Self-adaptive PID Control Research Articles

High-precision and wide-range temperature measurement and control system of satellite-borne calibration blackbody

The accurate measurement and control of blackbody temperature can play an important role in radiometric calibration. A novel circuit suitable for the aerospace environment is designed to measure and control the temperature of the blackbody. A coarse-fine measurement method considering the wire resistance and self-heating effect of the platinum resistance is presented to calculate the resistance values of the platinum resistors. The precision of temperature measurement is improved using a line fitting algorithm with error calibration and temperature drift error compensation. A fuzzy self-adaptive PID controller is designed to improve the temperature control effect. The performances of the developed system are implemented on an FPGA/A3PE3000-FG484I controller board. The test results show that the device temperature measurement precision can reach ±0.03K for the entire temperature range 238 K to 367 K, the temperature stability is better than 0.02 K/20 s, and the temperature uniformity is better than 0.1 K.

Radial Basis Function Network Based Self-Adaptive PID Controller for Quadcopter: Through Diverse Conditions

A quadcopter is an underactuated and nonlinear system which requires a robust controller to aid in maneuvering the quadcopter during flight. A Proportional-Integral-Derivative (PID) controller is easy and suitable to implement, and its efficiency is proved in quadcopter control. However, a PID controller with fixed parameters is inadequate enough to control a quadcopter system with different inputs or perturbations. This paper proposes the development of a self-adaptive PID controller assisted by Radial Basis Function (RBF) Network, to improve the function of the PID controller and help a quadcopter to better adapt towards different inputs and situations, independently. This work contributes to introducing RBF-PID controller to adaptively fly the underactuated quadcopter through different trajectory and perturbations using simulation. By using the hidden Gaussian function to train the current input, estimate the suitable output and update the Jacobian Information during system control, the PID gains can change adaptively during flight, additionally with the help of Gradient Descent Method (GDM). The proposed method is compared to the traditional PID controller tuned using the PID Tuner App in Simulink. Different inputs are given to test the altitude, attitudes, and position tracking such as step, multistep, sine wave, circular and lemniscate trajectory. The simulated results proved the robustness of RBF-PID in enhancing the disturbance rejection capacity by 13% to 25% in the presence of perturbations (sine wave and wind gust) compared to PID controller. The proposed controller can ensure quadcopter’s flight stability through perturbations that is within the quadcopter’s limitations.

Design of Smart Control System of Wing Anti-icing

A self-adaptive neural network-based control method in aircraft anti-ice systems is introduced. To get better accuracy and stability in wing anti-ice system control, a self-adaptive PID control is applied to drive the heater groups distributed around the wing and powered by remote power distribution units. Unlike the traditional system, the type of heater command is substituted from constant frequency to vary the frequency, and the control signals are given by PID controller whose three parameters propagation, integral and derivative, are given by backpropagation neural networks. Furthermore, in the tuning process, the plant’s predictive output is used to modify the weights of neural networks. The plant’s output will also be predicted by BP neural networks, and it is a nonlinear prediction that improves the predictive accuracy. The implementation and simulation program was written by MATLAB/Simulink.

Synchronous Control Characteristics Analysis of Shield Propulsion Hydraulic System Based on Tracking Differentiator and Self-Adaptive Nonlinear PID

Aiming at the low precision problem of multi-cylinder cooperative propulsion control in different regions of shield propulsion hydraulic systems under conditions of large load changes, this paper proposes a tracking differentiator and self-adaptive nonlinear PID (TD-NPID) control method to improve the synchronous control characteristics of shield propulsion hydraulic systems. First, the working principles of shield propulsion hydraulic systems were analyzed, and a mathematical model and TD-NPID controller were developed. Then, a simulation model was developed in AMESim-MATLAB environment, and the synchronous dynamic performances of fuzzy PID control, conventional PID control, and TD-NPID control were compared and analyzed. The results demonstrated that the shield propulsion hydraulic system with TD-NPID control had better servo tracking ability and steady-state performance than the systems with fuzzy or conventional PID control, which verified the feasibility of the application of TD-NPID control for the synchronous control of shield propulsion hydraulic systems.

Self-Adaptive PID Control Based on RBF Network for Trajectory Tracking of Dual-Mass Servo System

This paper presents a state-of-the-art algorithm of self-adaptive RBF network PID control and aims at achieving the precise and fast trajectory tracking for a dual-mass servo system. To increase the robustness of servo system parameter varying and external disturbances, a classical PID algorithm with enhanced structure based on RBF neural network is proposed. Extensive simulations show that it practically validates the superiority of the proposed RBF adaptive PID controller. The experiment was simulated respectively under the external disturbance, which indicates that accurate tracking performance of the servo system with dual-mass load has been achieved and also verifies the effectiveness of self-adaptive PID control strategy.

