处理动态优化问题中控制变量路径约束的方法

目前国际上对动态优化问题中的状态变量路径约束已有一些研究,但专门处理控制变量路径约束的方法却鲜见报道. 本文首先介绍两种分别基于三角函数变换、约束算子截断来处理控制变量路径约束的方法,然后提出一种基于光滑化的二次罚函数方法. 光滑化罚函数方法不仅能够处理控制变量路径约束,而且还能同时处理关于状态变量的路径约束. 最后使用目前流行的控制变量参数化 (Control variable parameterization, CVP)策略对最终获得的、不再含控制变量路径约束的动态优化问题求解. 实例测试一展现了三种方法各自的特点;实例测试二表明了光滑罚函数方法的有效性和优越性.     

热率约束下高超声速飞行器Gauss时间网格参数化轨迹规划

针对热率约束下高超声速飞行器(HV)再入轨迹规划, 提出一种结合光滑化不等式约束处理和非均匀Gauss离散时间网格的改进控制变量参数化(CVP)优化算法. 首先, 结合HV动力学方程和约束条件建立了HV再入轨迹优化问题; 然后, 采用光滑化函数对不等式路径约束进行处理并引入附加状态变量转化进微分方程中; 进一步, 在CVP算法框架下, 给出了基于Gauss分布的时间网格控制参数化策略, 以此改善HV攻角控制精度进而提升HV再入航程; 最后, 在通用航空器模型上进行仿真测试, 验证提出方法的性能并分析不同热率约束限值对最大航程规划的影响. 结果显示, 相较于均匀时间网格参数化CVP–S–P方法, 改进方法再入航程增加320.1 km(提升4.1%), 表明了改进算法的有效性; 同时, 基于本文方法仿真结果, 热率限值降低对HV最大航程减少有限, 当热率限值降低15%时, 最大航程损失仅3.16%, 展示了本文方法对HV热防护设计的理论价值.     

集装箱装卸摆动最优控制快速数值求解算法

为了实现起重机集装箱摆动最优控制,提出一种基于控制向量参数化(CVP)方法的最优控制问题快速求解算法.首先,建立了以摆动能量最小为目标的集装箱装卸最优控制数学模型.其次,采用光滑化惩罚函数路径约束处理方法降低了模型求解难度.进一步,针对控制向量参数化方法微分方程组求解耗时长难题,结合网格划分提出改进四阶Runge--Kutta方法的快速CVP算法加快了最优控制问题求解速度.仿真测试针对不同位置的集装箱装卸任务进行.数值测试结果显示,相较于其他变步长求解方法,改进方法在得到相近求解精度解的同时,求解耗时明显减少,表明本文方法在集装箱装卸最优控制方面的应用价值.     

具有未知参数的非线性系统动态优化

针对具有未知参数和不等式路径约束的非线性系统动态优化问题,提出一种新颖有效的数值求解方法.首先,将未知参数视为一个动态优化问题的决策变量;其次,利用多重打靶法将无限维的含未知参数动态优化问题转化为有限维的非线性规划问题,进而在不等式路径约束违反的时间段内,用有限多个内点约束替代原不等式路径约束;然后,用内点法求解转化后的非线性规划问题,在路径约束违反的一定容许度下,经过有限多次步数迭代后得到未知参数值的同时得到控制策略,并在理论上对所提出算法的收敛性进行相应证明;最后,对两个经典的含未知参数非线性系统的动态优化问题进行数值仿真以验证所提出算法的有效性.     

一种新的遗传算法求解有等式约束的优化问题

针对有等式约束的优化问题,提出了一种新的遗传算法.该算法是在种群初始化、交叉、变异操作过程中使用求解参数方程的方法处理等式约束,违反不等式约束的个体用死亡罚函数进行惩罚设计出的实数编码遗传算法.数值实验结果表明,新算法性能优于现有其它算法;它不仅可以处理线性等式约束,而且还可以处理非线性等式约束,同时提高了收敛速度和解的精度,是一种通用强、高效稳健的智能算法.     

计算机数控系统光滑时间最优轨迹规划

基于控制向量参数化(CVP)方法, 研究了计算机数控(CNC)系统光滑时间最优轨迹规划方法. 通过在规划问题中引入加加速度约束, 实现轨迹的光滑给进. 引入时间归一化因子, 将加加速度约束的时间最优轨迹规划问题转化为固定时间的一般性最优控制问题. 以路径参数对时间的三阶导数(伪加加速度)和终端时刻为优化变量, 并采用分段常数近似伪加加速度, 将最优控制问题转化为一般的非线性规划(NLP)问题进行求解. 针对加加速度、加速度等过程不等式约束, 引入约束凝聚函数, 将过程约束转化为终端时刻约束, 从而显著减少约束计算. 构造目标和约束函数的Hamiltonian函数, 利用伴随方法获得求解NLP问题所需的梯度.     

