Single PID Controller Research Articles

PID control for the water-air hybrid aquatic-aerial vehicle’s exit based on simulated annealing genetic algorithm optimization

Abstract A hybrid aquatic-aerial vehicle is a special equipment with the ability to perform amphibious operations in water and air, and its control accuracy and stability during the water outflow process are crucial for ensuring the safety and operational efficiency of the vehicle. This article first constructs the kinematic and dynamic models of the hybrid aquatic-aerial vehicle and designs the basic control strategy based on a PID controller. Given the limitations of a single PID control strategy in specific contexts, this study proposes a PID controller based on genetic algorithm optimization and introduces a simulated annealing algorithm in the optimization process of the genetic algorithm to make the adjustment of controller parameters more precise and efficient. Through simulation verification, it was found that the optimized PID controller improved the performance of the aircraft during the outflow stage, indicating that this optimization control strategy has a positive impact on improving the operational performance of the aircraft.

Delay Compensation in a Feeder-Conveyor System Using the Smith Predictor: A Case Study in an Iron Ore Processing Plant.

Conveyor belts serve as the primary mode of ore transportation in mineral processing plants. Feeders, comprised of shorter conveyors, regulate the material flow from silos to longer conveyor belts by adjusting their velocity. This velocity manipulation is facilitated by automatic controllers that gauge the material weight on the conveyor using scales. However, due to positioning constraints of these scales, a notable delay ensues between measurement and the adjustment of the feeder speed. This dead time poses a significant challenge in control design, aiming to prevent oscillations in material levels on the conveyor belt. This paper contributes in two key areas: firstly, through a simulation-based comparison of various control techniques addressing this issue across diverse scenarios; secondly, by implementing the Smith predictor solution in an operational plant and contrasting its performance with that of a single PID controller. Evaluation spans both the transient flow rate during step change setpoints and a month-long assessment. The experimental results reveal a notable increase in production by 355 t/h and a substantial reduction in flow rate oscillations on the conveyor belt, evidenced by a 55% decrease in the standard deviation.

Research on intervention PID control of VAV terminal based on LabVIEW

In recent years, the research on the control method of variable air volume (VAV) has been the focus of VAV air-conditioning research. Single terminal PID control is prone to overshoot in the system when the temperature difference is large. Aiming at the deficiency of single terminal PID control at the air conditioning terminal, a control method of coupling intervention control and PID control is proposed. This thesis used LabVIEW simulation and experiment to analyze the performance of terminal intervention PID control. According to the experiment, the change of room temperature after adding 1500W heat load in the room was recorded by thermocouple, and the step response of room temperature was obtained. The LabVIEW simulation program of PID and intervention PID control is established according to the characteristics of room step response. The reliability of LabVIEW simulation program was verified by experimental data, respectively. The intervention PID control and conventional PID control are simulated by LabVIEW simulation program under 1000W and 1500w load to compare their stability, anti-interference and robustness. The experiments and simulation results show that the control strategy using the intervention recognition algorithm nested with the PID algorithm has good dynamic performance, robustness, and adaptability.

Transition control of a tail-sitter unmanned aerial vehicle with L1 neural network adaptive control

The main task of this work is to design a control system for a small tail-sitter Unmanned Aerial Vehicle (UAV) during the transition process. Although reasonable control performance can be obtained through a well-tuned single PID or cascade PID control architecture under nominal conditions, large or fast time-varying disturbances and a wide range of changes in the equilibrium point bring nonlinear characteristics to the transition control during the transition process, which leads to control precision degradation. Meanwhile, the PID controller’s tuning method relies on engineering experiences to a certain extent and the controller parameters need to be retuned under different working conditions, which limits the rapid deployment and preliminary validation. Based on the above issues, a novel control architecture of L1 neural network adaptive control associated with PID control is proposed to improve the compensation ability during the transition process and guarantee the security transition. The L1 neural network adaptive control is revised to solve the multi-input and multi-output problem of the tail-sitter UAV system in this study. Finally, the transition characteristics of the time setting difference between the desired transition speed and the desired transition pitch angle are analyzed.

