Sequential Inverse Optimal Control of Discrete-Time Systems

This paper presents a novel sequential inverse optimal control(SIOC)method for discrete-time systems,which calculates the unknown weight vectors of the cost function in real time using the input and output of an optimally controlled discrete-time system.The proposed method overcomes the limitations of previous approaches by eliminating the need for the invertible Jacobian assumption.It calculates the possible-solution spaces and their intersections sequentially until the dimension of the intersection space decreases to one.The remaining one-dimensional vector of the possible-solution space’s intersection represents the SIOC solution.The paper presents clear conditions for convergence and addresses the issue of noisy data by clarifying the conditions for the singular values of the matrices that relate to the possible-solution space.The effectiveness of the proposed method is demonstrated through simulation results.

SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS

A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.

Analytic optimal pose tracking control in close-range proximity operations with a non-cooperative target

This paper investigates an analytical optimal pose tracking control problem for chaser spacecraft during the close-range proximity operations with a non-cooperative space target subject to attitude tumbling and unknown orbital maneuvering.Firstly,the relative translational motion between the orbital target and the chaser spacecraft is described in the Line-of-Sight(LOS)coordinate frame along with attitude quaternion dynamics.Then,based on the coupled 6-Degree of Freedom(DOF)pose dynamic model,an analytical optimal control action consisting of constrained optimal control value,application time and its duration are proposed via exploring the iterative sequential action control algorithm.Meanwhile,the global closed-loop asymptotic stability of the proposed predictive control action is presented and discussed.Compared with traditional proximity control schemes,the highlighting advantages are that the application time and duration of the devised controller is applied discretely in light of the influence of the instantaneous pose configuration on the pose tracking performance with less energy consumptions rather than at each sample time.Finally,three groups of illustrative examples are organized to validate the effectiveness of the proposed analytical optimal pose tracking control scheme.

A novel PID controller tuning method based on optimization technique

An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) based algorithm that derives its search directions by solving quadratic programming(QP) subproblems via an infeasible interior point method(IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver.The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems.To assess the performance of the proposed method,a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin(GPM) method and Ziegler-Nichols(ZN) method.The results reveal that,for both step and impulse response tests,the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time,settling-time and remarkably lower overshoot compared to GPM and ZN methods,and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.

On the Stable Sequential Kuhn-Tucker Theorem and Its Applications

The Kuhn-Tucker theorem in nondifferential form is a well-known classical optimality criterion for a convex programming problems which is true for a convex problem in the case when a Kuhn-Tucker vector exists. It is natural to extract two features connected with the classical theorem. The first of them consists in its possible “impracticability” (the Kuhn-Tucker vector does not exist). The second feature is connected with possible “instability” of the classical theorem with respect to the errors in the initial data. The article deals with the so-called regularized Kuhn-Tucker theorem in nondifferential sequential form which contains its classical analogue. A proof of the regularized theorem is based on the dual regularization method. This theorem is an assertion without regularity assumptions in terms of minimizing sequences about possibility of approximation of the solution of the convex programming problem by minimizers of its regular Lagrangian, that are constructively generated by means of the dual regularization method. The major distinctive property of the regularized Kuhn-Tucker theorem consists that it is free from two lacks of its classical analogue specified above. The last circumstance opens possibilities of its application for solving various ill-posed problems of optimization, optimal control, inverse problems.

