Optimized Model Based Controller with Model Plant Mismatch for NMP Mitigation in Boost Converter

In this paper,an optimized Genetic Algorithm(GA)based internal model controller-proportional integral derivative(IMC-PID)controller has been designed for the control variable to output variable transfer function of dc-dc boost converter to mitigate the effect of non-minimum phase(NMP)behavior due to the presence of a right-half plane zero(RHPZ).This RHPZ limits the dynamic performance of the converter and leads to internal instability.The IMC PID is a streamlined counterpart of the standard feedback controller and easily achieves optimal set point and load change performance with a single filter tuning parameterλ.Also,this paper addresses the influences of the model-based controller with model plant mismatch on the closed-loop control.The conventional IMC PID design is realized as an optimization problem with a resilient controller being determined through a genetic algorithm.Computed results suggested that GA–IMC PID coheres to the optimum designs with a fast convergence rate and outperforms conventional IMC PID controllers.

A Novel ANFIS Based SMC with Fractional Order PID Controller

Interacting The highest storage capacity of a circular tank makes it pop-ular in process industries.Because of the varying surface area of the cross-sec-tions of the tank,this two-tank level system has nonlinear characteristics.Controlling theflow rate of liquid is one of the most difficult challenges in the production process.This proposed effort is critical in preventing time delays and errors by managing thefluid level.Several scholars have explored and explored ways to reduce the problem of nonlinearity,but their techniques have not yielded better results.Different types of controllers with various techniques are implemented by the proposed system.Sliding Mode Controller(SMC)with Fractional Order PID Controller based on Intelligent Adaptive Neuro-Fuzzy Infer-ence System(ANFIS)is a novel technique for liquid level regulation in an inter-connected spherical tank system to avoid interferences and achieve better performance in comparison of rise time,settling time,and overshoot decrease.Evaluating the simulated results acquired by the controller yields the efficiency of the proposed system.The simulated results were produced using MATLAB 2018 and the FOMCON toolbox.Finally,the performance of the conventional controller(FOPID,PID-SMC)and proposed ANFIS based SMC-FOPID control-lers are compared and analyzed the performance indices.

Multi-loop Internal Model Controller Design Based on a Dynamic PLS Framework

In this paper,a multi-loop internal model control(IMC) scheme in conjunction with feed-forward strategy based on the dynamic partial least squares(DyPLS) framework is proposed.Unlike the traditional methods to decouple multi-input multi-output(MIMO) systems,the DyPLS framework automatically decomposes the MIMO process into a multi-loop system in the PLS subspace in the modeling stage.The dynamic filters with identical structure are used to build the dynamic PLS model,which retains the orthogonality among the latent variables.To address the model mismatch problem,an off-line least squares method is applied to obtain a set of optimal filter parameters in each latent space.Without losing the merits of model-based control,a simple and easy-tuned IMC structure is readily carried over to the dynamic PLS control framework.In addition,by projecting the measurable disturbance into the latent subspace,a multi-loop feed-forward control is yielded to achieve better performance for disturbance rejection.Simulation results of a distillation column are used to further demonstrate this new strategy outperforms conventional control schemes in servo behavior and disturbance rejection.

PID控制的阀控缸系统的Modelica建模与仿真实现

针对PID控制的阀控缸系统基于Modelica语言在开源仿真环境OpenModelica中进行了建模和仿真。给出了一套完善的阀控缸系统的建模思路,且所建立的模型具有很好的模型复用性。通过与AMESIM中建立的相同系统在相同参数下的仿真结果进行对比。结果表明,二者仿真结果基本一致,验证了所建立模型的正确性。由于整套系统均在开源环境下建模并仿真,因此解决了在实际工程中仿真PID控制的阀控缸系统时由于商业软件价格昂贵而造成的仿真成本过高的问题。

Hierarchical CNNPID Based Active Steering Control Method for Intelligent Vehicle Facing Emergency Lane-Changing

To resolve the response delay and overshoot problems of intelligent vehicles facing emergency lane-changing due to proportional-integral-differential(PID)parameter variation,an active steering control method based on Convolutional Neural Network and PID(CNNPID)algorithm is constructed.First,a steering control model based on normal distribution probability function,steady constant radius steering,and instantaneous lane-change-based active for straight and curved roads is established.Second,based on the active steering control model,a three-dimensional constraint-based fifth-order polynomial equation lane-change path is designed to address the stability problem with supersaturation and sideslip due to emergency lane changing.In addition,a hierarchical CNNPID Controller is constructed which includes two layers to avoid collisions facing emergency lane changing,namely,the lane change path tracking PID control layer and the CNN control performance optimization layer.The scaled conjugate gradient backpropagation-based forward propagation control law is designed to optimize the PID control performance based on input parameters,and the elastic backpropagation-based module is adopted for weight correction.Finally,comparison studies and simulation/real vehicle test results are presented to demonstrate the effectiveness,significance,and advantages of the proposed controller.

