Application of single neuron adaptive PID controller during the process of timber drying

The paper presents a method of using single neuron adaptive PID control for adjusting system or servo system to implement timber drying process control, which combines the thought of parameter adaptive PID control and the character of neural network on exactly describing nonlinear and uncertainty dynamic process organically. The method implements functions of adaptive and self-learning by adjusting weighting parameters. Adaptive neural network can make some output trail given hoping value to decouple in static state. The simulation result indicates the validity, veracity and robustness of the method used in the timber drying process

Adaptive Nonlinear PID Control and Rule-Based Compensation for Systems with Backlash

The control of dynamic nonlinear systems with unknown backlash was considered. By using an efficient approach to estimate the unknown backlash parameters, a rule? based backlash compensator was presented for canceling the effect of backlash. Adaptive nonlinear PID controller together with rule? based backlash compensator was developed and a satisfactory tracking performance was achieved. Simulation results demonstrated the effectiveness of the proposed method.

基于PPO的自适应PID控制算法研究

采用MATLAB物理引擎联合Python搭建了一个六轴机械臂,并模拟带有扰动的复杂控制环境,为机械臂训练提供现实中无法提供的试错环境。使用强化学习中近端优化算法(proximal policy optimization,PPO)算法对传统PID控制算法进行改进,引入多智能体思想,根据PID三个参数对控制系统的不同影响及六轴机械臂的特性,将三个参数分别作为不同的智能个体进行训练,实现多智能体自适应调整参数的新型多智能体自适应PID算法。仿真结果表明:该算法的训练收敛性优于MA-DDPG与MA-SAC算法,与传统PID算法的控制效果相比,在遇到扰动及振荡的情况下,能够更有效地抑制振荡,并具有更低的超调量和调整时间,控制过程更为平缓,有效提高了机械臂的控制精度,证明了该算法的鲁棒性及有效性。

基于模糊自适应PID的汽车硅油风扇离合器性能测试系统研究

以汽车硅油风扇离合器为研究对象,结合单片机和传感器技术,开发出性能测试系统.提出模糊自适应PID动态控制算法,设计控制器控制温度,利用速度传感器检测风扇转速,对测试系统进行整体设计,给出了硬件总体设计框图,设计模拟量输入输出电路,并设计对应的软件系统.实验结果表明:测试系统升降温速率最高不超过8.5℃/min,耦合区加温速率约为2.3℃/min,分离区降温速率约为6.6℃/min,系统性能稳定,符合标准.

引入PID反馈的SHAEKF算法估算电池SOC

电池荷电状态(SOC)的估算精度是电动汽车电池组的重要指标。为提升SOC估算精度,在融合Sage-Husa扩展卡尔曼滤波(SHEKF)算法与自适应扩展卡尔曼滤波(AEKF)算法的基础上,增加比例积分微分(PID)反馈环节,形成改进算法。采用粒子群优化(PSO)算法对二阶RC等效电路模型进行参数辨识;用开源电池数据集对模型和算法进行实验和分析。改进的SHAEKF算法在电池动态应力测试(DST)、北京动态应力测试(BJDST)和美国联邦城市驾驶(FUDS)等工况下的平均估计误差都在1%以内,与单纯的融合算法SHAEKF算法相比,最大误差可减小5%。

基于模糊PID控制的粉碎机转速和位置自适应控制研究

为提高粉碎机的控制性能,提出一种基于模糊PID控制的粉碎机转速和位置自适应控制算法。方法以粉碎机STM86步进电机控制为研究对象,构建粉碎机驱动电机数学模型,并结合PID控制和模糊控制设计基于模糊PID控制算法,以实现粉碎机的自适应控制。结果表明,模糊PID控制算法对粉碎机速度和位置控制的超调量分别为4.20%和0.13%,调节时间分别为0.21 s和0.02 s、平均跟随误差分别为0.02 mm和0.01 r/min;相较于PID控制、模糊滑模控制、模糊自抗干扰控制算法,模糊PID控制算法具有更低的超调量,更短的调节时间和更小的平均跟随误差。由此得出,模糊PID控制方法可实现更优异的粉碎机自适应控制,且具有一定的实际应用价值。

自适应PID在太阳能-新风系统温度控制中的应用

太阳能-新风系统是将太阳能热利用与新风系统相结合,用于室内进行通风换气的新型辅助供暖系统。太阳能-新风系统具有大惯性、滞后、非线性的特点,其数学模型难以精确建立,常规PID控制很难使新风温度保持平稳状态。为解决这一问题,设计了用于太阳能-新风系统的自适应PID控制器,通过在线调整PID控制器的参数来精准控制新风温度,以期为实现太阳能-新风系统温度控制提供另一种思路。

