Optimal PID Controller Research Articles

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms.

An Optimal Quantitative PID Controller Design for Ball and Beam System

In this paper, the design of two degree of freedom controller for ball and beam system is proposed. The Quantitative Feedback Theory (QFT) constraints are used to design two PID controllers to stabilize and compensate the nonlinear system with robust stability, robust performance and more desirable time response specifications. A multi objective cost function is proposed in this work to represent the function of QFT. The Black Hole Optimization (BHO) technique is a useful method to automate the design procedure of the proposed controller. The results of simulation indicate that the suggested optimal quantitative PID controller can give a desirable performance in comparison to other controllers designed in previous work.

Fuzzy Yaw Rate and Sideslip Angle Direct Yaw Moment Control for Student Electric Racing Vehicle with Independent Motors

In this paper, a new concurrent yaw rate, sideslip angle, and longitudinal-velocity direct yaw moment control (DYC) strategy is proposed to improve the handling and stability of a rear-wheel drive student electric racing vehicle (EV) equipped with two independent motors. In order to control these three parameters concurrently, three control schemes are developed: three fuzzy controllers, three optimized PID controllers, and two fuzzy controllers for the yaw rate and sideslip angle with a PID for longitudinal velocity. The EV dynamic behavior for the different control schemes is compared by using a nonlinear model of the EV. This model consists of three main parts: vehicle dynamics, wheel dynamics, and tire dynamics. Simulations under a circular-path driving scenario show that the proposed fuzzy controllers can effectively reduce the consumed energy by 10%, track the desired speed and path, and enhance the vehicle’s behavior and stability while maneuvering by decreasing both the yaw rate and sideslip angle deviation.

Neural networks on-line optimized PID controller with wind gust rejection for a quad-rotor

Abstract In this paper a complete methodology of modeling and control of quad-rotor aircraft is exposed. In fact, a PD on-line optimized Neural Networks Approach (PD-NN) is developed and applied to control the attitude of a quad-rotor that is evolving in hostile environment with wind gust disturbances and should maintain its position despite of these troubles. Whereas PD classical controllers are dedicated for the positions, altitude and speed control. The main objective of this work is to develop a smart Self-Tuning PD controller for attitude angles control, based on neural networks capable of controlling the quad-rotor for an optimized performance thus following a desired trajectory. Many problems could arise if the quad-rotor is evolving in hostile environments presenting irregular troubles such as wind gusts modeled and applied to the overall system. The quad-rotor has to rapidly achieve tasks while guaranteeing stability and precision and must behave quickly with regards to decision making fronting turbulences. This technique offers some advantages over conventional control methods such as PD controllers. Simulation results are achieved with the use of Matlab/Simulink environment and are established on a comparative study between PD and PD-NN controllers founded on wind disturbances application. These obstacles are applied with numerous degrees of strength to test the quad-rotor comportment. Experimental results are reached with the use of the V-REP environment with which some trajectories are tracked and then applied on a BLADE Inductrix FPV+. These simulations and experimental results are acceptable and have confirmed the efficiency of the proposed PD-NN approach. In fact, this controller has fairly smaller errors than the PD controller and has an improved ability to reject troubles. Moreover, it has confirmed to be extremely vigorous and efficient fronting disturbances in the form of wind disturbances.

A Novel Optimization of Fractional Order PID Controller Using Chaotic Maps Based Atomic Search Optimization for pH Control in Continuous Stirred Tank Reactor

A Novel Optimization of Fractional Order PID Controller Using Chaotic Maps Based Atomic Search Optimization for pH Control in Continuous Stirred Tank Reactor

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.

Design of support vector machine controller for hybrid power system automatic generation control

ABSTRACT This paper describes an online adaptive tuning methodology for Automatic Generation Control (AGC) of grid-connected wind-diesel hybrid power systems using the Support Vector Machine (SVM) controller. Using the Simulink design optimization tool/MATLAB, an optimized PID controller is first designed. To acquire system uncertainty, the SVM controller is trained using the input-output data set of an optimized PID controller obtained by varying the system nominal parameters by 40%. Simulations are carried out for conventional power generating units of various control areas integrated with hybrid wind-diesel power system. Outcomes are presented in the form of frequency aberration response, deviation in tie-line power exchange, and variation in mechanical power, and the efficacy of the proposed controller is presented in view of settling time, undershoot and overshoot. The impact of the hybrid power system on the thermal generating system is noted, and the usefulness of the developed SVM controller in keeping frequency and tie-line power exchange deviations within the stipulated limits is explored. The proposed SVM controller diminishes the settling time of frequency deviation response from 19.24 seconds to 14.36 seconds, and the undershoot and overshoots are reduced from 4.42% to 2.83% and 0.85% to 0.28%, respectively, for a three area three machine power structure. Further, the stability of the power systems under study is studied using bode-plot analysis with the designed controllers. As a result, the results indicated the robustness of the proposed SVM controller in minimizing system frequency and tie-line power variations caused by generation and load mismatch, as well as enhancing system stability.

