Self-tuning Proportional-integral-derivative Controller Research Articles

Fuzzy Logic and Neural network-based self-tuned PID controller of Quadcopter

Quadcopters have been established as a viable tool for the current era of technology. Quadcopters are also known as drones or unmanned aerial vehicles. The quadcopter can be used for aerial surveys for deliveries, filming, and missions such as detecting and mapping unreachable areas, hills, forests metropolitan areas, rocks, and obstructions that can be a danger or risk to navigation for commercial and security projects. It conducts its survey mission without human operator intervention. After the mission is accomplished, data can be fetched by GPS or the Quadcopter returns to a pre-programmed location where the data can be downloaded and processed. It can lead to an improvement in the position tracking performance due to an intrinsic change in the object's weight, buoyancy, and aerodynamic forces. In addition, quadcopters must navigate the extremely dynamic airborne environment that is represented by urban, rural, metropolitan, or natural environments. In light of this, efficient dock tracking is required to attain the requisite high precision performance, resulting in less time consumption, when the system's dynamic characteristics are time-dependent or the operating conditions and vision guidance alter. The automatic identification of objects by classifying images and self-tuning the gains of a PID (Proportional + Integral + Derivative) controller has been proposed in this paper. The object recognition is based on machine learning guided vision and self-tuning PID controller based on fuzzy logic and neural network.

Application of PID and self-tuning fuzzy PID control methods in the control of non-linear magnetic levitation system

Because of the Magnetic Levitation System's (MLS) low energy consumption and little friction two factors that are deemed crucial for unstable and nonlinear systems MLS research is now being conducted in the engineering area. This article discusses the comparison of the performance of control theories used by applying advanced control theories to cope with the complexity of the structure and controllability difficulties of MLS. The control methods compared are Proportional–Integral–Derivative (PID) and Self-Tuning Fuzzy PID (STFPID) methods. These methods were developed in the MATLAB environment. The MLS model created in the MATLAB environment was subjected to the suggested control methods, and the outcomes were compared. The outcomes unequivocally demonstrate that MLS location control may make use of PID and STFPID techniques. Four criteria were used to compare the developed control approaches performances. These are the criteria; rise time, settling time, percent maximum overshoot and overshoot value. It is clearly seen in the results that the STFPID control method provides control of the MLS with greater stability than the PID control method.

A self-tuning PID controller based on analog-digital hybrid computing with a double-gate SnS2 memtransistor.

Most commercial drones utilize a traditional proportional-integral-derivative (PID) controller because of its design simplicity. However, the traditional PID controller has certain limitations in terms of optimality and robustness; it is difficult to actively adjust the PID gains under some disturbances. In this study, we demonstrated an analog-digital hybrid computing platform based on double-gate SnS2 memtransistors to implement a self-tuning/energy-efficient PID controller in drones. The customized analog circuit with memtransistors executes the PID control algorithm with low power consumption; we experimentally verified that the energy consumption of the proposed hybrid computing-based PID controller is only 63% of that of the traditional PID controller. In addition, the precise tunability of analog conductance states in the memtransistor proved to be capable of reconfiguring the performance of the PID controller, where the developed self-tuning algorithm can automatically find the optimal PID control performance.

Self Tuning PID Control of Antilock Braking System using Electronic Wedge Brake

This paper describes the design of an antilock braking system (ABS) control for a passenger vehicle that employs an electronic wedge brake (EWB). The system is based on a two-degree-of-freedom (2-DOF) vehicle dynamic traction model, with the EWB acting as the brake actuator. The developed control structure, known as the Self-Tuning PID controller, is made up of a proportional-integral-derivative (PID) controller that serves as the main feedback loop control and a fuzzy supervisory system that serves as a tuner for the PID controller gains. This control structure is generated through two structures, namely FPID and SFPID, where the difference between these two structures is based on the fuzzy input used. An ABS-based PID controller and a fuzzy fractional PID controller developed in previous works were used as the benchmark, as well as the testing method, to evaluate the effectiveness of the controller structure. According to the results of the tests, the performance of the SFPID controller is better than that of other PID and FPID controllers, being 10% and 1% faster in terms of stopping time, 8% and 1% shorter in terms of stopping distance, 9% and 1% faster in terms of settling time, and 40% and 5% more efficient in reaching the target slip, respectively.