Modeling and control of rate-dependent hysteresis characteristics of piezoelectric actuators based on analog filters

The hysteresis nonlinearity and rate dependence of piezoelectric actuators seriously affect their positioning accuracy in precise positioning. Proper compensation needs to be given to improve the positioning accuracy of the actuator. In this paper, the static hysteresis characteristics are described by improving the BP neural network model based on the adaptive particle swarm optimization algorithm, and the PI model is used as the hysteresis factor by the extended space method to realize the multi-valued mapping of hysteresis nonlinearity. And input three types of voltage signals, compare and analyze the experimental data and model prediction output, and use the maximum relative error and root mean square error as the evaluation indicators to prove the accuracy of the built model. Based on the improved BP neural network model, a second-order low-pass analog filter is introduced to construct a rate-dependent hysteresis model. Comparing and analyzing the rate-dependent hysteresis model and the static hysteresis model, the experimental results show that when the signal frequency is 10 Hz, 40 Hz, and 100 Hz, the predicted output of the rate-dependent hysteresis model is compared with that of the static hysteresis model, and the root mean square error decreases by 46.3%. %, 59.6%, and 65.1%, the model can more accurately describe the hysteresis characteristics of piezoelectric actuators. Finally, a rate-dependent hysteresis inverse model is established, and the rate-dependent hysteresis inverse model is used as a feedforward combined with a single neuron network PID control to form a composite controller, and the experimental verification is carried out. The results show that the maximum error between the composite controller and the actual expectation is only 2.15%. Therefore, the established model can accurately express the rate-dependent hysteresis characteristics of piezoelectric actuators, and the use of a composite controller can effectively reduce the accuracy error caused by the hysteresis nonlinear characteristics of piezoelectric actuators.

Distributed optimization of heterogeneous UAV cluster PID controller based on machine learning

To resolve the problem of membership value and the control effect reliant on expert knowledge, a PID control approach based on an adaptive fuzzy PID-UAV attitude controller is proposed. The experimental findings demonstrate that the Unmanned Aerial Vehicle (UAV) is stable at the desired height of 2 m within 6 s after takeoff, and that altitude control can be accomplished with a single PID controller. It has been established that the control system has the advantages of high precision and ease of implementation, but the fuzzy controller has the advantages of minimal overshoot and transient response, as well as the ability to achieve precise and rapid behaviour control. When obtaining continuous square wave instructions, fuzzy PID has more obvious advantages than regular PID. The system has superior tracking and antiinterference, can achieve stable control faster, and has great dynamic and static properties.

Single loop PID controller design based on optimization algorithms for parallelly connected dc-dc converters

This paper addresses a viable single loop PID controller on the bases of optimization algorithms for parallelly connected DC-DC converters to improve current sharing, improve the systems dynamics and guarantee good steady-state performance simultaneously. Because of inconvenience and lack of accuracy of Ziegler-Nichols rule in tuning PID controller parameters, an optimized controller design strategy with the purpose of enhancing the system performance is introduced in this paper. The PID is tuned by the traditional Ziegler -Nichols technique along with three other different algorithms: Genetic algorithm, whale algorithm and grey wolf algorithm. A comparison has been established between these algorithms based on the objective function value, execution time, overshoot, settling time and current sharing. The simulation results were collected to authenticate effectiveness of the proposed techniques and to evaluate the advantages of these optimization algorithms over the traditional tuning method.

Design of Cascaded PI-Fractional Order PID Controller for Improving the Frequency Response of Hybrid Microgrid System Using Gorilla Troops Optimizer

This paper proposes a cascaded Proportional Integral-Fractional Order Proportional-Integral-Derivative (PI-FOPID) controller to improve the frequency response of a hybrid microgrid system. The optimum gains of the proposed controller are fine-tuned using Gorilla Troops Optimizer (GTO) which is a recent metaheuristic optimization algorithm. The case study is a two-area microgrid system that contains diesel generators, various renewable energy sources such as photovoltaic and wind generation systems, as well as different energy storage devices. Moreover, real wind speed and solar irradiance measurements have been collected for proper system modeling. The performance of the proposed cascaded PI-FOPID controller is compared to the single structure fractional order PID (FOPID) controller based on GTO and numerous other optimization techniques presented in the previous literature such as Genetic Algorithm and Particle Swarm Optimization. The robustness of the proposed cascaded PI-FOPID controller is investigated under different scenarios such as different step load perturbations, random load disturbances, and renewable energy sources variation. The simulation results are carried out using MATLAB/Simulink. The results show that the proposed controller provides an improvement in the maximum overshoot/undershoot and settling time of 99.8% and 75.9%, respectively, compared to other competing techniques.

Cascade Control Strategy on Servo Pneumatic System with Fuzzy Self-Adaptive System

Servo pneumatic position control has gained considerable attention due to its superiority in improving the precision of automation and mechatronic systems. This paper presents the proposed cascaded fuzzy self-adaptive with proportional, integral, and derivative (CFSAPID) control strategy in providing an accurate rod-piston displacement as desired input with stable pressure in chambers. Unlike the conventional cascade strategy, the control law of proposed CFSAPID was designed based on the Mamdani-type fuzzy approach as a dynamic tuner to each stage in the proportional, integral, and derivative (PID) controller that deals with the nonlinearity of servo pneumatics adequately. The bounder sector zone of Mamdani-type fuzzy was determined based on position and velocity states on servo pneumatic rod–piston, and the parameter was constructed to obtain the optimum gain parameters of PID in each stage. The proposed CFSAPID was verified using a pneumatic proportional valve with a double-acting cylinder dynamic model plant. Simulation and analyses were emphasized on steady-state error, difference in pressures of pneumatic cylinder’s chambers, hysteresis effect, tuning parameters convergence, performance criteria, and performance indices. The results show that the proposed CFSAPID controller was able to withstand the load disturbances, at variable input trajectory with a stable pressure in chambers at almost minimum hysteresis effect compared to the FSAPID and single PID controllers.

