件实现

模型预测控制(MPC)在流程工业中应用已经比较成熟.其核心为在线求解二次规划(QP)问题,运算负荷大时延长,对控制器的运算能力要求高,阻碍了MPC向更深更广的应用领域拓展.为解决上述问题,从算法本身和硬件平台2个方面入手,提出了MPC算法一种新的实现方案.新的以粒子群优化算法(PSO)为核心的MPC算法很好地解决了带约束的二次规划问题,并且以可编程逻辑门阵列(FPGA)为平台用实现了PSO-MPC控制器.这一方案使得MPC可以应用在控制器体积受限,采样频率高的运动控制场合.     

基于SVM和PSO的新型非线性模型预测控制

对于具有强非线性或复杂非线性约束的系统,通过非线性模型的线性化和二次规划优化实现非线性模型预测控制,难以取得满意的结果。提出了一种基于支持向量机模型和粒子群优化的非线性模型预测控制系统的算法。仿真实例表明了支持向量机模型的泛化能力和粒子群优化的寻优速度及能力,证明了将其运用于非线性模型预测控制中的可行性。     

基于混合粒子群优化算法的现金流优化

以最大化现金流净现值为优化目标的多模式资源约束调度问题MMRCPSP(Multi-mode Resource-Constrained Project Scheduling Problem)是一类带有复杂非线性特征的NP-hard问题,传统粒子群算法在解决该类离散问题上具有一定局限性。从粒子群算法的优化原理出发,结合遗传算法,在粒子群算法中引入交叉和变异操作,得出一种应用于MMRCPSP现金流优化的快速、易实现的混合粒子群算法,拓宽了粒子群优化算法在离散优化领域的应用。仿真实验结果验证了算法的有效性和高效性。     

求解约束优化问题的动态邻域粒子群算法*

粒子群算法(PSO)求解约束优化问题存在较严重的早熟收敛现象,为了有效抑制早熟收敛,提出了基于改进的约束自适应方法的动态邻域粒子群算法(IPSO)。算法采用动态邻域策略提高算法的全局搜索能力,设计了一种改进的自适应约束处理方法,根据迭代代数线性增加搜索偏向系数,在早期偏向于搜索可行解,在后期偏向于搜索最优解,并引入序列二次规划增强算法的局部搜索能力。通过基准测试函数实验对比分析,表明该算法对于约束优化问题具有较好的全局收敛性。     

小型无人直升机的模型预测控制算法研究

针对无人直升机在阵风干扰环境中的姿态控制精度低的问题.本文将非线性刚体动力学模型在悬停点应用小扰动理论得到了线性化数学模型.考虑系统输入输出和控制量约束,采用模型预测控制将控制器的设计问题转化为每个采样时刻求解一个带不等式和等式约束的凸二次规划问题.通过设计终端状态约束解决了有限时域模型预测控制(model predictive control, MPC)算法的稳定性问题,并通过引入松弛变量使得约束优化问题更容易求解.随机和常值阵风干扰下无人机悬停仿真验证了本文MPC预测控制器具有幅度不超过0.25 m/s的良好干扰抑制能力,性能明显优于线性二次型调节器(linear-quadratic regulator, LQR).     

一种求解多目标0-1规划问题的自适应粒子群算法

对于带有线性约束的多目标0-1规划问题,给出了一种自适应的粒子群优化算法。该算法利用变换来控制模型的线性约束,并通过对各目标函数进行自适应加权的方式形成适应度函数。数值结果表明该算法是有效的,可以求解实际应用中的一些模型。     

基于粒子群优化的约束广义预测控制实现方法

用微粒群优化算法解决存在约束的广义预测控制的优化问题,并给出了基于微粒群优化算法的广义预测控制算法的实现方法.将该算法应用到工业过程对象中进行测试,仿真结果表明了算法的有效性和高效性,获得了良好的控制效果.     