Fuzzy self-tuning PID control of MC3 LLC resonant LED drivers

An MC3 LLC resonant light-emitting diode (LED) driver is a multi-channel constant current (MC3) output solution based on LLC resonance. Through an effective combination of multiple transformers and a DC block capacitor, this driver allows constant current outputs in multiple channels when the LED channels are unbalanced. This study considers a fuzzy self-tuning proportional integral derivative (PID) control strategy that allows for the self-tuning of PID parameters according to fuzzy inference rules designed using the relationship between the current gain and the switching frequency. Thus, when LED channels are unbalanced, the system allows a constant current output for each of the channels and controls the input voltage disturbances well. Simulation and experimental verifications are carried out, and a 20 W 4-channel constant current power supply design based on LLC resonance is obtained for the LED driver. This realizes soft-switching of the switching device, which reduces system loss and improves system efficiency. Moreover, the adjustment time of the proposed method is half that of fixed-parameter control methods, and the overshoot of the proposed method is smaller than that of fixed-parameter control methods. When compared with PID controllers with fixed parameters, the proposed method is more robust. When a system is disturbed, it recovers faster and has a smaller range of current fluctuation with the proposed method.

Self-tuning fuzzy PID controller for servo control of hard disk drive with time delay

This paper presents the implementation of conventional proportional-integral-derivative (PID) controller, fuzzy logic with proportional-derivative (FL-PD) control configuration and self-tuning fuzzy logic PID (STFL-PID) controller for servo control of hard disk drive (HDD). Comprehensive performance analyses of the conventional PID, FL-PD and STFL-PID controllers are performed, and identify the superior control technique for the HDD. It is shown that the proposed and designed control technique improves the track seeking performance. The proposed controller is used to keep the read/write (R/W) head on track i.e., to minimize the position error signal (PES).

Realization of the Sensorless Permanent Magnet Synchronous Motor Drive Control System with an Intelligent Controller

This paper presents the sensorless control algorithm for a permanent magnet synchronous motor (PMSM) drive system with the estimator and the intelligent controller. The estimator is constructed on the novel sliding mode observer (SMO) in combination with a phase-locked loop (PLL) to estimate the position and speed of the rotor. The intelligent controller is a radial basis function neural network (RBFNN)-based self-tuning PID (Proportional-Integral-Derivative) controller, applied to the velocity control loop of the PMSM drive control system to adapt strongly to dynamic characteristics during the operation with an external load. The I-f startup strategy is adopted to accelerate the motor from standstill, then switches to the sensorless mode smoothly. The control algorithm program is based on MATLAB and can be executed in simulations and experiments. The control system performance is verified on an experimental platform with various speeds and the dynamic load, in which the specified I-f startup mode and sensorless mode, inspected by tracking response and speed regulation. The simulation and experimental results demonstrate that the proposed method has worked successfully. The motor control system has smooth switching, good tracking response, and robustness against disturbance.

Robust Intelligent Self-Tuning Active Force Control of a Quadrotor With Improved Body Jerk Performance

