基于干扰观测器的复杂非线性系统优化控制方法

为了提高具有未知干扰的复杂非线性系统的控制性能和运行性能,提出了一种基于干扰观测器的复杂非线性系统优化控制方法(IOCM).该方法设计了一个基于模糊神经网络的模型逼近器来捕捉系统的非线性动力学,并利用干扰观测器来描述系统的未知干扰,以获得更准确的系统预测模型.然后,在多目标模型预测控制的框架下,提出了一种具有协同成本函数和多梯度算法的优化控制结构,以综合求解设定点和控制律.采用城市污水处理过程基准仿真平台(BSM1)验证所提方法的有效性.实验结果表明:暴雨天气条件下平均出水水质(EQ)为6711 mg/L、平均运行能耗(EC)为3805 kW·h;对比其他分步实现的优化控制方法,IOCM具有较好的鲁棒性,能够提高非线性系统的优化控制性能.

考虑信息结构约束的协调型非线性优化励磁控制

提出了一种在互联电力系统中考虑信息结构约束条件下协调设计非线性优化励磁控制的方法。基于控制信息结构概念和线性二次型性能指标,将控制设计问题转化为求解扩展Levine-Athans方程组,采用直接迭代算法,可快速、精确地求出增益矩阵,进而得到机组端电压、角频率和功率偏差反馈的非线性优化励磁控制。将该方法应用于一个两区域四机电力系统,数字仿真结果表明:得到的控制策略较传统的AVR/PSS增强了系统的人工阻尼、暂态稳定性和区间传输能力。

船舶航向非线性H_∞逆优化输出反馈控制

为抑制船舶航向非线性优化控制中模型参数摄动和由状态观测器引入的不确定观测误差,提出了一种非线性H∞逆优化控制算法.首先,基于无源理论设计观测器以实现海浪滤波,该观测器无需海浪扰动的方差信息从而减少了观测器参数数量.然后,考虑模型参数摄动对观测误差的影响,给出了描述系统局部(全局)性态的局部(全局)H∞优化性能指标.在以广义黎卡提方程(GARE)对局部优化问题的求解的基础上,应用逆优化方法将全局H∞优化问题转化为构造闭环系统的Lyapunov函数问题,得到同时满足两种指标的优化控制器,并证明了稳定性.仿真结果证明了该算法的有效性.

高空风电系统的优化控制研究进展与挑战

为了实现“碳达峰,碳中和”的战略目标,我国将持续推动可再生能源的高比例发展,构建以新能源为主的新型电力系统.作为可再生的清洁源,风能的开发和利用已成为研究的重要方向.研究表明远离地面的高空中风速更加强劲并且更均匀,风向也更稳定,因此,风力发电的进一步突破可以通过用风筝捕获高空风能来实现.为了确保高空风能系统安全、经济、高效地运行,对其控制系统设计的要求极高.本文阐述了国际上几种主流高空风电技术的发电原理、发展进程以及现状.通过对典型的Yo-Yo式结构进行动力学建模,分析了各类非线性控制技术的原理和特点,具体描述了非线性模型预测控制原理和轨迹跟踪控制的仿真结果.总结了未来高空风能控制技术面临的控制算法计算量大,控制系统可靠性研究缺乏以及智能化水平不高等关键问题.

电磁力作用下绕流减阻与优化控制

电介质溶液中,电磁场产生的Lorentz力可以控制流体的运动。将其用于钝体绕流时,可以减少阻力、抑制分离和消除涡街。为了使这种控制更加经济和有效,电磁场的强度即控制强度,应当是优化的,随流场实时调正的。基于优化控制理论,以圆柱绕流的电磁优化控制为例,推导了流场电磁控制的性能指标表达式和协态方程,并对雷诺数Re为200流场的非线性优化控制问题进行了数值研究,流场的基本方程为指数极坐标中考虑场力的Navier-Stokes方程,计算采用交替方向隐式格式和快速傅里叶变换格式。得到了优化电磁场强度的变化规律,讨论了该优化控制下,流场和圆柱表面阻力和升力的变化过程。研究结果表明,通过优化控制,可以达到减少阻力、抑制分离和消除涡街及涡生振荡的目的。

APC过程优化控制系统在水泥窑系统中的应用

在现有的各种高级过程优化控制方案中,以多变量的非线性优化控制效果为最佳。APC过程优化控制系统在青海盐湖海纳化工有限公司烧成系统中的应用实践证明:该系统能稳定整个烧成系统的操作,特别是降低分解炉温度、窑头负压以及篦冷机篦下压力的波动,提高设备运转率;在CO许可范围内,减少C_1筒出口氧含量,减少用风,降低单位熟料燃煤的消耗和电耗,提高烧成系统产能;稳定煅烧温度,降低熟料fCaO的波动,减少过烧和欠烧现象,提高产品合格率;实现标准化操作,降低操作人员的劳动强度,还可减少操作人员,提高劳动生产率。

