基于多目标优化的脱硝系统改进自抗扰控制北大核心CSCD

大范围变工况的燃煤机组脱硝系统因具有大惯性特性、强非线性以及多源扰动等特点,使其控制具有一定的挑战性。为提高脱硝系统的跟踪和抗干扰控制性能,并节约喷氨量,提出了基于控制性能指标和喷氨量指标的多目标优化串级改进自抗扰控制策略。在改进自抗扰控制原理的基础上,通过单一变量法分析改进自抗扰控制参数对控制效果的影响。采用多目标遗传算法优化脱硝系统改进自抗扰控制参数。通过仿真验证所提改进自抗扰控制在设定值跟踪、扰动抑制方面的优势,蒙特卡洛实验验证了所提改进自抗扰控制在应对系统不确定性时具有很强的鲁棒性。仿真结果表明所提改进自抗扰控制具有很好的实际应用潜力。     

基于自抗扰控制技术的高压大功率异步电机矢量控制系统

异步电机是一个非线性、多变量、强耦合系统。针对异步电机的特点,设计了一种新颖的高压大功率异步电机矢量控制系统。利用自抗扰控制技术,设计了速度控制器和转子磁链控制器。通过速度控制器对负载扰动进行估计和补偿,消除了负载扰动可能带来的稳态跟踪误差。采用级联型多电平逆变器给异步电机供电,逆变器的开关信号利用载波移相脉宽调制方法生成。仿真结果表明：系统不仅具有良好的动态和稳态性能,而且对负载及系统参数扰动具有较强的鲁棒性。     

基于NSGA-Ⅱ算法的多目标优化决策系统

设计一款多目标优化的决策系统,研究集中于调整PID控制器中的KP、KI控制器的增益,使得反馈控制系统满足设计的性能需求。在设计变量参数与给定系统的性能指标之间,设计一种可自动优化增益的参数的算法以满足性能标准的目标。项目设计了PID控制器的增益可以通过NSGA-Ⅱ算法进行调整,从而使得反馈系统的性能指标达到满意的标准。     

基于分布式策略的直流微电网下垂控制器设计

本文研究了分布式控制策略下直流微电网的负荷分配和电压平衡问题. 给出一种新的基于分布式策略的下垂控制器设计方法, 能够在统一的框架下实现直流微电网负载共享和电压平衡. 首先,将直流微电网的负载共享和电压平衡问题转化为多目标优化问题, 其性能指标与微源的容量密切相关. 然后, 通过求解多目标优化问题获得实现负载共享和电压平衡的集中式控制策略, 并给出下垂控制器的设计方法. 为了降低系统的通信负担, 给出一种新的只需与邻居节点交换信息的分布式控制策略, 通过理论分析可知该分布式控制策略能够收敛到多目标优化问题的最优解. 最后, 通过对新能源汽车充换电站系统的仿真验证了本文提出的方法的有效性.     

基于线性自抗扰的开关磁阻电机调速控制研究

开关磁阻电机调速系统是复杂的非线性时变系统,负载扰动大,变量之间耦合严重,针对上述系统的性能特点提出采用线性自抗扰控制策略对系统进行控制的方法。首先为克服负载扰动变化,电机磁链呈非线性以及电流、位置等参数耦合的内外部干扰问题,设计扩张状态观测器对系统内扰和外扰进行准确估计并实时补偿。然后设计PD（比例-微分）控制器抑制系统给定与扩张状态观测器反馈的观测对象状态变量之间的跟踪误差。最后在仿真平台上对设计的控制系统进行试验并与传统PID控制方案进行对比,结果显示,对于给定的阶跃信号线性自抗扰控制器只需0.09s即可达到稳态且无超调,而PID控制器需要3s才能实现稳定跟踪。因此相比于传统PID控制,线性自抗扰控制器拥有更优的动静态性能,并且系统在外部负载扰动和内部模型参数变化的情况下也有良好的控制效果,表现出了很好的鲁棒特性。     

基于有界扰动分区的多模型自适应控制

对于含有界扰动的离散时间系统,将有界扰动变化范围分割成若干小区间,针对这些区间建立多个自适应模型,并针对每一个自适应模型建立相应的控制器.给定一个指标切换函数,基于多个自适应模型控制器和给定的指标切换函数构成多模型自适应控制器.可以证明多模型自适应控制器能够保证闭环系统是输入输出有界稳定的.仿真实例表明,当被控对象的有界扰动随时间变化的时候,采用单一模型自适应控制,系统输出很难跟踪设定值,而多模型自适应控制器却极大地改善了控制品质.     