改进的PSOGM算法在动态关联规则挖掘中的应用

针对动态关联规则挖掘中支持度向量和置信度向量变化趋势的分析和预测,提出一种改进的粒子群优化的灰色模型应用在动态关联规则挖掘中。由于灰色模型在引入背景值后导致在非平稳序列中的预测精度下降,因此有必要引入参数进行修正,通过在粒子群优化算法中引入二次搜索机制,优化求解灰色模型不同时刻的背景值,从而提高粒子群算法的局部搜索能力,进而提高灰色模型的预测精度。通过在Matlab平台上进行实验仿真,数据集采用超市购物数据,结果表明该方法比原始灰色模型、遗传算法优化的灰色模型和标准的粒子群优化的灰色模型具有更高的预测精度。     

一种改进细菌觅食优化算法及其在软测量建模中的应用

针对软测量建模中模型参数的优化需求,在分析细菌觅食优化算法(BFOA)和粒子群优化(PSO)算法的基础上,将二者有机结合,提出了一种新型细菌觅食粒子群混合优化算法(BSOA)。该算法将PSO粒子移动的思想引入BFOA,有效解决了BFOA趋向性操作中细菌位置更新的盲目性。将其分别用于典型函数的寻优与成品油研究法辛烷值最小二乘支持向量机(LSSVM)模型参数的优化,仿真结果表明:该方法有效增强了算法的全局寻优能力与收敛速度,并在一定程度上改善了模型的预测精度与泛化能力。     

A global piecewise smooth Newton method for fast large-scale model predictive control

In this paper, the strictly convex quadratic program (QP) arising in model predictive control (MPC) for constrained linear systems is reformulated as a system of piecewise affine equations. A regularized piecewise smooth Newton method with exact line search on a convex, differentiable, piecewise-quadratic merit function is proposed for the solution of the reformulated problem. The algorithm has considerable merits when applied to MPC over standard active set or interior point algorithms. Its performance is tested and compared against state-of-the-art QP solvers on a series of benchmark problems. The proposed algorithm is orders of magnitudes faster, especially for large-scale problems and long horizons. For example, for the challenging crude distillation unit model of Pannocchia, Rawlings, and Wright (2007) with 252 states, 32 inputs, and 90 outputs, the average running time of the proposed approach is 1.57 ms.     

Robust constrained predictive control using comparison model

This paper proposes a quadratic programming (QP) approach to robust model predictive control (MPC) for constrained linear systems having both model uncertainties and bounded disturbances. To this end, we construct an additional comparison model for worst-case analysis based on a robust control Lyapunov function (RCLF) for the unconstrained system (not necessarily an RCLF in the presence of constraints). This comparison model enables us to transform the given robust MPC problem into a nominal one without uncertain terms. Based on a terminal constraint obtained from the comparison model, we derive a condition for initial states under which the ultimate boundedness of the closed loop is guaranteed without violating state and control constraints. Since this terminal condition is described by linear constraints, the control optimization can be reduced to a QP problem.     

Steady-state target optimization designs for integrating real-time optimization and model predictive control

In industrial practice, the optimal steady-state operation of continuous-time processes is typically addressed by a control hierarchy involving various layers. Therein, the real-time optimization (RTO) layer computes the optimal operating point based on a nonlinear steady-state model of the plant. The optimal point is implemented by means of the model predictive control (MPC) layer, which typically uses a linear dynamical model of the plant. The MPC layer usually includes two stages: a steady-state target optimization (SSTO) followed by the MPC dynamic regulator. In this work, we consider the integration of RTO with MPC in the presence of plant-model mismatch and constraints, by focusing on the design of the SSTO problem. Three different quadratic program (QP) designs are considered: (i) the standard design that finds steady-state targets that are as close as possible to the RTO setpoints; (ii) a novel optimizing control design that tracks the active constraints and the optimal inputs for the remaining degrees of freedom; and (iii) an improved QP approximation design were the SSTO problem approximates the RTO problem. The main advantage of the strategies (ii) and (iii) is in the improved optimality of the stationary operating points reached by the SSTO-MPC control system. The performance of the different SSTO designs is illustrated in simulation for several case studies.     

Proportioning with second-order information for model predictive control

We propose an algorithm for the effective solution of quadratic programming (QP) problems arising from model predictive control (MPC). MPC is a modern multivariable control method which gives the solution for a QP problem at each sample instant. Our algorithm combines the active-set strategy with the proportioning test to decide when to leave the actual active set. For the minimization in the face, we use a direct solver implemented by the Cholesky factors updates. The performance of the algorithm is illustrated by numerical experiments, and the results are compared with the state-of-the-art solvers on benchmarks from MPC.     

Optimizing model predictive control of an industrial distillation column

The main scope of this work is the implementation of an MPC that integrates the control and the economic optimization of the system. The two problems are solved simultaneously through the modification of the control cost function that includes an additional term related to the economic objective. The optimizing MPC is based on a quadratic program (QP) as the conventional MPC and can be solved with the available QP solvers. The method was implemented in an industrial distillation system, and the results show that the approach is efficient and can be used, in several practical cases.