改进的filter-SQP用于过程优化的研究(英文)

SQP法是求解非线性规划问题最有效的方法之一,在求解过程中,一般需要对惩罚函数进行线性搜索.惩罚因子的选择会带来一些问题,filter-SQP是Roger Fletcher和Sven Leyffer提出的一种不用惩罚函数的算法.本文红模块环境下应用改进的filter-SQP对化工过程优化进行了研究,提出了相应的算法.采用的优化策略是不可行路径法,filter中的约束目标是由断裂流方程、设计规定及不满足的不等式约束线性组合得到.使用filter检验是否接受QP步长作为下次迭代的出发点,避免了对惩罚函数进行线性搜索带来的弊端.当filter搜索失败时,提出了相应的处理策略,提高了算法的稳定性.用于判断优化是否收敛的判据不冉是K-T误差,而是目标函数和约束条件地同时收敛.提出了一个逐步规格化策略,提高了计算效率.计算实例表明,filter-SQP法优于传统的SQP法,本文提出的策略提高了算法的效率和稳定性.     

罚函数凸优化迭代算法及在无人机路径规划中的应用

针对无人机路径规划问题,建立了具有定常非线性系统、非仿射等式约束、非凸不等式约束的非凸控制问题模型,并对该模型进行了算法设计和求解。基于迭代寻优的求解思路,提出了凸优化迭代求解方法和罚函数优化策略。前者利用凹凸过程(CCCP)和泰勒公式对模型进行凸化处理,后者将经处理项作为惩罚项施加到目标函数中以解决初始点可行性限制。经证明该方法严格收敛到原问题的Karush-Kuhn-Tucker(KKT)点。仿真实验验证了罚函数凸优化迭代算法的可行性和优越性,表明该算法能够为无人机规划出一条满足条件的飞行路径。     

具有不等式路径约束的微分代数方程系统的动态优化

针对具有不等式路径约束的微分代数方程（Differential-algebraic equations，DAE）系统的动态优化问题，通常将DAE中的等式路径约束进行微分处理，或者将其转化为点约束或不等式约束进行求解.前者需要考虑初值条件的相容性或增加约束，在变量间耦合度较高的情况下这种转化求解方法是不可行的；后者将等式约束转化为其他类型的约束会增加约束条件，增加了求解难度.为了克服该缺点，本文提出了结合后向差分法对DAE直接处理来求解上述动态优化问题的方法.首先利用控制向量参数化方法将无限维的最优控制问题转化为有限维的最优控制问题，再利用分点离散法用有限个内点约束去代替原不等式路径约束，最后用序列二次规划（Sequential quadratic programming，SQP）法使得在有限步数的迭代下，得到满足用户指定的路径约束违反容忍度下的KKT（Karush Kuhn Tucker）最优点.理论上证明了该算法在有限步内收敛.最后将所提出的方法应用在具有不等式路径约束的微分代数方程系统中进行仿真，结果验证了该方法的有效性.     

大规模过程系统优化方法

基于非线性约束的序列界无约束极小化方法,对大规模过程系统稳态优化的序列界约束极小化方法(SBCMM)进行了研究.对工程模型引进松弛变量处理后,SBCMM的罚函数仅包含等式约束的惩罚项,不包含界约束及不等式约束的惩罚项.原问题的解由求解一系列界约束极小化子问题而非无约束极小化子问题来获得.最后,用一类规模可变的非线性规划...     

Control variable parameterisation with penalty approach for hypersonic vehicle reentry optimisation

An efficient trajectory optimisation approach combining the classical control variable parameterisation (CVP) with a novel smooth technology and two penalty strategies is developed to solve the trajectory optimal control problems. Since it is difficult to deal with path constraints in CVP method, the novel smooth technology is firstly employed to transform the complex constraints into one smooth constraint. Then, two penalty strategies are proposed to tackle the converted path and terminal constraints to decrease the computational complexity and improve the constraints satisfaction. Finally, a nonlinear programming problem, which approximates the original trajectory optimisation problem, is obtained. Error analysis shows that the proposed method has good convergence property. A general hypersonic cruise vehicle trajectory optimisation example is employed to test the performance of the proposed method. Numerical results show that the path and terminal conditions are well satisfied and better trajectory profiles are obtained, showing the effectiveness of the proposed method.     