An effective Smith predictor based fractional-order PID controller design methodology for preservation of design optimality and robust control performance in practice

In this study, an optimal and robust fractional order PID (FOPID) controller design approach is suggested for Smith predictor based FOPID (SP-FOPID) control system design. This new design approach considers continued fraction expansion (CFE) based approximate models in the optimal design task of FOPID controllers, and this approach preserves the design optimality in control applications. For this purpose, an inverse controller loop shaping design methodology is performed in order to approximate the frequency response of a Bode’s ideal loop reference model, and robust performance CFE based FOPID controller models are obtained by solving a multi-objective optimisation problem via a genetic algorithm. Thus, the suggested algorithm can deal with several controller realisation concerns in the design task of FOPID controllers and overcome real-world controller performance issues related with model approximation errors and signal saturation boundaries of electronic hardware. Illustrative design examples demonstrate that the suggested design scheme can preserve design optimality and improve practical control performance in practice.

Design and Optimization of PID Controller using Various Algorithms for Micro-Robotics System

Microparticles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper attempts to provide a thorough comparison between five meta-heuristic search algorithms: Sparrow Search Algorithm (SSA), Flower Pollination Algorithm (FPA), Slime Mould Algorithm (SMA), Marine Predator Algorithm (MPA), and Multi-Verse Optimizer (MVO). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model, and LABVIEW software was used to run the experimental tests. It is observed that the MPA technique achieves the highest values of settling error for both simulation and experimental results among other control approaches, while the SSA approach reduces the settling error by 50% compared to former experiments. The results indicate that SSA is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters.

Effectual GA Optimized PID Control Strategy based MPPT Controller for Extracting Maximum Power from Photo Voltaic system

In renewable sources, especially Photo Voltaic (PV) is actively playing an important role in both autonomous and grid-connected applications. A real challenge with any PV system is its dependency on sun’s irradiance. Hence, it needs a supporting system for obtaining its maximum output power irrespective of the weather condition. The objective of this paper is to design a Genetic Algorithm (GA) tuned PID controller for a boost converter. Such design will track possible output power from the PV system. In the proposed controller, Perturb and Observe (P&O) methodology has been used for enhancing the system output in presence of varying irradiance. The advantage of discussed control strategy is that it adequately responds with the change in irradiance value and also boosts the conversion operation efficiency. The system performance has been validated both in MATLAB simulation and also in hardware implementation with 18V and 0.5A rated solar panel. The control algorithm has been implemented using low power, low-cost PIC16F877A microcontroller.
 HIGHLIGHTS
 
 An efficient MPPT controller has been developed to track the optimal MPP for PV system
 GA optimized PID control strategy has been utilized that operates effectively and enhances PV performance efficiency
 The developed control strategy has the capability to track the optimal power at all varying irradiance conditions
 
 GRAPHICAL ABSTRACT

THE ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.

Optimal PID Controller Tuning for DC Motor Speed Control Using Smell Agent Optimization Algorithm

This paper presents an optimal PID controller for speed control of DC motor using Smell Agent Optimization (SAO) Algorithm. DC Motor is an energy converter which transforms electrical energy into mechanical energy used in several industrial applications. A SAO based PID is proposed for optimal tuning of the parameters of the controller to improve speed control of the motor. The SAO uses the phenomenon of smell perception and the intuitive trailing behavior of an agent to identify the source of smell. The integral of time multiplied absolute error (ITAE) is taken as the objective function for obtaining the parameters of the PID controller. It has been observed SAO-PID controller outperforms the GA based PID controller with less settling time, rise time and overshoot. Keywords— DC motor, PID controller, rising time, settling time, smell agent optimization algorithm

An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System

In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated system (ANFIS) for satellite attitude estimation and control was developed. The controller was trained with the data provided by an optimal controller. Furthermore, a pulse modulator was used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the proposed controller in closed-loop simulation, an ANFIS observer was also used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor, and data gyro data. However, a new ANFIS system was proposed that can jointly control and estimate the system attitude. The performance of the proposed controller was compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance, and noise. The results show that the proposed controller can surpass the optimal PID controller in several aspects including time and smoothness. In addition, the ANFIS estimator was examined and the results demonstrate the high ability of this designated observer. Consequently, evaluating the performance of PID and the proposed controller revealed that the proposed controller consumed less control effort for satellite attitude estimation under noise and uncertainty.

