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.

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 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.

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.

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.

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.

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.

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.