机力通风冷却塔风机自寻优群控方法研究

机力通风冷却塔以占地面积小的显著优点,在中小型基建工程中得到广泛应用。为提升中小型基建工程外冷系统的节能性和可靠性、改进传统多风机控制方法,创新性地提出了一种基于累计运行时间的机力通风冷却塔风机自寻优群控方法。将多风机整体化控制改进为顺序化联动控制,可有效降低风机启动门槛出力、消除整体化控制固有缺陷,满足非线性、时变性、大滞后系统控制的节能、可靠需求。经工程验证:该方法是一种契合纯变频电机配置冷却塔系统的多风机控制方法。该方法风机出力柔和、控制线性度好、系统易镇定、稳态偏差小;能够通过累计运行时间参数比对,实现风机自动交互运行,运行可靠性高。该方法的提出与实际应用可对配置机力通风冷却塔的中小型基建工程的安全节能运行提供借鉴。

基于SQP算法的厢舱类产品快速设计技术研究

针对大规模定制背景下产品设计制造过程中遇到的轻量化和强度校核等需求,提出了一种基于序列二次规划(SQP)算法的结构优化与快速设计方法。以方舱为实例对象,按照最优控制理论构建产品关键参数设计数学计算模型,并运用SQP算法实现参数的最优求解,同时通过产品族模块化划分和参数化变型配置技术实现产品结构的快速设计。最后通过二次开发构建了相应的数字化设计平台,验证了该方法的可行性。结果表明:该方法能有效提高产品结构设计的效率和合理性。

基于储能SOC优化控制的风储电站实时跟踪发电计划控制策略

随着风力发电的快速发展,提高风力发电的可调度性受到了越来越多的关注。针对目前跟踪发电计划控制策略存在的问题,研究基于储能SOC优化控制的风储电站实时跟踪发电计划控制策略,提出保证在误差允许范围内实时跟踪发电计划的前提下,以降低储能系统电量波动范围和放电深度为控制目标、采用实时滚动优化方法的控制策略,建立了储能SOC优化控制模型,并采用基于动态规划的优化算法进行求解。最后以北方某风光储输联合发电示范工程中的实测数据为例编程仿真,并与普通控制策略对比分析,验证了本文方法的可行性与有效性。

混合动力系统偏好强化学习能量管理策略研究

为实现混合动力系统在电池荷电状态(state of charge,SOC)平衡以及动力性约束下的经济性提升,提出了基于偏好强化学习的混合动力能量管理策略,该策略将能量管理问题建模为马尔科夫决策过程,采用深度神经网络建立输入状态值到最优动作控制输出的函数映射关系。与传统的强化学习控制算法相比,偏好强化学习算法无需设定回报函数,只需对多动作进行偏好判断即可实现网络训练收敛,克服了传统强化学习方法中回报函数加权归一化设计难题。通过仿真试验和硬件在环验证了所提出能量管理策略的有效性和可行性。结果表明,与传统强化学习能量管理策略相比,该策略能够在满足混合动力车辆SOC平衡和动力性约束下,提升经济性4.6%~10.6%。

模型预测控制技术在婴配乳粉配料优化中应用

为降低婴幼儿配方乳粉生产过程中的营养成分含量波动,本研究基于模型预测控制(MPC)算法和序列二次规划(SQP)算法构建婴幼儿配方乳粉配料优化控制模型,进行配料过程营养成分含量预测、反馈校正和滚动优化。结果表明,通过配料优化控制模型模拟干预100批次乳粉生产过程后,蛋白质、脂肪和碳水化合物含量的标准差分别从5.18、4.91、5.86 g·kg^(-1)降低至1.01、1.22和1.33 g·kg^(-1),验证了配料优化控制模型的有效性。本研究可为实现婴幼儿配方乳粉的配料优化控制提供参考。

考虑储能SOC的微网接口变流器VSG协同控制

储能系统是微电网的重要组成部分,而保证储能系统的荷电状态(SOC)良好则是储能系统乃至整个微网安全高效运行的技术关键。文中提出了一种基于虚拟同步机(VSG)控制的交直流混合微网接口变流器与储能SOC协同控制策略,用以提高混合微网的频率、功率稳定性和系统内各储能SOC的分配合理性。首先对交直流微网两侧分布式电源的下垂控制方式及子网特性进行了分析,之后基于此特性提出了应用于接口变流器的VSG控制策略提高了系统频率功率稳定性,并且在功率分配环节中加入储能系统SOC控制策略,使各子网间储能SOC状态达到平衡,优化储能系统状态。最后利用Matlab/Simulink搭建了交直流混合微网模型对文中提出的算法进行了有效性验证。