This work presents an innovative hybrid control scheme for a quadrotor unmanned aerial vehicle (UAV) model to improve the disturbances rejection capability and body jerk performance by utilizing an active force control (AFC)-based robust intelligent control system via a simulation study. The proposed intelligent control approach incorporates a proportional-integral-derivative (PID) and an intelligent active force control (IAFC) element yielding a robust PID-IAFC scheme. A detailed mathematical model of a quadrotor system with six degrees of freedom (DOFs) was first derived using the Newton-Euler method taking into consideration the gyroscopic terms, disturbances, aerodynamics, and friction effects. In the derived model, the PID controller was first designed to stabilize the quadrotor model and achieve the required altitude and attitude motions. In addition, different types of external disturbances in the form of sinusoidal waves and repeated impulses (pulsating) were added. An AFC strategy, known as PID-AFC, was designed and incorporated into the PID controller, and was initially tuned heuristically. Then, an artificial intelligence (AI)-based method employing an iterative learning (IL) algorithm was designed and implemented into the AFC (ILAFC) to estimate the control parameters automatically while on-line. Thereafter, the performance of the ILAFC was compared to the AFC with fuzzy logic (FL) which became known as FLAFC. Also, a self-tuning (ST) PID controller was designed and employed based on the FL method to automatically tune the PID gains based on the prescribed operating and loading conditions. Moreover, a comparative study of the system performance was carried out utilizing the PID, PID-AFC, ILAFC, FLAFC, and ST-FPID-AFC schemes to analyze the system characteristics. Furthermore, the effectiveness of the AFC-based intelligent controller was investigated in connection with the body jerk performance in the presence of external disturbances. The simulated results reveal the effectiveness and robustness of the proposed control strategy based on the IAFC technique in improving the disturbance rejection capability and body jerk performance by 17% in the presence of uncertainties and external disturbances.

Research on temperature control of heating furnace with intelligent proportional integral derivate control algorithm

In view of the problems of large overshoot and large oscillation frequency in cur?rent furnace temperature control, based on the development of intelligent control theory, expert control, fuzzy control, and neural network control in intelligent control theory are combined with proportional integral derivative (PID) control. The intelligent PID control algorithm is used to carry out numerical simulation and experimental research on these several control algorithms. The results show that the adjustment effect of the intelligent PID control algorithm is significantly better than the traditional PID control algorithm. Among them, the fuzzy self-tuning PID control algorithm and the fuzzy immune PID control algorithm are feasible in the application of furnace temperature control. The neural network PID control algorithm It also has good development and application potential.

Extreme learning‐based non‐linear model predictive controller for an autonomous underwater vehicle: simulation and experimental results

In this study, an extreme learning-based non-linear model predictive controller (NMPC) is proposed for path following planning of an autonomous underwater vehicle (AUV) using horizontal way-points. The proposed controller comprises a kinematic controller and a dynamic controller. The kinematic controller is designed by using back-stepping approach whilst the dynamic controller is designed by employing the NMPC approach. The dynamics of the AUV is identified in real-time by employing an extreme learning machine (ELM) structure. In view of achieving improved performance of the ELM structure, its hidden layer parameters are optimally determined by applying Jaya optimisation algorithm. The resulting ELM model is then used to design a NMPC considering the constraint on rudder planes. The tracking performance of the proposed controller is compared with that of two recently reported control algorithms namely, $H_\infty $ H ∞ state feedback controller and inverse optimal self-tuning proportional–integral–derivative (PID) controller. The proposed controller is implemented using MATLAB and then in real-time on a prototype AUV developed in the authors’ laboratory. From both the simulation and experimental results obtained, it is observed that the proposed controller exhibits superior tracking performance compared to both $H_\infty $ H ∞ state feedback controller and inverse optimal self-tuning PID controller.

Design of an Augmented Output-Based Multiloop Self-Tuning PID Control System

Since the control performance of a proportional–integral–derivative (PID) control systems strongly depends on PID gains, lots of schemes for tuning PID gains have been proposed up to now. Recently, data-driven PID tuning schemes whose PID gains are directly determined from the closed-loop operating data attract attention. Lots of controlled objects are multi-input/multi-output systems, though almost data-driven PID tuning schemes target single-input/single-output systems. In this paper, a new design method of a multiloop self-tuning PID controller is proposed. The proposed scheme first employs a postdecoupler to remove the influence of mutual interference. The multiloop self-tuning PID controller is designed for the approximately decoupled system. The effectiveness of the control scheme is evaluated by numerical and experimental examples.

