Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.

Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

MigroGrid(MG)has emerged to resolve the growing demand for energy.But because of its inconsistent output,it can result in various power quality(PQ)issues.PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources.Similarly,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system.Thus,this research focuses on power quality enhancement in the MG using hybrid shunt filters.However,the performance of the filter mainly depends upon the design,and stability of the controller.The efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)controller.The performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this approach.Further,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in MG.From the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

A Novel Design Framework for Smart Operating Robot in Power System

This paper proposes the concept and framework of smart operating system based on the artificial intelligence(AI)techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.

Temperature Intelligent Control System of Large-Scale Standing Quench Furnace

Considering some characteristics of large-scale standing quench furnace, such as great heat inertia, evident time lag, strong coupling influence, hard to establish exact mathematical models of plant and etc, an artificial intelligent fuzzy control algorithm is put forward in this paper. Through adjusting the on-off ratio of electric heating elements, the temperature in furnace is controlled accurately. This paper describes structure and qualities of the large-scale standing quench furnace briefly, introduces constitution of control system, and expounds principle and implementation of intelligent control algorithm. The applied results prove that the intelligent control system can completely satisfy the technological requirements. Namely, it can realize fast increasing temperature with a little overshoot, exact holding temperature, and well-distributed temperature in quench furnace. It has raised the output and quality of aluminum material, and brought the outstanding economic and social benefits.

Applications of artificial intelligence technology to wastewater treatment fields in China

Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of system operation, use artificial neuron network for system modeling, water quality forecast and soft measure, and use fuzzy control technology for the intelligence control of wastewater treatment process. Finally, the main problems in applying artificial intelligence technology to wastewater treatment in China are analyzed.

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.

Use of Artificial Intelligence in the Issue of Protection against Negative Impact of Floods

The paper follows possible specification of a control algorithm of a WS （water management system） during floods using the procedures of AI （artificial intelligence）. The issue of minimizing negative impacts of floods represents influencing and controlling a dynamic process of the system where the main regulation elements are water reservoirs. Control of water outflow from reservoirs is implicitly based on the used model （titled BW） based on FR （fuzzy regulation）. Specification of a control algorithm means dealing with the issue of preparing a knowledge base for the process of tuning fuzzy regulators based on an I/O （input/output） matrix obtained by optimization of the target behaviour of WS. Partial results can be compared with the regulation outputs when specialized tuning was used for the fuzzy regulator of the control algorithm. Basic approaches follow from the narrow relation on BW model use to simulate floods, without any connection to real water management system. A generally introduced model allows description of an outflow dynamic system with stochastic inputs using submodels of robust regression in the outflow module. The submodels are constructed on data of historical FS （flood situations）.

基于ChatGPT的生成式人工智能自动化控制系统

将人工智能技术与自动控制相结合是自动化控制系统发展的趋势,因此,设计基于ChatGPT的生成式人工智能自动化控制系统;该系统划分为5个部分,输入层通过语音、文本、图像等形式将用户的控制信息输入系统内;ChatGPT处理层采用神经语义分析算法分析用户输入的控制信息的语义,提取用户意图与需求,生成相应的控制指令;在此基础上,指令生成层负责将生成的控制指令转化为具体的控制信号,并传输至控制执行层,利用模糊PID控制器实现被控目标的自动化控制;控制后学习优化层收集和分析用户的行为和反馈,以及被控目标的运行状态,以此学习和优化ChatGPT模型;经实验测试,该系统能够有效分析输入控制信息的语义,分析结果的问题匹配度和上下文相关交互匹配度分别达到97%和91%以上,并且能够准确控制被控目标的动作。

基于粒子群优化算法的太阳能水培智能控制系统设计与实现

为实现水培营养液水质参数的高效、精确控制,减少设备供能产生的碳排量,构建了一个基于粒子群优化(Particle Swarm Optimization,PSO)算法和最大功率点跟踪(Maximum Power Point Tracking,MPPT)算法的水培智能控制系统。用PSO算法优化模糊控制器的量化、比例因子,加入Smith预估器补偿反馈时延,对pH为4.5、电导率(Electrical Conductivity,EC)为0 mS/cm的营养液进行精确调控。经过优化,分别在44 s和43 s后达到预设值,并能维持稳定状态。建立光伏发电模块,引入MPPT算法,缩短跟踪时长至0.04 s。结果表明,该系统能提高营养液水质参数的调节精度,缩短控制时长,增强水培环境的稳定性;同时,能提升发电效率,实现节能减排。

大电网调控人机混合增强智能:概念内涵、应用框架、关键技术以及系统验证

伴随深度学习等人工智能技术的飞速发展,通过数字化赋能电力来实现电网的信息化、数字化和智能化成为未来新型电力系统发展的必然趋势。人工智能技术在电网调控应用研究广泛,但是实际应用过程中,由于电网不确定性、开放性和脆弱性造成的模型可信性和可解释性等问题,使得单纯的人工智能技术应用于电网调控领域难以满足安全可靠应用的需求。为了解决上述问题,需要引入人类的监督与互动,融合调控人员的智能与机器智能,实现电网调控人机智能的混合增强。该文首先介绍混合增强智能技术的概念和内涵以及在不同领域的研究现状。随后,针对电网调控业务特点,从人工智能和调控应用两个维度,提出电网调控人机混合增强智能的应用框架以及电网调控人机交互协同的目标和基本原则。在此基础上,分析和讨论电网调控人机混合增强智能所涉及的关键技术,并研发大电网调控人机混合紧急控制决策支持验证系统,用于省级规模电网的热稳调整、频率控制等多种电网调控场景应用验证,为人机混合增强智能技术在电网调控领域的应用提供参考和借鉴。

