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.

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.

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.

Research on Power Control of Wind Power Generation Based on Neural Network Adaptive Control

For the characteristics of wind power generation system is multivariable, nonlinear and random, in this paper the neural network PID adaptive control is adopted. The size of pitch angle is adjusted in time to improve the perfomance of power control. The PID parameters are corrected by the gradient descent method, and Radial Basis Functiion （RBF） neural network is used as the system identifier in this method. Sinlation results show that by using neural network adaptive PID controller the generator power control can inhibit effectively the speed and affect the output prover of generator. The dynamic performnce and robustness of the controlled system is good, and the peformance of wind power system is improved.

A New Equalization Method for Lithium-Ion Battery Packs Based on CUK

Aiming at the traditional CUK equalizer can only perform energy equalization between adjacent batteries,if the two single batteries that need to be equalized are far away from each other,there will be the problem of longer energy transmission path and lower equalization efficiency,this paper optimizes the CUK equalizer and optimizes its peripheral selection circuit,which can support the equalization of single batteries at any two positions.The control strategy adopts the open-circuit voltage(OVC)of the battery and the state of charge(SOC)of the battery as the equalization variables,and selects the corresponding equalization variables according to the energy conditions of the two batteries that need to be equalized,and generates the adaptive equalization current with an adaptive PID controller in order to improve the equalization efficiency.Simulation modeling is performed in Matlab/Simulink 2021b,and the experimental results show that the optimized CUK equalizer in this paper improves the equalization time by 25.58%compared with the traditional CUK equalizer.In addition,compared with the mean value difference(MVD)method,the adaptive PID method reduces the equalization time by about 30%in the static and charging and discharging experimental environments,which verifies the superiority of this equalization scheme.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

Research on Suspension Gravity Compensation System of Lunar Rover with Magnetic Levitation Servo

In order to overcome the shortcomings of the traditional sling suspension method,such as complex structure of suspension truss,large running resistance,and low precision of position servo system,a gravity compensation method of lunar rover based on the combination of active suspension and active position following of magnetic levitation is proposed,and the overall design is carried out.The dynamic model of the suspension module of microgravity compensation system was established,and the decoupling control between the constant force component and the position servo component was analyzed and verified.The constant tension control was achieved by using hybrid force/position control.The position following control was realized by using fuzzy adaptive PID(proportional⁃integral⁃differential)control.The stable suspension control was realized based on the principle of force balance.The simulation results show that the compensation accuracy of constant tension could reach more than 95%,the position deviation was less than 5 mm,the position deviation angle was less than 0.025°,and the air gap recovered stability within 0.1 s.The gravity compensation system has excellent dynamic performance and can meet the requirements of microgravity simulation experiment of lunar rover.

Study of Multi media Streams Dynamic Rate Control Based on Fuzzy Adaptive PID

A Multimedia streams dynamic rate control algorithm based on Fuzzy adaptive PID （MFPID） has been proposed to implement multimedia streams＇ end sending rate on-line self-regulating and smoothing, and to track system resources in time, so that it can avoid system＇s regulating oscillation and guarantee system＇s stability. And, some work has been done to analyze adaptive session model of multimedia streams, to implement future available bandwidth estimation of IP network, to achieve PID parameters＇ on-line self-tuning by fuzzy controlling. Simulation validated the theoretical results of MFPID.

Application of a simplified Grey Wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system

A Simplified Grey Wolf Optimizer(SGWO)is suggested for resolving optimization tasks.The simplification in the original Grey Wolf Optimizer(GWO)method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process.The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal,multimodal,and fixed dimension test functions.The results are also contrasted to the Gravitational Search Algorithm,the Particle Swarm Optimization,and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique.Practical application in a Distributed Power Generation System(DPGS)with energy storage is then considered by designing an Adaptive Fuzzy PID(AFPID)controller using the suggested SGWO method for frequency control.The DPGS contains renewable generation such as photovoltaic,wind,and storage elements such as battery and flywheel,in addition to plug-in electric vehicles.It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task.It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller.A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance.Finally,the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.

Suspending Control Scheme of 8/10 Bearingless SRM Based on Adaptive Fuzzy PID Controller

As a nonlinear,strong coupling and multi-variable system,the drive performance of bearingless switched reluctance motor(BLSRM)is always limited by its complicated electromagnetic properties.Generally,conventional PID methods are used to achieve the basic control requirement in wide industrial applications,however its inherent operating principle limits its use on suspending control of BLSRM.In this paper,the suspending force system,which is separately controlled from torque system,is built based on an adaptive fuzzy PID controller to limit the rotor eccentric displacement with proper generation of radial force.When compared with a system adopted using conventional PID method for suspending force control,the proposed adaptive fuzzy PID method has superior performance in shortening the response time,reducing the maximum eccentric displacement error and higher speed range of operation due to its online self-turning of controller parameters.Both in simulation and experimental cases,comparison of results of the above two methods validates the effectiveness of the adaptive fuzzy PID controller for BLSRM drive system.