Newton-conjugate gradient （CG） augmented Lagrangian method for path constrained dynamic process optimization

In this paper, a Newton-conjugate gradient (CG) augmented Lagrangian method is proposed for solving the path constrained dynamic process optimization problems. The path constraints are simplified as a single final time constraint by using a novel constraint aggregation function. Then, a control vector parameterization (CVP) approach is applied to convert the constraints simplified dynamic optimization problem into a nonlinear programming (NLP) problem with inequality constraints. By constructing an augmented Lagrangian function, the inequality constraints are introduced into the augmented objective function, and a box constrained NLP problem is generated. Then, a linear search Newton-CG approach, also known as truncated Newton (TN) approach, is applied to solve the problem. By constructing the Hamiltonian functions of objective and constraint functions, two adjoint systems are generated to calculate the gradients which are needed in the process of NLP solution. Simulation examples demonstrate the effectiveness of the algorithm.     

一种高效的快速近似控制向量参数化方法

控制向量参数化(Control vector parameterization, CVP) 方法是目前求解流程工业中最优操作问题的主流数值方法,然而,该方法的主要缺点之一是 计算效率较低,这是因为在求解生成的非线性规划(Nonlinear programming, NLP) 问题时,需要随着控制参数的调整,反复不断地求解相关的微分方程组,这也是CVP 方法中最耗时的部分.为了提高CVP 方法的计算效率,本文提出一种新颖的快速近似方法,能够有效减少微分方程组、函数值以及 梯度的计算量.最后,两个经典的最优控制问题上的测试结果及与国外成熟的最优控制 软件的比较研究表明:本文提出的快速近似CVP 方法在精度和效率上兼有良好的表现.     

Exact solutions to some minimum time problems with inequality state constraints

A heuristic method is developed for generating exact solutions to certain minimum time problems, with inequality state and control constraints. The control equation is linear and autonomous, with scalar-valued control. The state constraints are also linear inequalities. Assuming knowledge of a finite sequence, in which state and/or control constraints become active along an optimal path, the maximum principle is reduced to a set of equations and inequalities in a finite number of unknowns. A solution to the equations and inequalities determines both the solution path and a proof of its optimality. Certain types of constraint sequences lead to overdetermined equation systems, and this fact is interpreted in terms of the qualitative behavior of solutions to these problems. Two path-planning problems are solved, as illustrations of the solution technique.     

An interior penalty method for inequality constrained optimal control problems

This paper presents a penalty function approach to the solution of inequality constrained optimal control problems. The method begins with a point interior to the constraint set and approaches the optimum from within, by solving a sequence of problems with only terminal conditions as constraints. Thus, all intermediate solutions satisfy the inequality constraints. Conditions are given which guarantee that the un "constrained" problems have solutions interior to the constraint set and that in the limit these solutions converge to the constrained optimum. For linear systems with convex objective and concave inequalities, the unconstrained problems have the property that any local minimum is global. Further, under these conditions, upper and lower bounds in the optimum are easily available. Three test problems are solved and the results presented.     

A Riccati approach for constrained linear quadratic optimal control

An active-set method is proposed for solving linear quadratic optimal control problems subject to general linear inequality path constraints including mixed state-control and state-only constraints. A Riccati-based approach is developed for efficiently solving the equality constrained optimal control subproblems generated during the procedure. The solution of each subproblem requires computations that scale linearly with the horizon length. The algorithm is illustrated with numerical examples.     

Optimal robust vehicle motion control under equality and inequality constraints

In this paper, a new robust controller is proposed to improve the motion control performance of an autonomous four-wheel steering vehicle. The vehicle is subject to time-varying uncertainties caused by parameter perturbation and unmodeled disturbance. First of all, the vehicle motion control problem is modeled as a constraint-following problem with the goal to drive the vehicle system to follow the given constraints. For safety reasons, inequality constraints are imposed on the lateral displacement of the vehicle. Next, the diffeomorphism mapping method is used to deal with the inequality constraints in the vehicle motion control, and the constrained lateral displacement space is mapped to an unbounded space. On this basis, a new multi-parameter robust constraint-following control is designed. Based on Lyapunov stability theory, the approximate constraint tracking performance of the path tracking system is proved. In order to make a trade-off between the system performance and control cost, a multi-parameter optimization problem is established, and the optimal robust controller is obtained. Last, the main theoretical results are verified by the Carsim-Simulink co-simulations.     

An iterative algorithm for optimum control problems with inequality constraints

This paper presents an iterative algorithm to approximate inequality constrained optimal control problems. The method uses Pontryagin's necessary conditions of optimality with a penalty method. The initial values of the adjoint vectorPsi(t), and the penalty coefficients are evaluated in such a way that the final conditions are satisfied and the extremal distances between the obtained trajectory and the constraints are imposed. The computing time is remarkably small. This method can treat linear problems with fixed or variable final time with mixed or simple constraints. A test problem is solved.