Adaptive PID Controller Research Articles

Application of an adaptive PID controller enhanced by a differential evolution algorithm for precise control of dissolved oxygen in recirculating aquaculture systems

In aquaculture, the dissolved oxygen (DO) content of a water body is important for the growth of aquatic products and thus needs to be precisely controlled. For this purpose, through an open-loop experiment of DO aeration under different airflow rates, empirical transfer function models that can fully describe the dynamic response relationship between aeration flow rate and DO content were established. Based on these models, this study proposed a differential evolution (DE) algorithm-optimised radial basis function (RBF) neural network proportional integral derivative (PID) controller (DE-RBF-PID). The proposed controller has two optimisation parts. The first part is devoted to finding the optimal initial parameters of PID using an improved DE algorithm. The second part utilises the powerful learning ability of the RBF neural network to adjust the PID parameters online, which can not only eliminate overshoots, but also improve the controller adaptability. The simulation results for a typical DO nonlinear control system demonstrated the superiority of the proposed DE-RBF-PID controller over conventional PID and RBF-PID controllers. Therefore, this controller can be applied for precise tracking control of DO in complex circulating aquaculture systems. • The transfer function models of the control process for DO were established. • An adaptive PID controller enhanced by DE algorithm was proposed. • This proposed controller was used for DO control in circulating aquaculture. • This work can provide theoretical basis and accumulate experience for practical application.

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

Adaptive Fractional Order PID Controller Based MPPT for PV Connected Grid System Under Changing Weather Conditions

Adaptive Fractional Order PID Controller Based MPPT for PV Connected Grid System Under Changing Weather Conditions

Research on neuron PID- controlled simple wind tunnel system

The motion of the object in the wind tunnel is irregular. In order to control it accurately, a simple device is designed. The table tennis is blown by fan, its position in wind tunnel is measured by ultrasonic module, and its motion is controlled by PID algorithm. In order to accurately control the position and orientation of table tennis in the wind tunnel, an improved adaptive PID control algorithm is used to generate PWM wave to control the wind force of the axial flow fan. The online adjustment of P, I and D parameters is realized to improve the adaptability of the system. The test results show that the table tennis can hover at any given position or move regularly through the self-adaptive PID adjustment and the automatic adjustment of online parameters.

System Identification and Controller Design of a Novel Autonomous Underwater Vehicle

Autonomous underwater vehicle is an effective tool for humans to explore the ocean. It can be used for the monitoring of underwater structures and facilities, which puts forward more accurate and stable requirements for the system operation of the autonomous underwater vehicle. This paper studies the system and structural design, including the parameter identification design and control system design, of a novel autonomous underwater vehicle called “Arctic AUV”. The dynamic mathematical model of the “Arctic AUV” was established, and the system parameter identification method based on the multi-sensor least squares centralized fusion algorithm was proposed. The simplification of the mathematical model of the robot was theoretically derived, and the online parameter identification and motion control were combined, so that the robot could cope with the influence of the arctic water velocity and external turbulence. Based on the hybrid control scheme of adaptive PID and predictive control, the accurate motion control of the “Arctic AUV” was realized. A prototype of “Arctic AUV” was developed, and system parameter identification experiments were carried out in indoor pool water. Hybrid adaptive and predictive control experiments were also carried out. The validity of the parametric design method in this paper was verified, and by comparative experiment, the effect of the control method proposed in this paper was better than the traditional method.

Adaptive Type-2 Fuzzy PID LFC for an Interconnected Power System Considering Input Time-Delay

This paper addresses the designing of an adaptive type-2 fuzzy PID (T2FPID) controller for load frequency control (LFC) of an interconnected nonlinear power system considering input time-delays. The input delay, which can commonly be raised by failure in the communication links and actuator operating lag may lead to control system instability. In the proposed adaptive T2FPID controller, output scaling factors have been chosen judiciously (according to a stable tuning algorithm) and thus improves the stability and performance of the controlled system. To perform this, the stability of the proposed method has been examined using the Lyapunov–Krasovskii (L-K) theorem, and the adaptation laws of the output scaling factors are derived. The proposed adaptation laws for the output scaling factors are simple and need a little knowledge about the structure of the power system. To demonstrate the efficiency of the proposed adaptive T2FPID controller, a comparison to the non-adaptive T2FPID and type-1 FPID (T1FPID) controllers is given for a two-area interconnected nonlinear power system with input time-delays. The obtained simulation results prove that the performance of the proposed T2FPID controller is superior compared to the other controllers in the presence of time-varying input delays, nonlinearities, and uncertainties.