Development of a servo-hydraulic system with a self-tuning fuzzy PID controller to simulate injection molding process

This study designs an experimental system to simulate two controlling stages, namely injection and packing, in the injection molding process. During the injection stage, injection speed is the main concern while during the packing stage, packing pressure is the main concern. A servo-hydraulic system that uses servo motors and hydraulic pumps to provide energy for injection molding, which combines the advantages of a hydraulic injection molding machine (e.g., small volume but high working force) and an all-electric injection molding machine (e.g., quiet, energy efficiency, stable working performance), is investigated in the experimental system. Hence, the experimental system includes a servo motor and a pump. Because of the fast response requirements of hydraulic systems, it is necessary to apply a microcontroller unit (MCU) in the servo-hydraulic system with a proper sampling rate and a good control algorithm in order to meet the expected performance specifications. In order to determine the optimal sampling rate, MCUs with different sampling rates were applied in the experiment. Additionally, it is difficult to use a proportional–integral–derivative (PID) controller to meet the expected performance specifications for non-linear systems such as hydraulic systems. Thus, a self-tuning fuzzy PID controller is designed so as to provide the experiment system with a better response and more stable packing pressure control. The self-tuning fuzzy PID controller with a high sampling rate was applied in the experimental system in order to meet the performance requirements of a servo-hydraulic system for an injection molding machine.

SPEED CONTROL OF BLDC MOTOR WITH SEAMLESS SPEED REVERSAL CAPABILITY USING MODIFIED FUZZY GAIN SCHEDULING

Brushless Direct Current (BLDC) motors have gained popularity in recent years due to their high-power density. Many type of speed controller techniques have been developed and Proportional Integral Derivative (PID) controller has been the most widely used. However, PID’s performance deteriorates during nonlinear loads conditions. Over the past five years, controllers have been developed to overcome this limitations in BLDC speed control, however the solutions are focusing on forward motoring only. In this paper, a speed controller for BLDC with seamless speed reversal using Modified Fuzzy Gain Scheduling is proposed. The proposed controller regulates the speed using Fuzzy Gain Scheduling 49 base rules. The controller was tested for six test cases and compared to PID and Self-Tuning Fuzzy PID controller. It is found out the proposed controller yields lowest steady state error, ess of 0.025 % during step-changing speed test case. Overall, Modified Fuzzy Gain Scheduling BLDC speed controller outperforms the other two similar controllers in variable speed conditions. The controller has potential to be used as bidirectional drive in highly dynamic load conditions.

An anti-windup self-tuning fuzzy PID controller for speed control of brushless DC motor

ABSTRACTThis paper presents an anti-windup self-tuning euzzy proportional-integral-derivative (PID) controller for the speed control of brushless direct-current (BLDC) motor. The proposed controller consists of a self-tuning fuzzy PID controller and an snti-windup controller, which combines the merits of these two controllers. The anti-windup controller is designed as a fuzzy proportion controller and is switched by a logic function based on the output of proposed controller and the speed error. A detailed simulation study in MATLAB/Simulink is performed to investigate the performance of proposed controller, and the simulation experiments are conducted on different conditions. The control system parameters of the proposed controller such as overshoot, settling time and rising time are compared with that of the conventional PID controller. The simulation results show that the proposed controller can accelerate the response speed of BLDC motor, strengthen the ability of anti-windup and make motor runs smoothly, it is proven that anti-windup self-tuning fuzzy PID controller has better performance than conventional PID controller.

Intelligent Controller Design for Quad-Rotor Stabilization in Presence of Parameter Variations

The paper presents the mathematical model of a quadrotor unmanned aerial vehicle (UAV) and the design of robust Self-Tuning PID controller based on fuzzy logic, which offers several advantages over certain types of conventional control methods, specifically in dealing with highly nonlinear systems and parameter uncertainty. The proposed controller is applied to the inner and outer loop for heading and position trajectory tracking control to handle the external disturbances caused by the variation in the payload weight during the flight period. The results of the numerical simulation using gazebo physics engine simulator and real-time experiment using AR drone 2.0 test bed demonstrate the effectiveness of this intelligent control strategy which can improve the robustness of the whole system and achieve accurate trajectory tracking control, comparing it with the conventional proportional integral derivative (PID).