大功率模块化储能系统SOC优化均衡控制

针对大功率模块化储能系统中储能模块数量庞大而导致的储能电池SOC不均衡问题,提出了一种基于反步法的储能模块SOC优化控制方法,实现了储能电池工作时SOC的一致性。首先根据大功率模块化储能系统和储能模块拓扑结构,分别建立了储能模块的SOC状态方程和输出功率状态方程。然后应用反步法推导出了使各储能模块的SOC趋近一致的优化控制率,设计了储能模块控制器。在此基础上,针对系统中每个储能模块的充电状态、放电状态和切除状态,均进行储能模块的SOC控制,使同一桥臂上偏离期望值较大的储能模块SOC值快速接近期望值,实现了各储能模块充放电过程中的SOC一致性。最后建立了大功率模块化储能仿真系统,验证了SOC均衡控制的有效性。

考虑储能荷电状态的风储联合调频控制策略

储能配合风电参与调频可以改善一次调频效果,但储能荷电状态(state of charge,SOC)接近上下限值时会出现储能功率不足,此时储能SOC保护与调频效果很难兼顾。综合考虑风速和负荷随机变化的复杂工况下的调频需求,提出一种考虑储能SOC的风储联合调频控制策略,在SOC处于标准值附近时储能以最大下垂系数充放电,在储能SOC偏高或偏低时采用考虑SOC自适应的充放电控制策略保护SOC,同时风电机组通过变下垂控制弥补储能出力不足,以此改善调频效果。最后,通过MATLAB/Simulink仿真及风−储联合调频实验平台进行验证。实验结果表明,相比一次调频和SOC自适应调频方法,本文所提策略的频率偏移度及SOC偏移度均有明显降低,所提控制策略兼顾了调频效果及SOC保护。

基于序列最优控制算法的隔震曲线梁桥非线性振动控制研究

强震作用下隔震曲线梁桥会产生较大的弹塑性变形,需要采取一定的控制措施以保证其安全性。采用经典的Bouc-Wen模型,建立考虑上部结构偏心的隔震曲线梁桥的非线性动力方程,求解罕遇地震作用下结构的动力响应。针对隔震曲线梁桥罕遇地震作用下产生的较大弹塑性变形,建立隔震曲线梁桥的非线性振动控制方程,将该方程等效线性化后采用序列最优控制算法(SOC)和经典线性最优控制(COC)对桥梁进行振动控制研究。结果表明:在罕遇地震下,隔震曲线梁桥的下部结构与隔震层都已屈服进入塑性阶段,且有少量的残余位移产生;序列最优控制算法和经典线性最优控制都能有效地减小隔震曲线梁桥弹塑性状态下的平动位移与残余位移,并有效抑制曲线桥的扭转效应,其中序列最优控制算法较经典最优控制更能显著削减隔震曲线梁桥的峰值响应。

RBCC高超声速飞行器上升段轨迹快速优化

针对火箭基组合循环(RBCC)高超声速飞行器上升段轨迹设计所具有的动力系统工作模态复杂、推力与飞行状态存在强耦合、模型强非线性且存在多种复杂约束限制等典型特征,设计了一种基于序列凸优化的RBCC动力上升段轨迹快速优化方法。针对攻角控制系统是否存在二阶滞后情况,分别建立了适用于RBCC高超声速飞行器上升段轨迹优化的数学模型。基于凸优化理论对原优化模型进行凸化和离散化处理,进而设计了改进的轨迹优化求解策略。以末端机械能最大为优化指标,针对攻角控制系统存在/不存在二阶滞后的情况分别进行了上升段轨迹优化仿真。结果表明,所构建模型和轨迹优化方法可以快速、有效地完成RBCC高超声速飞行器上升段轨迹优化,优化结果符合RBCC动力系统工作特点,且可为RBCC动力运用和攻角控制系统设计提供参考。