Design of model reference adaptive–PID controller for automated portable duodopa pump in Parkinson’s disease patients

This paper focuses on the design of an automated portable Duodopa pump (PDP) for Parkinson’s disease (PD) patients, with the aim of achieving robust performance on intra-day, intra-patient and inter-patient variability under various disturbances, parameter variations and uncertainties. For achieving this objective a suitably modified Hacisalihzade’s pharmacokinetics-pharmacodynamics (PK-PD) model of Levodopa has been chosen. By using Bernoulli’s equation for pressure balance and Hagen-Poiseuille law for flow rate, a positive displacement piston type drug dispensing syringe connected to PEG-J (percutaneous endoscopic gastrostomy- Jejunal) tube is modeled under the assumption of Newtonian, incompressible and laminar flow properties. A PDP is proposed employing model reference adaptive-PID (MRA-PID) as the control strategy and the closed-loop responses are compared with a PID controller tuned by particle swam optimization (PSO-PID). In MRA-PID controller an impeccable tracking of reference model is accomplished by a parameter adaptation mechanism such as a gradient decent method named MIT rule. The overall performances of both the controllers are evaluated under two different perturbation protocols as well as sensor noise and the robust stability is analyzed theoretically for structured uncertainties in system gain and time constant by using a cohort of eight in- silico subjects with large inter-patient variability. Even though the responses from both the controllers are stable with zero steady state error and zero undershoots, the dynamic performances of proposed controller is superior and safe under unannounced meal disturbances, intra and inter patient variability.

Adaptive Control and Estimation of the Condition of a Small Unmanned Aircraft Using a Kalman Filter

The article was motivated by the design of adaptive control algorithms for the control of a a fixed wing unmanned aerial vehicle (UAV). An adaptive system is a system that, with its structure or parameters, adapts to changes in the behavior of the object and based on the knowledge of variable properties, maintains the quality of its regulatory transition. The knowledge gained on this small UAV can be applied to larger aircraft. The creation of the proposed adaptive control into the UAV consisted of the creation of a simulation model of the aircraft based on known physical laws, the properties of the aircraft and a mathematical description. An adaptive PID controller for stabilization with changing coefficients based on the airspeed of the aircraft was designed and simulated. A validated control of the mathematical model of an unmanned aircraft was designed and simulated using the methods of estimation and identification of the UAV model parameter based on measured data from flight tests. Identifying dynamic parameters is a challenging task due to several factors, such as random vibration noise, interference, and sensor measurement uncertainty. The designed adaptive UAV control provides very promising results in improving the controllability of the aircraft while reducing the effect of speed changes on the stability and controllability of the system compared to the conventional PID controller. The comparison was performed on three selected types of PID controllers. The first type had fixed coefficients for the entire range of speeds calculated using the Control toolbox in MatLab. The second type also had constant coefficients over the entire range of speeds calculated using the Naslin method. The third adaptive type of PID controller had variable coefficients based on approximate polynomials dependent on the change in flight speed. The reason for the comparison was to show an increase in margin of stability using the method of variable coefficients of the PID controller based on the change of flight speeds. The obtained results show that the proposed adaptive control algorithm is robust enough to control the movement of the aircraft in the longitudinal plane and due to the introduced process and measurement errors, while the used Kalman filter effectively eliminates these errors.

Fuzzy Adaptive PID Control of Pneumatic Position Servo System Using High-speed On/Off Valves

Fuzzy Adaptive PID Control of Pneumatic Position Servo System Using High-speed On/Off Valves

Evaluating car's ride comfort and controlling vibration of suspension system based on adaptive PID control

The vertical vibration of the vehicles not only affects the durability of parts of the vehicle and road surface but it also affects the driver’s ride comfort and health. The aim of this study is to evaluate the effect of the vertical vibration on the driver’s ride comfort and health under the vehicle different operating conditions. The adaptive PID control is then applied to improve the vehicle's ride comfort. To achieve this goal, a 2D vibration model for the cars with 5 DOF is established to simulate. The different operating conditions of the speed, road surface, load, and working time of the vehicles are respectively evaluated based on the vertical weighted r.m.s. acceleration responses of the driver’s seat and the international standard ISO 2631. The results show that the road surface condition has the greatest influence on the driver’s comfort and health. With the vehicle's suspension system controlled by the adaptive PID controller, the ride comfort of the vehicle is significantly improved under the various road surfaces. Particularly, at ISO level B, the vertical driver's seat root-mean-square acceleration value is greatly reduced by 24.99 % while the pitching vehicle body root-mean-square acceleration value is decreased by 25.10 % in comparison with the passive suspension system.