Vehicle Active Steering Control Research Based on Two-DOF Robust Internal Model Control

Because of vehicle＇s external disturbances and model uncertainties,robust control algorithms have obtained popularity in vehicle stability control.The robust control usually gives up performance in order to guarantee the robustness of the control algorithm,therefore an improved robust internal model control（IMC） algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness.The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties.In order to separate the design process of model tracking from the robustness design process,the improved 2 degree of freedom（DOF） robust internal model controller structure is given from the standard Youla parameterization.Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm,on the basis of a nonlinear vehicle simulation model with a magic tyre model.Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance,which can enhance the vehicle stability and handling,regardless of variations of the vehicle model parameters and the external crosswind interferences.Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Application of PID Controller Based on BP Neural Network in Export Steam’s Temperature Control System

By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.

SVM Approximate-based Internal Model Control Strategy

一台支持向量机器(SVM ) 近似底的内部模型控制(IMC ) 策略为同步发电机的蒸气 valving 控制被介绍。建议 SVM IMC 策略包括二主要部分：在内部模型结构的 SVM 近似反的控制器和无常赔偿。SVM 反的控制器经由泰勒扩大用一条输入产量近似途径直接被导出，并且没有进一步的联机训练，它通过非线性的系统鉴定被实现。而且，一个坚韧性过滤器在内部模型结构被用于无常赔偿。模拟为蒸气 valving 控制显示出 SVM IMC 策略的有效性。

Support vector regression-based internal model control

This paper proposes a design of internal model control systems for process with delay by using support vector regression(SVR).The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle.Closed-system stability and steady error are analyzed for the existence of modeling errors.The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.

Damping controller design based on FO-PID-EMA in VSC HVDC system to improve stability of hybrid power system

Wind energy sources have different structures and functions from conventional power plants in the power system.These resources can affect the exchange of active and reactive power of the network.Therefore,power system stability will be affected by the performance of wind power plants,especially in the event of a fault.In this paper,the improvement of the dynamic stability in power system equipped by wind farm is examined through the supplementary controller design in the high voltage direct current(HVDC)based on voltage source converter(VSC)transmission system.In this regard,impacts of the VSC HVDC system and wind farm on the improvement of system stability are considered.Also,an algorithm based on controllability(observability)concept is proposed to select most appropriate and effective coupling between inputs-outputs(IO)signals of system in different work conditions.The selected coupling is used to apply damping controller signal.Finally,a fractional order PID controller(FO-PID)based on exchange market algorithm(EMA)is designed as damping controller.The analysis of the results shows that the wind farm does not directly contribute to the improvement of the dynamic stability of power system.However,it can increase the controllability of the oscillatory mode and improve the performance of the supplementary controller.

Temperature Control Based on Fuzzy Logic Two-degree-of-freedom Smith Internal Model

According to the characteristics of the large time delay,nonlinearity and the great inertia of temperature control system in biomass pyrolysis reactor,a two-degree-of-freedom Smith internal model controller based on fuzzy control is proposed.Firstly,the mathematical model of the temperature control system is established by using the step response method,and then the two-degree-of-freedom Smith internal model controller is designed,and the good tracking performance and disturbance suppression performance can be obtained by designing the set value tracking controller and interference rejection capability.Secondly,the fuzzy control algorithm is used to realize the on-line tuning of the control parameters of the two-degree-of-freedom Smith internal model algorithm.The simulation results show that,compared with the traditional internal model control,fuzzy internal model PID control and two-degree-of-freedom Smith internal model control,the algorithm proposed in this paper improves the influence of lag time on the control system,realizes the separation control of set point tracking and anti-jamming performance and the self-tuning of control parameters,and improves the control performance of the system.

Eigen-Space Decomposition(ESD)Method for the Design of Internal Model Controller(IMC)from Noisy Input and Output Plant Data

A novel approach to design Internal Model Controller(IMC)is proposed in this paper directly from measuredinput and output plant data,which are assumed to becontaminated by measurement noise.In order to avoidthe complicated structure-identification problem inmost cases,two Finite Impulse Response(FIR)modelsare taken to represent the plant model and the internalmodel controller respectively.Taking account of mea-surement noise both in the plant input and its output,anESD based Total Least Squares(TLS)solution is appliedfor the unbiased identification of the plant model and itsinverse model,the latter constitutes the internal modelcontroller according to the principle that the internalmodel controller approximates the inverse dynamics ofthe plant model.Simulations are given for a testifica-tion.