A Back Propagation Neural Network with Double Learning Rate for PID Controller in Phase-Shifted Full-Bridge Soft-switching Power Supply

This paper mainly focuses on the control strategy for phase-shifting full-bridge soft switching electrolytic silver power supply based on Zero Voltage Switching (ZVS) soft switching technology. Taking into consideration the low performance of traditional PID control for phase-shifting full-bridge soft-switching, this paper introduce a PID improved by Back Propagation (BP) neural network with one single learning rate which is used to calculate weights from the input layer to the hidden layer and weights from the hidden layer to the output layer. After testing, it is found that setting independent learning rate for calculation of weights from the input layer to the hidden layer and weights from the hidden layer to the output layer which will not have an adverse effect on the design of the controller. Instead, the learning rate can be set according to the respective characteristics of the weights between the two layers, which is called double learning rate BP neural network PID. The simulation results indicate that compared with the single learning rate BP neural network PID control, the double learning rate BP neural network control has higher response speed, less over-shoot, short time to enter the steady state and strong immunity.

Modelling and Validation of An Electronic Wedge Brake System with Realistic Quarter Car Model for Anti-Lock Braking System Design

With the advancement in battery and electronics technologies, soon Electric Vehicles (EV) will replace traditional vehicles as they are more efficient and environment friendly. This will require replacement of all mechanical systems in vehicles with their electrical counterparts. This study focuses on electromechanical brakes (EMB) as replacement of hydraulics brakes. Particularly a type of EMB known as Electronic Wedge Brake (EWB) which uses wedges to create self reinforcing braking force and consume less power than other EMBs. Detailed mathematical model of an EWB system is presented which provides braking force and torque to the disk brake. A Quarter Car Model (QCM) with realistic parameter values and aerodynamic deceleration is modelled to validate the EWB system. The system is validated for different road conditions and anti-lock braking system (ABS) is demonstrated for snowy road using a single PID controller. The results validate the brake and car model and a need for cascaded control strategy to implement ABS is established.

A novel decoupling control method based on neural network for EV’s Driving PMSM

Permanent Magnet Synchronous Motor (PMSM) is often used in Electrified Vehicle (EV), while its Id (direct-axis current) and Iq (quadrature-axis current) are coupled. Traditional FOC (Field Oriented Control) method can’t get an accurate decoupling control of them when the motor speed changed with the vehicle’s driving condition, especially at high speed. This coupling issue leads an unstable torque output. And further, it deteriorates the NVH (noise vibration and harshness) performance of EV. This paper focuses on the decoupling solution and puts forward a new control strategy which combines the neural network control idea with FOC method. The novel method gives a neural network controller based on four single neuron PID controllers, which its function is to realize the d-q axis interacted adjustment and decoupling. Four single neurons PID controllers achieve the negative feedback control of Id to Ud (direct-axis voltage), Iq to Ud, Iq to Uq (quadrature-axis voltage), and Id to Uq respectively. Creatively, it takes PMSM speed as one of the neuron inputs to adjust the feedback weight of Id and Iq dynamically. A comparing simulation which is set up in the Simulink platform is given in this paper. Simulation results show that this method gives a good self-adaptiveness and decouples the influence of Id and Iq, as well as improve the motor control quality at high speed.

Type-2 Fuzzy Logic Controllers Made Even Simpler: From Design to Deployment for UAVs

This paper aims to provide a clear explanation of the role of the footprint of uncertainty (FOU) parameters on the control signal generation and, thus, to increase the interpretability of specially structured interval type-2 (IT2) fuzzy logic controllers, namely single input IT2 fuzzy PID (SI-IT2-FPID) controller. In this context, we extend the analysis performed for SI-IT2-FPID controllers by providing the effect of the FOU parameters on control surface (CS) generation. We show that, by only adjusting a single parameter that shapes the FOU, it is possible to generate commonly employed CSs without a requirement of an optimization assistance. In order to validate our theoretical analysis, we present comparative real world quadcopter flight tests. The real-time experimental results show that the SI-IT2-FPID controller can achieve better control performance in the presence of uncertainties and strong wind conditions when compared to its type-1 and conventional counterparts. We believe that the results of this study will open the doors to a wider use of SI-IT2-FPID controllers in real world control applications as the proposed structure is easy to design and feasible to deploy, especially in real-time control systems.