基于多传感器融合的模糊PID采煤机截割路径自适应控制方法

截割路径自适应控制是提高采煤机生产效率和质量的重要手段,但现行方法控制精度较低,在实际中,截割路径RMSE、MAE均比较高。针对现行方法存在的不足和缺陷,提出基于多传感器融合的模糊PID采煤机截割路径自适应控制方法。采用多传感器融合技术感知采煤机截割运动学模型中位置及状态信息,并对多传感器数据融合处理,根据采煤机截割运动学规律得到采煤机截割路径,模糊化截割路径偏差,利用PID控制器调控比例、积分、微分3个参数,修正采煤机截割路径偏差,实现基于多传感器融合的模糊PID采煤机截割路径自适应控制。实验证明,设计方法应用下的采煤机截割路径RMSE和MAE得到了明显降低,为采煤机截割路径自适应控制提供了一定参考。

基于自适应模糊PID算法的锂电池组双层均衡控制

为提高电池重组时的均衡效率,在传统Buck-Boost均衡拓扑电路的基础上,设计了一种锂电池组双层均衡拓扑电路。组内采用Buck-Boost电路均衡,组间利用双向反激变压器进行均衡。均衡控制策略采用自适应模糊PID算法,以电池荷电状态(state of charge, SOC)为均衡变量,利用模糊控制算法对PID参数进行调节,缩短了均衡时间,提高了均衡效率。在Matlab/Simulink中搭建了锂电池组双层均衡拓扑电路和自适应模糊PID控制算法模型。实验结果表明:在不同工作状态下,所提出的电池组均衡拓扑及其控制策略将均衡时间效率平均提高了58.36%,验证了该方案的有效性。

基于改进自适应遗传算法的小径短绳自动打捆机步进电机PID控制

为提高小径短绳自动打捆机步进电机的控制精度,在分析经典遗传算法和经典自适应遗传算法的基础上,引入进化代数和个体适应值排名来指导自适应交叉率和自适应变异率,设计了一个基于改进自适应遗传算法的PID控制器,以削弱进化过程中优良参数的退化现象。MATLAB仿真结果表明,改进的自适应遗传PID提高了收敛精度,并具有更快的收敛速度。针对步进电机的实际测试结果表明,与经典遗传算法相比,改进的自适应遗传PID调整时间更短、超调量更低。

基于模糊PID算法的食用菌大棚温度控制系统研究

食用菌大棚温度控制系统是一个具有时滞性的复杂系统,传统的开关量或常规PID控制很难满足设计要求。该研究设计一种基于STM32F103CRT6单片机为控制核心,加入模糊自适应PID控制算法,利用DHT11温湿度传感器进行温度实时采集,单片机运算处理,光耦固态继电器驱动电路对热风机进行控制,从而达到温度调节的目的。并在MATLAB/simulink环境下建立常规PID控制和模糊自适应PID控制的仿真模型,通过仿真结果,对比验证了该方法比常规PID控制方法具有低超调量和调节时间短等较好的控制效果。

Study on Fuzzy Self-Adaptive PID Control System of Biomass Boiler Drum Water

The paper discusses the features of the Biomass Boiler drum water level. Conventional PID Control System can not reach a satisfaction result in nonlinearity and time different from Biomass Boiler Drum Water Control System. In this study, a kind of fuzzy self-adaptive PID controller is described and this controller is used in biomass boiler’s drum water level control system. Using the simulink tool of MATLAB simulation software to simulate the fuzzy adaptive PID and conventional PID control system, the result of the comparison shows that the fuzzy self-adaptive PID has the strong anti-jamming, flexibility and adaptability as well as the higher control precision in Biomass Boiler Drum Water.