Building Integrated Mobile Robots for Soccer Competition

Robot soccer competition provides an excellent opportunity for robotics research. We have built a soccer robot system to participate in internal and oversea matches. Firstly, we propose a new learning control scheme adaptive PID learning controller. It means to overcome the drawbacks of the conventional PID type control methods. Secondly, we introduce our vision recognition algorithm. It remarkably increases the speed of recognition. Finally, we refer the communication system. We adopt bulletin board system to prevent communication confusion.

Dynamic Control of Electric Output Characteristics of Proton Exchange Membrane Fuel Cell System

This paper discusses dynamic characteristics of proton exchange membrane fuel cell (PEMFC) under rapid fluctuation of power demand. Wavelet neural network is adopted in the identification of the characteristic curve to predict the voltage. The system control scheme of the voltage and power is introduced. The corresponding schemes for voltage and power control are studied. MATLAB is used to simulate the control system. The results reveal that the adopted control schemes can produce expected effects. Corresponding anti-disturbance and robustness simulation are also carried out. The simulation results show that the implemented control schemes have better robustness and adaptability.

Model-based adaptive controller for personalized ventilation and thermal comfort in naturally ventilated spaces

This work develops a standalone autonomously controlled personalized ventilation(PV)unit in a naturally ventilated(NV)office space to maintain acceptable thermal comfort{TQ under steady and transient indoor conditions and activity levels.The NV-PV proportional integral derivative(PID)controller adjusts the PV supply temperature(7spv)at the occupant set flow rate(Qspv)based on predicted TC using a regression model.The target TC level that the controller attains at all times is between 0(neutral)and 1(slightly comfortable).Process transfer functions were developed and then used to find the adaptive PID tuning coefficients using the Internal Model Control(IMC)method.The controller was tested in a case study at indoor temperature range of 25 to 33℃ with relative humidity range of 55%and 80%.It was shown that the NV-PV controller adjusted Tspv to maintain acceptable TC under transients of indoor conditions and metabolic rates.

Convergence performance comparisons of PID, MRAC, and PID ＋ MRAC hybrid controller

This study proposes a hybrid controller by combining a proportional-integral-derivative （PID） control and a model reference adaptive control （MRAC）, which named as PID ＋ MRAC controller. The convergence performances of the PID control, MRAC, and hybrid PID ＋ MRAC are also compared. Through the simulation in Matlab, the results show that the convergence speed and performance of the MRAC and the PID ＋MRAC controller are better than those of the PID controller. In addition, the convergence performance of the hybrid control is better than that of the MRAC control.

Modeling and test on height adjustment system of electrically-controlled air suspension for agricultural vehicles

To reduce the damages of pavement,vehicle components and agricultural product during transportation,an electric control air suspension height adjustment system of agricultural transport vehicle was studied by means of simulation and bench test.For the oscillation phenomenon of vehicle height in driving process,the mathematical model of the vehicle height adjustment system was developed,and the controller of vehicle height based on single neuron adaptive PID control algorithm was designed.The control model was simulated via Matlab/Simulink,and bench test was conducted.Results show that the method is feasible and effective to solve the agricultural vehicle body height unstable phenomenon in the process of switching.Compared with other PID algorithms,the single neuron adaptive PID control in agricultural transport vehicle has shorter response time,faster response speed and more stable switching state.The stability of the designed vehicle height adjustment system and the ride comfort of agricultural transport vehicle were improved.

Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV

At present,the attitude control method of plant protection UAV is the classical PID control,but there are some imperfections in the PID control,such as the contradiction between speediness and overshoot,the weak anti-jamming ability and adaptability.The physical parameters of plant protection UAV are time-varying,and the airflow also interferes with it.The control ability of classical PID is limited,and its control parameters are fixed,and its anti-jamming ability and adaptability are not strong.Therefore,a fuzzy adaptive PID controller is proposed in this paper.Fuzzy logic control is used to optimize the control parameters of PID in order to improve the dynamic and static performance and adaptability of attitude control of plant protection UAV.In the process of research,the mathematical model of UAV is established firstly,then the fuzzy adaptive PID is designed,and then the simulation is carried out in Simulink.The simulation results show that the fuzzy adaptive PID controller has better dynamic and static control performance and adaptability than the traditional PID controller.Therefore,the proposed control method has excellent application value in the attitude of plant protection UAV.