Fuzzy self-adaptive PID control technique for driving HHO dry cell systems

When the standard operating temperatures are exceeded in hydroxy generators, production performance decreases and power consumption increases. Self-adaptive fuzzy proportional integral derivative systems have been used to prevent this situation and to maintain optimum production conditions. The Fuzzy Logic is provided to tune the optimum PID parameters by using a gain scheduling method. The tuning scheme is demonstrated by a fuzzy decision process, which consists of fuzzification, knowledge and rule base, inference and defuzzification. Results of developed fuzzy proportional integral derivative (FPID), on-off, conventional PID and fuzzy control approach are discussed and compared. The presented result shows that self-adaptive fuzzy PID controllers achieve better control performance than conventional control methods mentioned. The last but not the least, developed approach minimizes the expertise needed to apply pulse width modulation technique.

Oxygen Excess Ratio Control of PEM Fuel Cell Based on Self-adaptive Fuzzy PID

Oxygen excess ratio is closely related to the output efficiency and service life of the proton exchange membrane (PEM) fuel cell system. To simulate dynamic behaviors of air flow, a fourth-order model of PEM fuel cell system is established. Then, a self-adaptive fuzzy PID (SFPID) controller is proposed to regulate the oxygen excess ratio on-line through its adaptive characteristic. Under different current disturbances, a comparison of proposed SFPID with several control topologies such as feedforward, PID, and PID plus feedforward (PID-FF) is carried out to validate the advantages of the proposed controller. Moreover, the proposed SFPID controller is applied to track the variable oxygen excess ratio. The simulation results display that the set-point value of oxygen excess ratio can be tracked rapidly and accurately, and verify that the proposed SFPID controller has good dynamic response performance.

A study on power balance control strategies of mining variable speed magnetic coupling based on fuzzy self-adaptive PID

This paper studied fuzzy self-adaptive PID control strategies that are applicable to mining variable speed magnetic coupling based on analysis on the magnetic coupling mathematical model fitted by a large number of data simulation and calculation by combining traditional PID control strategies and fuzzy control methods and then carried out experiments for further verification. It can be proved by experimental analysis that such control strategy can not only solve multi-motor driving power balance problems effectively, but also adapt to working condition changes and revise control parameters automatically, which can provide theoretical support for project practices.

Improved fuzzy PID controller design using predictive functional control structure

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control.

Control of suspended low-gravity simulation system based on self-adaptive fuzzy PID

In this paper, an active suspended low-gravity simulation system is proposed to follow the vertical motion of the spacecraft. Firstly, working principle and mathematical model of the low-gravity simulation system are shown. In order to establish the balance process and suppress the strong position interference of the system, the idea of self-adaptive fuzzy PID control strategy is proposed. It combines the PID controller with a fuzzy controll strategy, the control system can be automatically adjusted by changing the proportional parameter, integral parameter and differential parameter of the controller in real-time. At last, we use the Simulink tools to verify the performance of the controller. The results show that the system can reach balanced state quickly without overshoot and oscillation by the method of the self-adaptive fuzzy PID, and follow the speed of 3m/s, while simulation degree of accuracy of system can reach to 95.9% or more.

The Gate Automatic Control Based on Multi-input Parameters, Time-independent and Self-adaptive Fuzzy PID

The gate control system has some features such as multiple input parameters, nonlinearity, hysteresis, time change etc., therefore it can’t achieve good results by using traditional PID. In consideration of these characteristics, based on self-adaptive fuzzy PID control, we improved it and created a new time-independent, deterministic fuzzy PID. Then by auto-fusion of multiple input parameters, we have established a multi-input parameters fusion, time-independent and self-adaptive fuzzy PID controller and used it to control the reservoir gate and water level automatically under heavy rainfall. Simulations and experiments have shown that this method can offer faster response, smaller overshoot and better static and dynamic performance.

A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm

Course control is the basis of Unmanned Surface Vehicle (USV) control system, largely determines the performance of USV. Because of some uncontrollable factors such as wind, wave and other random disturbances, the course control of an Unmanned Surface Vehicle (USV) is always difficult. In recent years, there have been some studies of adaptive course control system for USV, but they are not precise enough for engineering practice. In this paper, we propose a novel adaptive course control method based on Back-propagation Neural Network (BPNN) and Artificial Bee Colony (ABC) algorithm. We use classic PID algorithm as the main course control algorithm, and back-propagation neural network (BPNN) is also utilized to achieve more effective self-adaptive PID control. At the same time, in order to improve the convergence speed and precision of BPNN, we bring in Artificial Bee Colony (ABC) algorithm to minimize the error of system and adjust the weight of BPNN. The system has been proven in simulation that it can accurately output the rudder angle according to the input course angle and can perform better than that without ABC algorithm optimization. The error of parameters obtained by this method is within the acceptable range, which can provide reference for engineering practice.

An improved single neuron self-adaptive PID control scheme of superheated steam temperature control system

An improved single neuron self-adaptive PID control scheme of superheated steam temperature control system

Dynamics analysis and active control of a floating crane

Original scientific paper The floating cranes, which transfer payloads during offshore constructions, need meet rigorous demands for efficiency and safety in serious sea conditions while the unexpected motion of the floating crane caused by wave make it even harder. This paper analyses the dynamics of a floating crane and presents an active heave compensation system (HCS) to allow the payloads motion decoupled from the wave-induced ship motion in the vertical direction. Specifically, virtual prototyping technology is introduced for the floating crane systems to investigate the real behaviour of the HCS. A virtual mechanical model of the floating crane is created to analyse the dynamics of the floating crane. In addition, an active HCS is designed for the under actuated floating crane system. A self-adaptive PID control law based on improved genetic algorithm is proposed for the essential part of the HCS. The co-simulation and experiment results demonstrate that the payload motion due to the ship’s vertical motion can be reduced significantly in harsh sea conditions.

