基于遗传算法优化可调整控制规则的模糊控制

针对一类参数时变、时滞后系统,设计了一种解析描述可调整控制规则的模糊控制器.再利用遗传算法对表达式中的可调整因子进行全局快速优化,从而实现控制规则的学习与修正.仿真结果表明,优化后控制器具有良好鲁棒性与抗扰动能力,对时变时滞后系统具有较好的适应能力.     

采用遗传优化自适应模糊PID控制球杆系统

为解决球杆系统动态、静态性能不高的问题,提出了遗传算法优化自适应模糊PID控制器的控制方法.该模型在拉格朗日方程建立球杆系统数学模型的基础上,采用遗传算法优化模糊控制规则、隶属函数和自适应PID参数.在GBB1004系统中建立了遗传算法优化后的自适应模糊PID控制器以及控制模型,并对该控制器进行实验验证.实验结果证明了遗传算法优化后的模糊控制器有效地减小了系统的超调量,缩短了系统的调节时间,能够较好地控制球杆系统.     

基于免疫遗传算法优化的模糊控制方法及其应用

在模糊控制器的设计过程中,为了使模糊控制器的性能达到全局优化,应用免疫遗传算法对模糊控制器参数进行优化设计;在综合考虑各种参数对控制器性能影响的基础上,给出了一种全面优化隶属度函数、比例因子和量化因子的优化方法;利用了免疫算法能保持个体的多样性和能对学习过程进行引导的特点,对模糊控制器的多个参数同时进行优化,从而显著提高了系统的收敛性、稳定性。应用该方法对数控铣削加工过程的模糊控制器的设计进行了仿真,并与其他方法进行比较和控制实例的验证,表明了该基于免疫遗传算法优化的模糊器能获得更优良的控制性能。     

基于遗传算法的模糊支持向量网络控制

将模糊控制与支持向量网络相结合，设计了一种模糊支持向量网络控制器．该控制器融合了模糊控制与支持向量网络的优点，具有不依赖被控对象模型、泛化能力强等特点．利用遗传算法来优化支持向量机参数和控制器比例因子参数，以期实现最优的控制性能. 仿真结果表明了控制系统具有优良的控制性能．     

一种复合控制器在电液系统中的应用

针对电液伺服系统模型不精确、参数时变和负载干扰大的特点,提出一种Bang-Bang+Fuzzy-PI的自适应复合控制器.为避免在不同控制方式间切换造成系统抖动,选择高斯函数作为切换曲线,并采用遗传算法对Fuzzy控制器的尺度因子和PI控制器的积分常数进行在线优化.比较复合控制器与Fuzzy控制器,Fuzzy-PI控制器和PID控制器的主要动、静态指标,仿真结果表明这种复合控制器调节时间短、稳态精度高、鲁棒性强,具有优良的控制性能.     

单容时滞系统的ITAE优化控制

本文给出单容时滞系统ITAE优化控制算法.为此先将时滞因子e-Ls表示成μ阶近似模型,后将单容对象和时滞因子的μ阶近似模型看成一个整体,为它们构造一个整体观察器,最后用遗传算法,在Matlab平台上,寻求满足ITAE最优控制律的状态控制器的优化参数.     

遗传算法在智能车控制器参数寻优中的应用

研究智能车控制器优化设计问题,由于控制器参数不准确,引起控制误差.为解决上述问题,根据智能车的运动学模型,设计出智能车轨迹跟踪控制律.采用遗传算法对控制律参数寻优,利用逆推(Backstepping)方法,在搜索空间里获得全局最优解.最后利用提出的遗传算法对控制律参数优化方法,通过智能车对圆周轨迹的跟踪仿真,证明了智能车在遗传算法对该控制律参数寻优下,能够实现对给定轨迹的全局渐近跟踪,获得了较好的控制效果.验证了遗传算法对智能车控制器参数寻优的可行性和有效性.     

