Robust and novel two degree of freedom fractional controller based on two-loop topology for inverted pendulum

A rotary single inverted pendulum (RSIP) typically represents a space booster rocket, Segway and similar systems with unstable equilibrium. This paper proposes a novel two degree of freedom (2-DOF) fractional control strategy based on 2-loop topology for RSIP system which can be extended to control the systems with unstable equilibrium. It comprises feedback and feed-forward paths. Primary controller relates the perturbation attenuation while the secondary controller is accountable for set point tracking. To tune the parameters of proposed fractional controller a simple graphical tuning method based on frequency response is used. The study will serve the outstanding experimental results for both, stabilization and trajectory tracking tasks. The study will also serve to present a comparison of the performance of the proposed controller with the 1-DOF FOPID controller and sliding mode controller (SMC) for the RSIP system. Further to confirm the usability of the proposed controller and to avoid the random perturbations sensitivity, robustness, and stability analysis through fractional root-locus and Bode-plot is investigated.     

Performance analysis of two-degree of freedom fractional order PID controllers for robotic manipulator with payload

The robotic manipulators are multi-input multi-output (MIMO), coupled and highly nonlinear systems. The presence of external disturbances and time-varying parameters adversely affects the performance of these systems. Therefore, the controller designed for these systems should effectively deal with such complexities, and it is an intriguing task for control engineers. This paper presents two-degree of freedom fractional order proportional-integral-derivative (2-DOF FOPID) controller scheme for a two-link planar rigid robotic manipulator with payload for trajectory tracking task. The tuning of all controller parameters is done using cuckoo search algorithm (CSA). The performance of proposed 2-DOF FOPID controllers is compared with those of their integer order designs, i.e., 2-DOF PID controllers, and with the traditional PID controllers. In order to show effectiveness of proposed scheme, the robustness testing is carried out for model uncertainties, payload variations with time, external disturbance and random noise. Numerical simulation results indicate that the 2-DOF FOPID controllers are superior to their integer order counterparts and the traditional PID controllers.     

基于改进的免疫克隆算法的PID参数优化

依据PID控制器的控制原理,结合人工免疫算法和克隆选择原则,提出了一种新的基于周期变异的免疫克隆算法,并基于此算法在线优化PID控制参数,仿真结果表明控制输出能快速平稳地跟随期望输出值,系统响应可以获得良好的实时性、稳定性和较高的控制精度。     

模糊自适应PID控制器在液压伺服系统的应用

将模糊控制器和PID控制器结合在一起,利用模糊控制实现了PID控制器参数在线自调整,进一步完善了PID控制器的性能,提高了液压伺服系统的控制精度.实验与仿真结果表明:模糊自适应PID控制器,具有良好的动态、稳态性能以及较强的鲁棒性.     

改进型BP-PID的舵机控制系统研究

针对机械式负载模拟器体积大、成本高及电液式负载模拟器污染大、维修困难等问题,设计了一款电动式直线电机加载测试台。通过Simulink仿真对比了二自由度PID和改进型BP-PID两种控制系统的控制效果。结果表明,改进型BP-PID控制系统不依赖于参数整定,而是通过神经网络多次训练样本自整定出所需参数,稳定性更高、跟踪性更好,完全满足加载测试系统指标要求。     

一种智能控制算法在智能仪器中的实现

数字PID控制以其算法简单、实用性强而广泛应用于智能仪器控制中，但由于控制系统的非线性和复杂性使得PID的控制精度和鲁棒性较差。这里介绍了一种适用于智能仪器控制中的智能控制算法，它结合了人工智能中的专家系统技术、模糊控制技术及常规的PID控制方法，具有简单、实用性强、控制精度高和鲁棒性好的特点。     

Two-degree-of-freedom fractional order-PID controllers design for fractional order processes with dead-time