基于MPC的智能车辆路径规划与跟踪控制

针对在障碍物环境下的避障路径动态规划效果较差,以及在面对复杂工况和曲率较大的路况时,跟踪控制的效果仍然不理想等问题,本文以智能车辆为研究对象,提出了一种模型预测控制(MPC)结合人工势场(APF)算法的路径规划跟踪系统。将改进的势场模型函数引入到MPC的目标函数和约束中,设计了基于MPC和APF的避障路径动态规划器。。运用模糊控制对MPC的车辆横向路径跟踪控制器的权重系数进行优化。仿真结果表明:在干燥路面下,与MPC控制器相比,模糊MPC路径跟踪控制器的最大横向偏差减少19.14%。在湿润路面下,模糊MPC控制器最大横向偏差减少0.55 m。基于MATLAB/Simulink与Carsim软件搭建避障路径规划与跟踪控制联合仿真模型,选择动态障碍物不同速度进行障碍物路径动态规划及跟踪控制仿真试验。实验结果表明:跟踪规划路径过程中的最大横向偏差约为0.170 m,说明规划的避障路径能够安全有效地避开障碍物。

最优Fuzzy-GA PID控制器及其应用

提出了一种基于模糊推理与遗传算法的最优PID控制器的设计方法 .该控制器由离线和在线 2部分组成 .在离线部分 ,以系统响应的超调量、上升时间及调整时间为性能指标 ,利用遗传算法搜索出一组最优的PID参数K p ,T i 及T d ,作为在线部分调节的初始值 ;在在线部分 ,采用一个专用的PID参数优化程序 ,以离线部分获得的K p ,T i 及T d 为基础 ,根据系统当前的误差e和误差变化率 e ,通过模糊推理在线调整系统瞬态响应的PID参数 ,以确保系统的响应具有最优的动态和稳态性能 .计算机仿真结果表明 ,与传统的PID控制器相比 ,这种最优PID控制器具有良好的控制性能和鲁棒性能 。

碳减排政策下的农机装备智能优化作业研究

针对我国农机装备能源消耗过大导致温室气体排放多、智能化程度较低的问题,以太阳能自动灌溉系统为例,在碳减排政策下对其进行智能优化研究。系统的主要组成包括供电系统、自动控制系统、监测和显示系统、抽蓄水系统以及通讯系统。自动灌溉系统采用模糊控制器进行控制,且对模糊控制器的结构、组成和算法进行了设计。为了验证系统性能,对其进行了数据传输和自动灌溉控制试验,结果表明:系统可以实现对灌溉的智能控制,且太阳能供电装置还可应用于其他农机装备。

基于安全深度强化学习的电网有功频率协同优化控制

可再生能源占比不断增加给互联电网频率控制带来严峻考验.由于常规的自动发电控制(AGC)策略没有考虑电网潮流安全约束,所以传统方法根据专家知识和经验进行尝试性发电机功率调整,需耗费较多时间;基于最优电力潮流的互联电网AGC优化模型由于非凸性和大规模性,求解时间较长且存在收敛性问题.鉴于常规深度强化学习具有“离线训练、在线端对端形成策略”的优点,但在动作探索过程中无法保证系统安全性,提出一种基于安全深度强化学习的电网有功频率协同优化控制方法.首先,将电网频率控制建模为约束马尔可夫决策过程,对决策过程添加相关安全约束进行智能体设计;然后,基于华东电网实际系统算例对智能体进行训练和性能提升;最后,对比智能体决策与常规AGC策略效果.结果表明:所提方法在多种运行方式下可快速生成有功频率控制策略,且保证系统频率恢复过程中电网的安全性,可辅助调度员在线决策.

基于Fuzzy-PID的人工气候室智能控制系统设计

介绍了控制对象人工气候室的特点 ,阐述了Fuzzy-PID控制原理 ,并把通过开关切换实现的Fuzzy-PID控制算法实际应用到人工气候室智能控制系统中.给出了该控制系统Fuzzy-PID控制器的总体设计方案、具体设计过程,解决了人工气候室控制中的难点问题.

基于人工智能技术的电力安全风险控制研究

为了有效评估电力安全风险,提升电力安全风险控制效果,设计基于人工智能技术的电力安全风险控制方法。首先建立电力安全风险评估指标体系,通过学习向量量化神经网络评估电力安全风险,按照电网实际情况设置阈值建立触发机制,判断风险值是否超过阈值,若超过阈值,则触发电力安全风险控制模型,以最低电力损耗为目标函数建立电力安全风险控制模型,通过改进布谷鸟算法求解该模型,根据解得到电力安全风险控制策略。实验证明:该方法可有效评估电力安全风险,可以使电网负荷点电压标幺值迅速恢复至平稳状态,降低风险事件发生严重度与电力损耗。