基于SMPA的半挂车自动泊车运动规划方法研究

半挂车辆的非稳定运动学特性为其泊车过程中自主运动规划带来严峻挑战。针对半挂车在多障碍物的静态场景中泊车运动规划算法效率低、结果平滑性差等问题,本文提出了序列式运动规划方法(sequential motion planning algorithm,SMPA)。首先,提出了基于二次规划策略和改进双向快速扩展随机树(bidirectional rapidly-exploring random tree algorithm,Bi-RRT)的初始路径生成方法。然后,结合车辆非完整微分约束下的路径节点可行性判别方法研究,提出基于概率的目标偏向采样策略,提高了采样效率。最后,构建了面向车辆系统控制变量连续性的非线性最优化控制模型,解决泊车换向点的对接问题,提高了泊车轨迹平滑性。仿真结果表明,该方法在多障碍物场景中,规划时间相比Hybrid A*和Bi-RRT分别降低了86.71%和21.44%,轨迹质量也更具优越性。

Multi-domain modeling and simulation of proportional solenoid valve

A multi-domain nonlinear dynamic model of a proportional solenoid valve was presented.The electro-magnetic,mechanical and fluid subsystems of the valve were investigated,including their interactions.Governing equations of the valve were derived in the form of nonlinear state equations.By comparing the simulated and measured data,the simulation model is validated with a deviation less than 15%,which can be used for the structural design and control algorithm optimization of proportional solenoid valves.

An Improved Control Vector Iteration Approach for Nonlinear Dynamic Optimization. II. Problems with Path Constraints

This paper considers dealing with path constraints in the framework of the improved control vector iteration （CVI） approach. Two available ways for enforcing equality path constraints are presented, which can be directly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the ll penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reaCtor operation problem are in agreement with the literature reoorts, and the comoutational efficiency is also high.

Neuro-fuzzy predictive control for nonlinear application

Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to reduce the model errors caused by changes of the process under control. To cope with the difficult problem of nonlinear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.

Optimal design of coordination control strategy for distributed generation system

This paper presents a novel design procedure for optimizing the power distribution strategy in distributed generation system. A coordinating controller, responsible to distribute the total load power request among multiple DG units, is suggested based on the conception of hierarchical control structure in the dynamic system. The optimal control problem was formulated as a nonlinear optimization problem subject to set of constraints. The resulting problem was solved using the Kuhn-Tucker method. Computer simulation results demonstrate that the proposed method can provide better efficiency in terms of reducing total costs compared to existing methods. In addition, the proposed optimal load distribution strategy can be easily implemented in real-time thanks to the simplicity of closed-form solutions.

Bellman Equation for Optimal Processes with Nonlinear Multi-Parametric Binary Dynamic System

A process represented by nonlinear multi-parametric binary dynamic system is investigated in this work. This process is characterized by the pseudo Boolean objective functional. Since the transfer functions on the process are Boolean functions, the optimal control problem related to the process can be solved by relating between the transfer functions and the objective functional. An analogue of Bellman function for the optimal control problem mentioned is defined and consequently suitable Bellman equation is constructed.

A Parameter Determination Method of Distribution Voltage Regulators Considering Tap Change and Voltage Profile

This paper proposes a parameter determination method of distribution voltage regulators load ratio control transformers （LRT） and step voltage regulators （SVR） considering the tap change and voltage profile. The method takes two procedures in order to simplify the optimization problem and to reduce calculation time. One is to simultaneously determine the control parameters of LRT and SVR minimizing voltage violations and voltage variations. The algorithm is based on particle swarm optimization （PSO）, which is one of non-linear optimization methods by using a concept of swarm intelligence. Another is to determine the dead-band width of LRT and SVR on the basis of bi-evaluation of tap change and voltage margin. The concept of a Pareto optimal solution is used for the decision of the best dead-band width. As the results of numerical simulations using distribution network model, the validity of the proposed method has been affirmed.

OPTIMAL CONTROL OF GROWTH RATE FOR A CLASS OF POPULATION SYSTEMS

In this paper,we discuss the optimal control of growth rate for a class of population systems governed by nonlinear degenerate parabolic equations.Given an ap-propriate cost functional,we prove the existence of the optimal growth rate and give some necessary conditions for optimal growth rate.