基于遗传算法的HVDC系统控制参数优化

高压直流输电技术以其大容量远距离输电、交流系统之间的异步互联、传输功率快速可控等优点在我国得到了广泛应用,高压直流系统的控制参数直接影响到直流输电系统的运行特性,因此对高压直流系统控制参数的优化显得尤为重要。本文以传统高压直流系统为研究对象,以MATLAB/SIMULINK为仿真平台,基于采用简单记忆法策略,即在每代寻优结束后,将最优个体保留在适应度最低的个体的位置方法对传统遗传算法进行改进,并采用改进后的遗传算法对高压直流控制参数进行优化。应用MATLAB将遗传算法和高压直流系统模型相结合进行仿真,通过多次迭代得到优化的控制器参数。同时,将优化后的控制器参数与原始参数的鲁棒性进行比较,证明了控制器参数优化的必要性以及遗传算法优化控制器参数的可行性和优越性。     

一种混合遗传算法在异步电机直接转矩调速系统中的应用

针对煤矿电机车异步电动机直接转矩控制系统中数学模型参数的不确定问题提出了一种基于混合遗传算法的模糊控制器,讨论了遗传算法中禁忌搜索(TabuSearch)的变异算子,并对它进行了一些改进.理论分析和仿真结果表明,采用遗传算法可以对模糊控制器的加权因子进行优化,有助于改善系统的动态性能.     

自抗扰控制器参数的免疫遗传优化及应用

针对自抗扰控制器参数较多不易整定的问题,提出了基于免疫遗传算法的参数优化设计方法。与标准遗传算法相比,免疫遗传算法引入了免疫记忆库和浓度控制机制,提高了算法的收敛效率和局部收敛性能。并且综合考虑系统动态性能和实际工程中控制代价的限制因素建立了控制系统性能评价的目标函数,按照分离性原则进行自抗扰控制器设计并用免疫遗传算法对其关键参数进行寻优。将该方法应用于过热汽温度控制系统的变工况运行,仿真实验结果表明经过免疫遗传算法优化后的自抗扰控制器适应性较强,适用于模型参数变化范围较大的受控对象。     

模糊自适应PID参数自整定控制器的研究

当控制系统中的被控对象存在纯滞后、时变或非线性等复杂因素时,普通的PID控制器的控制效果很难达到较好的控制效果,针对这一问题,应用模糊控制和自适应控制的知识,设计了模糊自适应PID参数自整定控制器,此控制器的比例系数、积分系数和微分系数可根据模糊推理规则进行在线调整。仿真结果表明,该控制方法提高了系统的动、静态特性,使该系统具有较好的鲁棒性。     

一类控制系数有界的时变非线性系统的自适应鲁棒控制

针对一类带有有界控制系数和有界扰动的时变参数严反馈非线性系统, 将Nussbaum函数增益及光滑投影算法与自适应逆推设计工具相结合, 提出一种自适应鲁棒非线性控制方案. 在此方案中无需知道控制系数的符号, 以及时变参数和扰动的界. 借助Lyapunov函数及相关引理证明了所设计的自适应鲁棒非线性控制器能保证闭环系统中的所有信号全局一致有界. 可以通过恰当地选取设计参数, 保证系统具有任意指定的控制性能. 仿真研究证明了所提出算法的可行性和有效性.     

Research on adaptive sliding synchronization of Rikitake chaotic system with single unknown control coefficient

The control of second order system with uncertain parameters and single unknown control coefficient was investigated to solve the synchronization problem of Rikitake chaotic with reduced number of active inputs. In addition, a kind of adaptive strategy was hybrid with sliding mode method, where the adaptive strategy was used to cope with uncertain parameters produced in the process of sliding mode controller design. At last, detailed numerical simulations with both second order systems and synchronous chaotic system were done to testify the rightness of the proposed method and also multi-time random simulations were done to testify the robustness of the controller. In addition, the main conclusion is that the sliding mode control has very good consistency since the strategy formation is almost the same as the controller for system with known control coefficient, and high gain is necessary for system with single uncertain control coefficient.     

Synthesis of adaptive sub-optimal feedback control laws†

In this paper a method for synthesizing a sub-optimal feedback controller which is sensitive to variations in plant parameters is presented. The structure of the sub-optimal control is a linear combination of suitably chosen basis functions multiplied by coefficients which are functions of the varying plant parameters. The coefficient multipliers are determined by the minimization of a mean-square error using data obtained from numerically computed optimal trajectories. The controller may be called adaptive since it uses identification of the varying plant parameters to modify the coefficient multipliers. Examples are included which show that with relative sensitivity as the criterion the method is superior to other methods of suboptimal control design.     