Price based fuzzy automatic generation control for Indian tariff system

In order to maintain grid discipline and bring about more responsibility and accountability among the participants in the system, the availability based tariff (ABT) mechanism was introduced in Indian power sector. Though there is a considerable improvement, it is further required to maintain the system with greater frequency stability and reduced fuel cost for power generation. This paper presents a new control scheme for price based automatic generation control (AGC) to minimize the unscheduled interchange (UI) charge through nullifying the deviations in frequency and to maintain the generation at various units economically following a disturbance. This scheme also gives the system operator flexibility to schedule the generation in a desired manner. A two input single output Mamdani type fuzzy PID (FPID) controller is used in the proposed scheme to improve the system dynamic response. The proposed scheme is tested on a single area system with four generating units and is compared with an existing control scheme and found effective. The results indicate that the UI charges are considerably minimized by maintaining frequency at normal value and the load perturbations can be adjusted among the generators in a desired manner following a disturbance.

Launch Coordination Control Based on Twin-Clutch Torque Distribution for DCT Vehicle

For Dual Clutch Transmission (DCT) vehicle, this study provides a launch control method that coordinates the transmission torque of twin clutches. The launching process is divided into 2 stages. For the first stage, the total torque is obtained by using Model Predictive Control (MPC) method, and total torque is distributed to two clutches based on principle of equal slipping work. For the second stage, a torque distribution rule is given to allow launching clutch engaged and separating clutch disengaged. Then, stability of MPC controller is discussed. Compared with single clutch launch PID controller, the simulation results show that the proposed MPC launch controller could greatly shorten the time of launching process, decrease slipping work, avoid possible power coupling and power cycling.

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

Multiple Models Fuzzy PID Controller Design in Ship Power System

The ship power system is a complex system with time varying, nonlinear and strong coupling characters. The traditional PID controller is difficult to meet the requirements of the system.In this paper, a multiple models fuzzy PID controller is designed. Considering the different operating conditions of on-land ship power system, two excitation regulation model which respectively corresponds to a fuzzy PID controller are set up. Each time, based on the input operation condition tested, only one fuzzy PID Controller is chosen to improve the control precision. At the end, simulation results show that multiple model fuzzy PID controller is superior to the single fuzzy PID controller, which effectively improves the transient response of the system, the steady state accuracy and robustness, having good prospects for engineering applications.

The Research of a Piezoelectric Valve Positioner Control Algorithm

The dynamic characteristic of smart valve positioner is nonlinear in its operational range, while the traditional five-step method or a single switch PID control algorithm could not meet a nonlinear model uncertain control system for high-speed, high-precision control. This paper presents a new control algorithm which five-step switching method with backward PWM and PID control method combines, it realize that the opening degree of the valve is fast and accurate positioning under conditions in nonlinear. The experimental result show that the control algorithm is better than the five-step control method and PID control algorithm, and the algorithm is simple, high reliability, speed control, and have certain adaptive and robustness, valve positioning accuracy to meet the control requirerments.

Multiple Models Fuzzy PID Controller Design in Air-Condition Temperature Control System

This paper deals with the study of air-condition temperature control, which is a complex system of time varying, nonlinear, imprecise model and uncertain work environment. In this paper, a multiple models fuzzy PID controller is designed. Considering the air-condition temperature adjustment system to combat aircraft and the frequently changing external environment the multiple fuzzy PID controllers are estimated according to the temperature changing area. Each time, based on temperature tested, only one fuzzy PID Controller is chosen to improve the control precision. At the end, simulation results show that multiple model fuzzy PID control is superior to the single fuzzy PID control, which effectively improve the transient response of the system, the steady state accuracy and robustness, having good prospects for engineering applications.

The Application of Adaptive Fuzzy PID in the Liquid Level Cascade Control System

As liquid level cascade system has the character the issue of non-linearity ,time variability and the overshoot,tradition PID control can not meet the requirement of precise molding system. So devise a self-_ adaptive fuzzy PID control .A self-_ adaptive fuzzy PID control combined PID to control calculate way and faintness to control the advantage of method, this text permits water tank to carry on mathematics model to design the double permit a water tank liquid misty PID string class control system. Matlab/Simulink and fuzzy logic toolbox are simulated to the single loop PID control system,the cascade control system and the cascade control system based on fuzzy self-tuning PID were simulated with Simulink. The analysis and simulation results indicate that the character issue of non-linearity ,time variability and the overshoot of the liquid level cascade control system based on a self-_ adaptive fuzzy PID controller are superior to previous of two methods.