Black-box Optimization of PID Controllers for Aircraft Maneuvering Control

Black-box Optimization of PID Controllers for Aircraft Maneuvering Control

Optimized PID Controller based on Artificial Bee Colony Algorithm for Robot Arm

Optimized PID Controller based on Artificial Bee Colony Algorithm for Robot Arm

A Study of PID Controller for Parallel Double-Linked Rotary Inverted Pendulum

One-linked Rotary Inverted Pendulum is a classical model in control automation. Many directions of development to increase the complication is operated, such as, putting more link to create two-linked, three-linked... systems. Another direction which is less popular is creating a parallel double-linked rotary inverted pendulum. In this paper, we build a PID controller through investigation the operation of system for balancing this model and use genetic algorithm to find suitable control parameters. Genetic algorithm also show its ability in optimizing PID controller under different generations. These surveys are operated in Matlab/Simulink.

Optimized Two-Level Control of Islanded Microgrids to Reduce Fluctuations

The main problem in the operation of micro-grids is controlling the voltage and frequency. The inertia of the whole grid is low, so the operation of the system is interrupted by sudden changes in load or incidence in the absence of a proper control system. In order to solve this issue, various control structures have been proposed. In this paper, an optimal distributed control strategy for coordinating multiple distributed generation instances is presented in an islanded microgrid. A secondary frequency control method is implemented in order to eliminate voltage deviation and reduce the small signal error. In this layer, an optimized PID controller is used. PID controller optimization is carried out via the Honey Badger Algorithm, and results are obtained using the MATLAB software. According to the results, inadequate adjustment of a secondary loop leads to poor and unacceptable outcomes, and the necessary power quality is not achieved. However, by using the proposed method, a proper performance of the microgrid in the face of disturbances is achieved.

Research on Temperature Control of Liposome High Pressure Homogenizer Based on Genetic Algorithm Optimization PID

To ensure quality of cosmetic liposome, the amended genetic algorithm (GA) is constructed to optimize parameters of PID controller, and proposed GA optimization PID controller is utilized to control temperature of high pressure homogenizer, Firstly, the preparation of liposome is studied, and the preparing process of cosmetic liposome is analyzed, and the effect of temperature on preparing liposome is analyzed. Secondly, the GA is improved through introducing the crossover probability and mutation probability of hyperbolic cosine adaptive GA and generating population based on chaotic theory, the improved GA is applied to optimize parameters of PID controller. Finally, a high pressure homogenizer is used as researching object to carry out temperature controlling simulation analysis, and results illustrate that established GA optimization PID controller has quicker regulation speed and higher controlling precision. The proposed controller has wide application view.

A decoupler assisted optimised controller design for the greenhouse system

This paper presents the novelty of the feedback feed-forward linearisation integrated with the optimised PID controller for the greenhouse system (GHS). The GHS is a group of multi-input multi-output (MIMO) systems with highly coupled, disturbed, and nonlinear dynamics. Using the proposed novel approach, initially, the nonlinear MIMO system is decoupled into two independent linearised single-input single-output (SISO) systems (i.e., temperature loop and humidity loop). After the decoupling, the separated loops are regulated by the genetic algorithm-based optimised PID controllers. The extensive simulation study is carried out for the stabilising and the tracking control for both the loops of the GHS in the presence of various initial conditions, and various load disturbances (e.g., canopy temperature, external temperature, external humidity, and solar radiation). Finally, the performance robustness of the proposed control structure is compared with the linear quadratic regulator (LQR) controller, conventional PID controller, and the Ziegler-Nichols tuned PID controller under diversified scenarios.

Optimal PID ø axis Control for UAV Quadrotor based on Multi-Objective PSO

In this paper, we tackle the problem of PID Control tuning for a quadrotor. This task is vital given the several applications a UAV quadrotor can perform. This paper focuses on the optimal PID controller tuning in ø axes. We propose using a Multi-objective Particle Swarm Optimization (PSO) algorithm for PID performance improvement. The proposed system is evaluated in a Parrot Mambo FPV UAV Quadrotor virtual model using MATLAB & Simulink environment. We compare four different cost functions for the multi-objective optimization approach. Those cost functions focus on improving the UAV performance based on the settling time, overshoot, steady-state error, and control effort. The evaluation system shows the best performance in combining the different factors through the Multi-objective PSO algorithm.