The intelligent integrated speed controller of DTC for induction motor

The parameters selection of proportional coefficient and integral coefficient (PI) for speed controller is important for direct torque control system. However, it is difficult to adjust these parameters. In this paper, firstly, we use particle swarm optimization to search the appropriate PI values of the speed controller. Secondly, based on the optimized PI parameters, the fuzzy-PI speed control strategy is presented to solve the poor self-adaptability problem. Thus, the proportional coefficient k _p and integral coefficient k _i can be adjusted dynamically to adapt to the speed variations. And finally, to obtain the high-speed parallel processing ability, the well-trained RBF neural network replaces the fuzzy-PI speed controller. The comparison with conventional PI speed controller shows that the proposed intelligent integrated speed controller brings good benefits of fast speed response and good stability and reduces the torque ripple. The validity of the proposed intelligent integrated speed controller is verified by the simulation results.     

基于系数图法的多输入多输出系统控制器设计方法

多输入多输出系统的交互作用会限制适用于单输入单输出系统的多种先进设计方法的使用,且多数关于多输入多输出系统控制研究只重视消除交互作用的影响,忽略系统控制问题,这样不但弱化系统的鲁棒性,且设计过程复杂,解耦后系统阶次也较高.鉴于此,提出一种基于系数图法的多入多出系统的控制器设计方法,将多输入多输出系统解耦问题转化为参数优化问题.首先,给出目标函数和两个线性约束条件,通过在频域基于粒子群算法优化目标函数,从而设计补偿器实现解耦;其次,基于系数图法确定控制器结构及参数整定,兼顾系统的稳定性和鲁棒性;最后,通过仿真实验验证所提出方法的有效性.     

Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

This paper deals with the design of a novel fuzzy proportional–integral–derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching–learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.     

发电机的非线性自适应逆推综合控制

发电机励磁和汽门系统是一个典型的多变量、非线性、强耦合、不确定复杂系统,其综合控制将会改善电力系统稳定性和动态品质,所以设计简单、有效的综合控制器既必要又困难.针对单机无穷大励磁与汽门系统,运用自适应逆推方法和系统的Lyapunov函数,获得了发电机的非线性综合控制器和参数替换律,文中给出了该控制器的具体设计步骤.由于在控制器设计中没有运用任何线性化方法,因而所得控制器充分利用了系统的非线性特性;同时考虑了发电机阻尼系数的不确定性,使得控制器对系统参数的变化具有很强的鲁棒性.数字仿真结果表明,所设计的控制器具有鲁棒性,并可有效地提高电力系统的稳定性.     

半导体温控系统的自抗扰控制器设计

针对半导体温控系统大时间常数、时滞较大且易受参数扰动影响的问题,提出了自抗扰控制(ADRC)技术。针对自抗扰控制器待调节参数多、参数整定较为困难的问题,从fal函数特性及扩展状态观测器(ESO)设计出发,确定了自抗扰控制器的参数,并探讨了如何准确估计建模误差与外扰。仿真试验表明,系统在半导体致冷器件优值系数、冷热端温差发生变化及受到电压纹波干扰时均具有良好的鲁棒性能和动态性能。     

Synthesis of a linear controller for a control system with interval parameters on a base of D-partition in vertices of a coefficient's polyhedron of characteristic polynomial

The article deals with the technique developed for the parametric design of a robust controller which ensures the desired robust quality indicators in the system with interval parameters. This technique is based on the construction of the D-partition in the space of controller parameters at vertices of the coefficient polyhedron of a characteristic polynomial. Grounds for this are the fact that the interval system has its minimum degree of stability and maximum degree of oscillation at certain vertices of the specified polyhedron. To determine the minimum sets of those vertices, a new technique has been developed. The developed technique is based on inequalities obtained for outlet angles of the edge branches coming out from system poles, which determine the root indices of robust quality. Applying the developed technique for the vertex D-partition, the design of the PI controller of the stabilization system for a tethered underwater vehicle has been carried out. The results of the design confirm the efficiency of the developed approach.