Synthetical Control of AGC/LPC System Based on Neural Networks Internal Model Control

One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

基于前馈补偿LQR与PID的矿井无轨胶轮车横纵向控制研究

无人驾驶技术是实现无轨胶轮车井下安全、智能、高效运输的重要方案之一,为了提高无人驾驶过程中的轨迹跟踪精度,提出了基于前馈补偿的横向线性二次型调节器(Linear Quadratic Regulator,LQR)与纵向比例积分微分(Proportion Integration Derivative,PID)位移速度调节器相结合的控制策略,实现车辆的横纵向协调控制。通过建立考虑轮胎侧偏特性的2自由度无轨胶轮车动力学模型和跟踪误差模型,并采用井下无轨胶轮车实车参数建立其电机模型,得到车辆的驱动制动输出。利用Carsim和Matlab/Simulink搭建联合仿真环境,分别在井下双车道工况、单车道工况与颠簸路面工况下进行了轨迹跟踪仿真验证。结果表明:在3种工况下车辆轨迹跟踪过程中的最大横向误差仅为5 cm,最大纵向误差仅为10 cm,速度误差控制在1 m/s以内,航向误差范围为±0.1 rad,前轮偏转角变化平稳未出现抖动现象。为验证控制器在井下实际环境下的跟踪性能,使用实验室小车于陕西某井下巷道进行了现场试验验证,结果表明:井下实际巷道下试验结果误差仍在合理范围内,解决了车辆运行过程中的速度和路径的时变问题,反映出该控制器具有较高的精度和较好的稳定性。

基于模糊PID控制的柔性下肢助力外骨骼

为给老年人在行走时提供辅助力,设计了一种对人体下肢髋关节和膝关节提供助力的柔性外骨骼,针对直流电动机进行了动力学建模,利用模糊自适应PID控制算法对柔性外骨骼进行控制。通过Simulink仿真分析得出,模糊自适应PID控制算法能够有效地跟踪电动机的期望轨迹曲线。与传统PID控制算法对比,模糊自适应PID算法能有效地抑制外界环境因素对系统的干扰,适应性较强。

SWISS整流器分数阶PID控制策略

针对SWISS整流器的传统整数阶PID控制策略动态响应速度较慢、鲁棒性较差的问题,提出一种基于分数阶PID的SWISS整流器控制策略。基于SWISS整流器的工作原理建立其小信号模型,得到SWISS整流器的等效传递函数,并设计基于分数阶PID电压控制的双闭环控制结构。为简化分数阶PID的阶次参数设计,采用整数阶PID参数预整定的方法得到参数初值,并利用粒子群算法对分数阶PID控制器参数进行进一步设计优化。仿真和实验结果表明,分数阶PID控制策略下的SWISS整流器发生参考电压突变时电压稳定时间小于7 ms、超调量小于2%,发生负载扰动时电压稳定时间小于4 ms、超调量小于3%,动态性能优于整数阶PID控制策略。

基于STM32的密炼机自整定PID温控系统设计

针对密炼机温度控制系统中人工整定PID带来的系统响应不及时、误差大等问题,该文设计了一种基于STM32的自整定PID温度控制系统。对于电动阀只能单极性调节问题,通过设置适当的工艺点来控制继电输出。为提高震荡曲线的辨识,采用参数归一化方法计算震荡曲线的临界增益。考虑密炼机的工作环境和系统响应曲线的频率特性,加入带有临界滞环控制的继电控制方法,提高了特征参数的精确性,更好地整定了PID参数。应用结果表明,基于STM32的PID整定温度控制系统能够将温度误差控制在1℃左右,且控制时间相比于人工PID控制系统提高了30%,极大地满足了密炼机的温度控制要求,具有良好的实用价值。

Modified Two-Degrees-of-Freedom Internal Model Control for Non-Square Systems with Multiple Time Delays

A modified two-degrees-of-freedom( M-TDOF) internal model control( IMC) method is proposed for non-square systems with multiple time delays and right-half-plane( RHP) zeros. In this method,pseudo-inverse is introduced to design the internal model controller,and a desired closed-loop transfer function is designed to eliminate the unrealizable factors of the derived controller. In addition,set-point tracking and load-disturbance rejection of each process are separately controlled by two controllers. The simulation results show that in addition to high decoupling performance and robustness,the proposed control method also effectively improves loaddisturbance rejection and simultaneously optimizes the input tracking performance and disturbance rejection performance by selecting the parameters of controllers. Furthermore,the higher tolerance of model mismatch is achieved in this paper.

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.