Modeling and control of autonomous underwater vehicle (AUV) in heading and depth attitude via self-adaptive fuzzy PID controller

This paper focuses on design of a new self-adaptive fuzzy PID controller based on nonlinear MIMO structure for an AUV. Complexity and highly coupled dynamics, time-variance, and difficulty in hydrodynamic modeling and simulation, complicates the AUV modeling process and the design of proper and acceptable controller. In this work, the comprehensive nonlinear model of AUV is derived through kinematics and dynamic equations and then its treatment in open-loop is verified. In proposed controller, the PID parameters are adjusted by Mamdani fuzzy rules. Combined adaptive methods and dual PID controllers can improve solving of the uncertainty challenge in the PID parameters and AUV modeling uncertainty. The simulation results indicate that developed control system is stable, competent, and efficient enough to control the AUV in tracking the two channels of heading and depth with stabilized speed. Obtained results show that the proposed controller is not only robust, but also gives excellent dynamic, stunning steady-state characteristics and robust stability compared with a classically tuned PID controller.

Study on Vehicle Velocity Controller for the Energy Regeneration System of the Hydraulic Hybrid Vehicle

This paper simplifies the energy regeneration-based vehicle velocity system of the hydraulic hybrid bus into a process in which the extension rod of the hydraulic cylinder drives the secondary-element variable delivery pump/motor to change its displacement. This process enables braking of the vehicle and also allows recovery of energy. Based on the mathematical model established for the system, the fuzzy self-adaptive PID controller is designed and the compared with the conventional PID controller with respect to their impact on system performance.

PID Parameter Fuzzy Self-Tuning of the Motion Control System

In the motion control system, the fuzzy self-tuning of PID parameter is the combination of fuzzy control strategy and traditional PID control, and it is an effective method to improve the control precision. The paper discussed the principle of fuzzy self-adapted PID controller and the parameter fuzzy adjustment, and it realized self-tuning online of PID parameter for the motion control system by using a fuzzy inference method, and the controller was simulated in application of motion control system under the environment of Matlab/Simulink. The simulation result indicated the self-adaptive PID controller enable the motion control system to achieve the satisfied effect.

Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length

This paper presents a new technology of real-time arc length monitoring and sag-depression prediction through arc sound signal, which is essential to realize the welding penetration control during gas tungsten arc welding of arc length. A set of automatic measurement and control system have been proposed to achieve real-time arc length control via audio sensing system. After preprocessing of arc sound signal, the piecewise linear models of arc sound signal were established under two different arc length variation 3–4 and 4–5–6 mm, analyzing the prediction errors of linear model, which were proved to be good enough for online monitoring of arc length in pulse GTAW. Based on the linear relationship between arc sound and arc length, the linear fitting model was implemented on predicting the surface height of weld pool. A segmented self-adaptive PID controller was proposed to achieve the monitoring and controlling of arc length;, the confirmatory experiments have been designed to test the control effect of arc-length monitoring based on arc sound signal.

Design and Experimental Study on Fuzzy Self-Adaptive Controller of Common-Rail Pressure for DME Engine

A fuzzy self-adaptive PID controller is developed for controlling the pressure of the common-rail of DME (Dimethyl Ether) engine, and the performance test was carried out on a pump test bench. The results showed that the lag time of pressure following was not more than 0.34ms when the set value of common rail pressure stepped under the different speed, and the fluctuation value of common-rail pressure did not exceeding 0.5MPa while the target pressure changed from 5MPa to 11MPa, and the rail pressure fluctuation decreases rapidly with the target pressure increased, and the rail pressure fluctuation value did not exceeding 0.1MPa when the target pressure reaches at 11MPa. All these results fully illustrated that the fuzzy adaptive PID controller possesses the common advantages of PID controller and fuzzy controller, and the regulator of common rail pressure has the advantages of fast response, small static error, tiny overshoot, good robustness and quite stability.

Study on Ride Comfort of Automobile Active Suspension

A half-vehicle model with 4-DOF was built up. In response to a nonlinear, time delay and uncertain system of automobile suspension, an improved self-adaptive fuzzy PID controller of the active suspension was designed. Taking SANTANA2000s suspension parameter for simulation and taking a step function as the input of simulation road, time-domain simulation in Matlab was finished. The simulation result indicates that the ride comfort was improved obviously by using the active suspension with improved self-adaptive fuzzy PID controller. The time of response reach to steady state was shortened notably. The active suspension with improved self-adaptive fuzzy PID controller is superior to the passive suspension and the simple active suspension with fuzzy controller in improving the ride comfort and maneuverability. All of these advantages are of considerable referential values in development of vehicles active suspension controller.