基于非线性自抗扰控制器的PMSM直接转矩控制

针对传统的永磁同步电机(PMSM)直接转矩控制中转矩脉动和磁链脉动较大及转速超调等问题,研究一种基于非线性自抗扰控制的PMSM直接转矩控制策略.将传统的PI控制器替换成非线性的自抗扰控制器,设计转速环自抗扰控制器.自抗扰控制器中的扩张状态观测器将外部扰动和未知系统的参数的变化进行估计,并通过补偿手段加以控制,提高系统的抗干扰性能.微分跟踪器将给定转速平滑化,使得系统快速跟踪给定的转速信号,提高系统的响应能力.仿真实验验证了该策略的可靠性和有效性.     

应用Clegg积分器实现多目标优化控制

给出多目标优化控制系统的定义，它的稳定判据，讨论怎样应用Ｃｌｅｇｇ积分器实现多目标优化控制，解决了控制论中长期没有解决的多目标优化控制问题。文中所讨论的设计方法适用于其它类型的非线性积分器组成的多目标优化控制。     

基于多目标粒子群的非线性系统PID控制器设计

PID控制器自产生以来,一直是工业生产过程中应用最广泛、最成熟的控制器。随着控制品质的要求越来越高,综合考虑系统的准确性、稳定性、快速性等多个性能指标,基于改进的Pareto最优排序多目标粒子群算法,给出一个适用于一类非线性系统的PID控制器设计方法。采用经典的非线性倒立摆系统作为PID被控对象进行仿真,将超调量和调节时间两个目标作为多目标粒子群算法的目标,求出一组Pareto最优控制参数,通过跟踪控制得到精确稳定的控制效果。     

基于神经网络的无人机负载模拟器的复合控制

衡量负载模拟器系统性能的关键指标是多余力矩的抑制。针对无人机负载模拟器系统的非线性及多余力矩强扰动的特点,依据神经网络的非线性逼近和自学习特性,提出了一种基于神经网络和前馈相结合的复合控制器,用来提高系统的性能。复合控制器利用前馈来补偿定常多余力,利用神经网络进行在线辨识、控制来补偿系统的非线性部分,很好地抑制了多余力矩。该文给出了具体的控制结构和算法。仿真结果还表明该方法极大地改善了系统动态加载性能,有很强的鲁棒性。     

Numerical treatment of multiobjective optimal control problems

We present a numerical procedure for solving optimal control problems with both linear terminal constraints and multiple criteria. Using a Chebyshev spectral procedure, the problem reduces to a constrained optimization problem which can be solved using hybrid penalty partial quadratic interpolation (HPPQI) technique. The proposed procedure compares quite favorably with other methods on a sample of well-known examples.     

Multiobjective controller design handling human preferences

Trends in controller design point to the integration of several objectives to achieve new performances. Moreover, it is easy to set the controller design problem as an optimization problem. Therefore, future improvements are likely to be based on the adequate formulation and resolution of the multiobjective optimization problem. The multiobjective optimization strategy called physical programming provides controller designers with a flexible tool to express design preferences with a ‘physical’ meaning. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories such as desirable, tolerable, unacceptable, etc. to which numerical values are assigned. The problem is normalized and converted to a single-objective optimization problem but normally it results in a multimodal problem very difficult to solve. Genetic algorithms provide an adequate solution to this type of problems and open new possibilities in controller design and tuning.     