基于Adaptive Interaction理论的PID控制器的设计及应用

本文在Adaptive Interaction理论的基础上,提出了一种新的自调整 PID 控制器。这种新的控制器根据输入及其误差信号进行在线训练,通过误差评价函数的最小化,在模型未知的情况下能很好地调整比例、积分、微分三个参数。对于被控对象的变化具有鲁棒性,很大程度上解决了传统的 PID 控制器对于非线性、不稳定系统控制效果不佳及在线调整困难的问题。通过仿真实例,验证了应用 Adaptive Interaction 理论的 PID 控制器的有效性和实用性。

Application of Single Neuron Adaptive PID Regulators with Auto-tuning Gain in Industrial Parameter(Pressure)Closed-loop Process Control Systems

A type of single neuron adaptive PID regulator with auto-tuning gain is proposed and applied to the work control of fans, waterpumps and air-pressers etc. in Handan Iron & Steel Compel China. The robusthess of induStrial parameter closed-loop process controlsystems is improved, and the work quality of the systems bettered.

基于预测自适应PID控制的家禽养殖基地温度控制系统设计

家禽养殖时,影响家禽生存状态的因素很多,其中最直接的因素是温度。由于温度控制的滞后性比较大,温度控制的要求不同,控制参数和设定要求存在差异。针对不同环境下的控制要求,结合普通PID的控制调节方式,提出一种能普遍适应的控制方法。其核心是基于常规PID的控制基础建立预测自适应控制模型,实现对控制系统中温度的预测自适应PID调节。该方法将模型预测所得的未来一段时间内系统输出值与期望值的均方误差作为性能指标,以间接的方式动态调节PID参数,控制精度较高,充分发挥了PID方法的鲁棒性。所提方法实现了温度的精准控制,能够改善家禽的生活环境,实现高效的无人化管理环境,对于实际的家禽饲养提供参考。

Adaptive PID control system for an autonomous underwater vehicle

The control system of an autonomous underwater vehicle （AUV） is introduced. According to control requirements of the AUV, a simple but practical adaptive PID control method is designed The semi-physical simulation is done to test the feasibility of the control system. The neural network idea and the structure of PID controller are referred to design the adaptive PID controller. An intelligent integral is introduced to improve control precision. Compaed with traditional PID con- trollers, the adaptive PID controller has simple structure, good online adjusting ability, fast convergence and good robustness. The simulation experiments also show that the adaptive PID control system has high precision and fine antijamming ability.

Fuzzy adaptive PID control on hydraulic servo system of quadruped robot

Quadruped robot driven by high power density hydraulic device works in unstructured en- vironment. With variable load and various external disturbance, the hydraulic servo system has fea- tures such as nonlinear, time-varying parameters. Traditional control method has some limitation. In order to help the hydraulic servo system of the quadruped robot to adapt to harsh environments, and to obtain high control quality and control precision, an incremental fuzzy adaptive PID controller based on position feedback is designed to solve the related technical problems. Matlab/Simulink sim- ulation and experimental results show that the incremental fuzzy adaptive PID controller improves the dynamic performance of the system, enhances the respond speed and precision of the hydraulic ser- vo system, and has some theory significance and practical value.

Adaptive PID controller based on Q-learning algorithm

An adaptive proportional–integral–derivative(PID)controller based on Q-learning algorithm is proposed to balance the cart–pole system in simulation environment.This controller was trained using Q-learning algorithm and implemented the learned Q-tables to change the gains of linear PID controllers according to the state of the system during the control process.The adaptive PID controller based on Q-learning algorithm was trained from a set of fixed initial positions and was able to balance the system starting from a series of initial positions that are different from the ones used in the training session,which achieved equivalent or even better performances in comparison with the conventional PID controller and the controller only uses Q-learning algorithm.This indicates the advantage of the adaptive PID controller based on Q-learning algorithm both in the generality of balancing the cart–pole system from a relatively wide range of initial positions and in the stabilisability of achieving smaller steady-state error.

A Proposal of Adaptive PID Controller Based on Reinforcement Learning

Aimed at the lack of self-tuning PID parameters in conventional PID controllers, the structure and learning algorithm of an adaptive PID controller based on reinforcement learning were proposed. Actor-Critic learning was used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of reinforcement learning effectively. In order to reduce the demand of storage space and to improve the learning efficiency, a single RBF neural network was used to approximate the policy function of Actor and the value function of Critic simultaneously. The inputs of RBF network are the system error, as well as the first and the second-order differences of error. The Actor can realize the mapping from the system state to PID parameters, while the Critic evaluates the outputs of the Actor and produces TD error. Based on TD error performance index and gradient descent method, the updating rules of RBF kernel function and network weights were given. Simulation results show that the proposed controller is efficient for complex nonlinear systems and it is perfectly adaptable and strongly robust, which is better than that of a conventional PID controller.

An Adaptive Neuro-Fuzzy Inference System to Improve Fractional Order Controller Performance

The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.