Self-tuning PID controller design using fuzzy logic for half car active suspension system

A car active suspension system plays an imperative role in adequately guaranteeing the stability of the car, retaining the continuous road wheel contact and improving the suspension performances. In this paper, Proportional Integral Derivative (PID), fuzzy logic and $$\hbox {H}\infty $$ controllers are used to control the car suspension system based on half car model. Moreover, a self-tuning PID controller based on fuzzy logic is developed to improve the performance of the system. The proposed structure overcomes the difficulty of having suitable responses in controlling the process in all conditions, especially when the system parameters are changing. The controllers are designed based on the mathematical model of the system. Through the proposed method, suspension working space is minimized and the best comfort of the driver is achieved. The comparison between simulation results demonstrates that by applying the presented method, the performance of the suspension system will be improved significantly.

Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.

For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

Self-tuning Control for Air Conditioning in a Room with Varying Temperature and Imperfectly Mixed Air

This study aims to control indoor temperatures in an air-conditioned room to ensure the occupant’s thermal comfort while minimizing energy consumption. In the literature, controlled simulations of air conditioning systems usually assume that the indoor air is perfectly mixed. This assumption provides little information on spatial temperature and air flow. By contrast, this study deals with imperfectly mixed air. A computational fluid dynamics method is used to model an air-conditioned room and links this model with controllers. A self-tuning controller can monitor plant changes based on recursive estimation and adjusts control parameters to meet desired performance. Therefore, this study develops self-tuning controllers to control room temperature. Disturbances of varying temperature are exerted to investigate control performance. This paper compares the performance of a self-tuning linear quadratic controller and a self-tuning proportional-integral-derivative (PID) controller. Simulation results show that both controllers track desired temperatures well. Compared with the self-tuning PID controller, the self-tuning linear quadratic controller yields less overshoot with a slower response. The proposed method in this study is validated by experimental results.

Temperature control for a vehicle climate chamber using chilled water system

The effective temperature control of a vehicle climate chamber was investigated. The temperature was regulated by electrical heater power and valve opening in the chilled water system. Cooling load and water flow rate through the adjusting valve were studied based on the distributed parameter model developed in a previous article. In order to ensure the flow capacity and increase linearity between the cooling side control output and the air temperature, two equal percentage valves were applied. The climate chamber has characteristics of large thermal inertia and non-linearity. Performance of a proportional–integral–derivative (PID) controller used in the existing system was not satisfactory, so a fuzzy self-tuning PID controller was designed. The PID gains were tuned online according to temperature error, error change rate, and air velocity using fuzzy logic. Model predictions showed that the fuzzy PID control stabilized the air temperature in a shorter time and with smaller overshoot and undershoot. The time averaged temperature error was reduced from 0.37°C to 0.09°C in the high temperature test, and that of the low temperature test was reduced from 0.42°C to 0.18°C.

Optimal Self-Tuning PID Controller Based on Low Power Consumption for a Server Fan Cooling System.

Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID) controller, combining a PID neural network (PIDNN) with fan-power-based optimization in the transient-state temperature response in the time domain, for a server fan cooling system. Because the thermal model of the cooling system is nonlinear and complex, a server mockup system simulating a 1U rack server was constructed and a fan power model was created using a third-order nonlinear curve fit to determine the cooling power consumption by the fan speed control. PIDNN with a time domain criterion is used to tune all online and optimized PID gains. The proposed controller was validated through experiments of step response when the server operated from the low to high power state. The results show that up to 14% of a server’s fan cooling power can be saved if the fan control permits a slight temperature response overshoot in the electronic components, which may provide a time-saving strategy for tuning the PID controller to control the server fan speed during low fan power consumption.