Recently, fractional order (FO) processes with dead-time have attracted more and more attention of many researchers in control field, but FO-PID controllers design techniques available for the FO processes with dead-time suffer from lack of direct systematic approaches. In this paper, a simple design and parameters tuning approach of two-degree-of-freedom (2-DOF) FO-PID controller based on internal model control (IMC) is proposed for FO processes with dead-time, conventional one-degree-of-freedom control exhibited the shortcoming of coupling of robustness and dynamic response performance. 2-DOF control can overcome the above weakness which means it realizes decoupling of robustness and dynamic performance from each other. The adjustable parameter η₂ of FO-PID controller is directly related to the robustness of closed-loop system, and the analytical expression is given between the maximum sensitivity specification M_s and parameters η₂. In addition, according to the dynamic performance requirement of the practical system, the parameters η₁ can also be selected easily. By approximating the dead-time term of the process model with the first-order Padé or Taylor series, the expressions for 2-DOF FO-PID controller parameters are derived for three classes of FO processes with dead-time. Moreover, compared with other methods, the proposed method is simple and easy to implement. Finally, the simulation results are given to illustrate the effectiveness of this method.     

基于限幅的最优PID控制器

本文提出了一种允许PID控制器输出达到限幅的非线性控制策略，探讨了寻求PID最优参数的搜索算法，并对搜索算法进行了改进。仿真表明，本文所提出的基于限幅的PID控制策略与传统的线性PID控制策略相比，能使系统的调节时间、超调量、鲁棒性等性能指标有着显著的提高。     

A novel auto-tuning PID control mechanism for nonlinear systems

In this paper, a novel Runge–Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence.     

基于神经内分泌的并联机器人智能控制系统

为研究一种高速度、高精度的二自由度冗余驱动并联机器人自适应控制系统,通过建立运动学模型,基于神经内分泌甲状腺激素调节原理,设计了一种带长环、超短环结构的神经内分泌智能控制器,对机器人系统进行控制,并给出了其控制算法。仿真分析结果表明,运动学模型简单有效,使机器人运动过程中各个关节运动稳定、连续且平滑;相对于传统PID控制算法,神经内分泌智能控制算法具有较好的快速响应性、稳定性、鲁棒性、自适应性和抗干扰能力。该方法为机器人的复杂控制提出了一种新思路。     

无刷直流电机模糊自适应控制系统的建模与仿真分析

在分析无刷直流电机数学模型的基础上,通过模块功能划分来搭建无刷直流电机系统的仿真模型.通过Matlab中的Simulink模块和模糊控制工具箱实现模糊自适应整定PID的控制系统,并与常规PID控制比较,有较好的控制效果,适应性强.     

基于模糊遗传算法的二自由度PID控制器优化设计

针对一般遗传算法中交叉概率和变异概率存在的选择困难问题，利用模糊推理系统来自适应估计交叉概率和变异概率，提出了模糊遗传算法（FGAs），并用于二自由度PID控制器参数寻优设计。仿真和实验表明，所设计的二自由度PID控制器可以使系统同时具有良好的目标值跟随特性和干扰抑制特性。     

Design of a fractional order PID controller using GBMO algorithm for load–frequency control with governor saturation consideration

Load–frequency control is one of the most important issues in power system operation. In this paper, a Fractional Order PID (FOPID) controller based on Gases Brownian Motion Optimization (GBMO) is used in order to mitigate frequency and exchanged power deviation in two-area power system with considering governor saturation limit. In a FOPID controller derivative and integrator parts have non-integer orders which should be determined by designer. FOPID controller has more flexibility than PID controller. The GBMO algorithm is a recently introduced search method that has suitable accuracy and convergence rate. Thus, this paper uses the advantages of FOPID controller as well as GBMO algorithm to solve load–frequency control. However, computational load will higher than conventional controllers due to more complexity of design procedure. Also, a GBMO based fuzzy controller is designed and analyzed in detail. The performance of the proposed controller in time domain and its robustness are verified according to comparison with other controllers like GBMO based fuzzy controller and PI controller that used for load–frequency control system in confronting with model parameters variations.     