Design of an adaptive mutation operator in an electrical load management case study

An adequately designed and parameterized set of operators is crucial for an efficient behaviour of Genetic Algorithms (GAs). Several strategies have been adopted in order to better adapt parameters to the problem under resolution and to increase the algorithm's performance. One of these approaches consists in using operators presenting a dynamic behaviour, that is displaying a different qualitative behaviour in different stages of the evolutionary process. In this work a comparative analysis of the effects of using an adaptive mutation operator is presented in the operational framework of a multi-objective GA for the design and selection of electrical load management strategies. It is shown that the use of a time/space varying mutation operator depending on the values achieved for each objective function increases the performance of the algorithm.     

Multiobjective model predictive control

This paper proposes a novel model predictive control (MPC) scheme based on multiobjective optimization. At each sampling time, the MPC control action is chosen among the set of Pareto optimal solutions based on a time-varying, state-dependent decision criterion. Compared to standard single-objective MPC formulations, such a criterion allows one to take into account several, often irreconcilable, control specifications, such as high bandwidth (closed-loop promptness) when the state vector is far away from the equilibrium and low bandwidth (good noise rejection properties) near the equilibrium. After recasting the optimization problem associated with the multiobjective MPC controller as a multiparametric multiobjective linear or quadratic program, we show that it is possible to compute each Pareto optimal solution as an explicit piecewise affine function of the state vector and of the vector of weights to be assigned to the different objectives in order to get that particular Pareto optimal solution. Furthermore, we provide conditions for selecting Pareto optimal solutions so that the MPC control loop is asymptotically stable, and show the effectiveness of the approach in simulation examples.     

Decentralized model predictive control of a multi-evaporator air conditioning system

Temperature control of multiple zones with a multi-evaporator vapor compression system is a common problem in modern air conditioning. Due to the coupled system dynamics, standard decoupled controllers can interfere with each unit′s performance. This paper proposes an architecture that is decentralized and modular, avoiding competing controllers and the practical difficulty of implementing a centralized controller. A model predictive control (MPC) supervisor calculates evaporator cooling and pressure setpoints for each zone, balancing temperature regulation with energy efficiency; these setpoints are tracked by local level controllers, which rely upon MPC's ability to respect constraints in order to maintain safe, efficient operation.     

Asymptotic convergence of metaheuristics for multiobjective optimization problems

This paper analyzes the convergence of metaheuristics used for multiobjective optimization problems in which the transition probabilities use a uniform mutation rule. We prove that these algorithms converge only if elitism is used.     

Adaptive differential dynamic programming for multiobjective optimal control

An efficient numerical solution scheme entitled adaptive differential dynamic programming is developed in this paper for multiobjective optimal control problems with a general separable structure. For a multiobjective control problem with a general separable structure, the “optimal” weighting coefficients for various performance indices are time-varying as the system evolves along any noninferior trajectory. Recognizing this prominent feature in multiobjective control, the proposed adaptive differential dynamic programming methodology combines a search process to identify an optimal time-varying weighting sequence with the solution concept in the conventional differential dynamic programming. Convergence of the proposed adaptive differential dynamic programming methodology is addressed.     

Multiobjective problems: Linear and nonlinear control

In this note, we compare the value of nonlinear and linear control for a multiobjective control problem with a linear time-invariant (LTI) plant. We show that if the performance of a nonlinear feedback controller is measured by the maximum incremental gain, the optimal achievable performance with nonlinear, time-varying control is identical to that achievable by LTI control.     

Robust output-feedback controller design via local BMI optimization

The problem of designing a globally optimal full-order output-feedback controller for polytopic uncertain systems is known to be a non-convex NP-hard optimization problem, that can be represented as a bilinear matrix inequality optimization problem for most design objectives. In this paper a new approach is proposed to the design of locally optimal controllers. It is iterative by nature, and starting from any initial feasible controller it performs local optimization over a suitably defined non-convex function at each iteration. The approach features the properties of computational efficiency, guaranteed convergence to a local optimum, and applicability to a very wide range of problems. Furthermore, a fast (but conservative) LMI-based procedure for computing an initially feasible controller is also presented. The complete approach is demonstrated on a model of one joint of a real-life space robotic manipulator.