基于凸优化的城轨列车车载复合储能控制系统优化方法

针对城轨列车复合储能系统的功率匹配不佳、能量利用率不高等问题,提出了一种凸优化能量控制策略.通过建立主动式复合储系统,进行系统模型的凸优化,以电池SOC和成本为目标函数,进行最优化分析,利用MATLAB进行仿真.仿真结果表明:该方法得到了全局最优能耗成本,电池的SOC达到最优,使整车的系统能量利用率提升9.6%,降低了系统的能耗,提高了整车经济性和稳定性.

考虑分布式光伏和储能参与的配电网电压分层控制方法

提出一种考虑分布式光伏无功和储能有功参与的配电网电压分层控制方法。首先,分析了由分布式光伏与负荷时空不匹配引起的高/低电压问题,依据配电网拓扑以及线路参数计算得到配电网节点电压与注入功率的无功-电压、有功-电压近似灵敏度矩阵。其次,在第一层控制中,提出了分布式光伏参与配电网调压的无功-电压下垂系数优化控制方法,通过求解电压优化模型得到分布式光伏逆变器的最优下垂系数,从而计算出其无功输出。当光伏逆变器的无功容量不足时,提出了第二层控制,即储能参与调压的有功-电压自适应下垂控制策略,储能逆变器的下垂控制系数根据储能荷电状态(state of charge,SOC)与所在节点电压自适应调整,既考虑了储能的容量,又实现了储能功率和SOC的相对均衡控制。最后,以一实际10节点配电网系统作为算例验证了所提方法的有效性。

基于序列二次规划算法的插电式混合动力汽车模型预测控制策略

为提升插电式混合动力汽车(PHEV)的车速预测精度和燃油经济性,提出基于序列二次规划(SQP)算法的模型预测控制能量管理策略。以卷积神经网络(CNN)构建的车速预测模型为基础,选取三类典型历史工况数据作为CNN车速预测模型的训练集,使用鲸鱼优化算法(WOA)优化CNN参数,通过优化的WOA-CNN模型预测未来时域内的车速;采用SQP算法对模型预测控制策略进行求解,且与基于规则的电量消耗和电量保持(CD-CS)策略和基于全局优化的动态规划(DP)策略的控制结果进行对比分析,验证所提策略的有效性。结果表明:通过WOA-CNN模型可提高车速预测精度,为4.88%~8.39%;与DP控制策略相比,本文提出策略的燃油消耗量高出1.98%,但计算时间减少了74.32%,能量管理的实时性得到大幅提升;与CDCS控制策略相比,提出策略的节油率为20.37%。综合考虑,本文提出策略的整车能量消耗和计算成本较优,可合理实现对PHEV转矩分配的智能控制。

净化液氮洗顺序控制联锁优化及应用

采用DCS控制系统PKS300对液氮洗分子筛进行了顺序控制。介绍了液氮洗主装置流程、吸附再生程序;根据系统的组态软件特点和相应功能顺序论述了分子筛顺序控制的过程和方法。本文以煤化工企业为研究,通过对在实际应用过程中,结合了工艺操作的实际经验,将DCS与安全仪表系统之间的串口通信优化为硬接点I/O输入输出方式,又进行顺控部序算法的优化,仪表异常状态下的故障退守模式,使得液氮洗顺控性能优化改造能以最佳状态适应整个生产系统。成功解决了影响装置非计划停车的问题,为其同行提供一些参考。

A sequential estimation problem with control and discretionary stopping

We show that“full-bang”control is optimal in a problem which combines features of(i)sequential least-squares estimation with Bayesian updating,for a random quantity observed in a bath of white noise;(ii)bounded control of the rate at which observations are received,with a superquadratic cost per unit time;and(iii)“fast”discretionary stopping.We develop also the optimal filtering and stopping rules in this context.