基于前馈控制的交流伺服系统高速定位控制

在要求高速、快响应的交流伺服定位系统中,采用传统PID控制很难实现高速定位.为解决此问题.在设计出包括位置环、速度环和电流环的三闭环PID伺服控制系统的基础上,引入电流环和速度环的前馈控制来提高定位控制的性能.通过仿真验证了前馈控制在指令信号跟踪方面的良好效果,然后设计出实验系统,进行了与普通PID伺服控制系统的对比实验.结果表明,引入前馈控制的PID伺服控制系统可以实现交流伺服系统的高速定位控制.     

负载模拟器的DRNN神经网络控制

为了增强负载模拟器的自适应能力以抵抗系统的非线性、时变参数及运动扰动的影响,特提出利用对角回归神经网络(DRNN)与PID的并联进行控制与调节的控制方法。PID保证了系统的初始稳定性,由于神经网络引入了速度信号作为参考输入,使系统具有了很好的自适应消扰能力,减小了多余力的影响。仿真和试验证明了该方法的可行性和有效性,收到了很好的控制效果。     

液压机械手电液比例系统模糊PID控制研究

针对液压机械手的电液比例系统存在较大程度的系统参数变化和负载干扰等特点,一般控制方法难以全部满足性能要求。常规PID控制方法虽然算法简单、可靠性好、鲁棒性高,但由于参数整定繁杂,往往造成参数整定不良、性能欠佳、适用性能差。为了改善这些缺陷,将模糊控制理论与PID控制理论相结合,设计了模糊PID控制器,实现了对PID参数的在线整定。利用MATLAB/Simulink进行仿真,比较常规PID控制与模糊PID控制下电液比例系统的控制效果,发现模糊PID控制器较好地克服了系统的非线性和负载干扰的影响,提高了系统的稳定性和动态性能。     

基于RBF神经网络在带钢厚度控制中的应用

轧钢厚度控制系统的数学模型难以精确建立,传统的PID控制器的自适应能力较差,很难达到满意的控制效果。本文根据以上问题。提出了一种新的控制方法,即基于RBF神经网络自整定PID控制方法。这种控制方法结合了RBF神经网络和PID控制器的控制优势,不仅具有很强的自适应能力、鲁棒性。而且充分发挥了PID控制优势,并且将这种控制方法应用在带钢厚度的控制系统中,取得了很好的控制效果,证明了控制方案的正确性和有效性。     

基于模糊PID的永磁同步电机矢量控制仿真

基于传统固定增益PID（Proportion—Integral—Derivative）的永磁同步电机矢量控制系统,存在着响应速度不快、稳态性能较差、转矩脉动较大的缺陷．针对这一问题,利用具有参数自整定功能的模糊PID控制器对矢量控制系统进行改进,并在MATLAB／Simulink环境下建立了系统仿真模型．仿真结果表明：模糊PID控制器可显著提高系统鲁棒性,很好地满足了永磁同步电机（PMSM）高精度、快响应的控制要求．     

车辆动力总成试验台动态模拟控制方法

利用交流测功机模拟车轮受到的路面负载与惯性负载,以实现在室内台架上实现对车辆动力总成系统的动态模拟。进行了车辆纵向动力学模型和台架系统动力学模型的构建,计算出整车等效到主动车轮的转动惯量,利用转速跟踪方法动态模拟了实车工况;采用基于迭代反馈整定理论的二自由度PID控制算法来控制油门踏板,使节气门快速准确地达到目标位置。３次迭代后,油门踏板控制系统超调量降低至14.7%,稳定时间缩短为0.5s,提高了动态模拟的控制精度和响应速度。     

基于遗传算法的PID整定在液位控制中的应用

以IAE为优化目标,将遗传算法优化PID参数,应用于一单容水箱的液位控制系统中,并给出了算法的仿真结果。研究表明,遗传算法得到较好性能指标的搜索结果,对参数整定优化策略具有很强的灵活性、适应性和鲁棒性,具有一定的推广应用价值。