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.

Adaptive type-2 fuzzy PID controller for LFC in AC microgrid

An AC microgrid (MG) system links the distributed generation (DG) based on renewable energy resources and local electrical loads in modern power systems. Intermittent characteristics of wind and solar resources influence not only the availability of energy but also the stability of the entire AC microgrid. This matter as well makes a challenge on the load frequency control (LFC) problem in an islanded MG system, and consequently, the fixed structure controllers are unable to provide a proper performance over a wide range of operating conditions. Therefore, it is required to design a dedicated controller for LFC to ensure the capability of maintaining a generation–load balance by managing the intermittent resources. In this paper, an adaptive type-2 fuzzy PID (T2FPID) controller is suggested for LFC in an AC MG system to enforce the frequency deviation to zero by supplying the load demands. The designed T2FPID controller is a nonlinear controller that can handle the system nonlinearities and uncertainties in the MG system in a better way. Using a Lyapunov function, the stability of the system is evaluated and parameter tuning laws for the T2FPID controller are derived using this function. It has been illustrated that the adaptive parameters are bounded and the frequency deviation reaches to zero properly. The proposed adaptive T2FPID controller has been applied for LFC of a MG system, and the simulation results have been compared with those obtained by type-1 fuzzy (T1F) PI and nonadaptive fuzzy PID controllers, which is the latest research in the problem in hand. The simulation results indicate the efficiency of the proposed adaptive controllers in dealing with intermittent resources and changing in the power system parameters.

Feedback-Linearization-Based Fuel-Cell Adaptive-Control Paradigm in a Microgrid Using a Wavelet-Entrenched NeuroFuzzy Framework

The article portrays an adaptive control paradigm for the swift response of a solid-oxide fuel cell (SOFC) in a grid-connected microgrid. The control scheme is based on an adaptive feedback-linearization-embedded fully recurrent NeuroFuzzy Laguerre wavelet control (FBL-FRNF-Lag-WC) framework. The nonlinear functions of feedback linearization (FBL) are estimated using a fully recurrent NeuroFuzzy Laguerre wavelet control (FRNF-Lag-WC) architecture with a recurrent Gaussian membership function in the antecedent part and a recurrent Laguerre wavelet in the consequent part, respectively. The performance of the proposed control scheme is validated for various stability, quality, and reliability factors obtained through a simulation testbed implemented in MATLAB/Simulink. The proposed scheme is compared against adaptive NeuroFuzzy, PID, and adaptive PID (aPID) control schemes using different performance parameters for a grid-connected load over 24 h.

IWOSSA: An improved whale optimization salp swarm algorithm for solving optimization problems

In this paper, a hybrid improved whale optimization salp swarm algorithm (IWOSSA) is proposed. The main idea behind IWOSSA is to combine improved Whale Optimization Algorithm (IWOA) and Salp Swarm Algorithm (SSA). First, WOA algorithm is improved by applying exponential relationships instead of linear relationships. Then, the algorithm chooses between either IWOA or SSA depending on a specific condition. To validate the efficiency of the proposed algorithm, IWOSSA is applied to 23 different benchmark functions of different dimensions and results are compared with 8 optimization algorithms including WOA and SSA. As an application to an industrial process and to confirm the good performance of the suggested algorithm, they are applied to tune an adaptive PID controller. The PID controller is used in controlling a divided wall column. Different disturbances are applied. From the simulation results, the enhancement made by the IWOSSA is proved by means of the different performance indexes.

Mitigation of harmonics and inter-harmonics with LVRT and HVRT enhancement in grid-connected wind energy systems using genetic algorithm- optimized PWM and fuzzy adaptive PID control

The growing installed wind capacity over the last decade has led many energy regulators to define specific grid codes for wind energy generation systems connecting to the electricity grid. These requirements impose strict laws regarding the Low Voltage Ride Though (LVRT) and High Voltage Ride Though (HVRT) capabilities of wind turbines during voltage disturbances. The main aim of this paper is to propose LVRT and HVRT strategies that allow wind systems to remain connected during severe grid voltage disturbances. Power quality issues associated with harmonics and inter-harmonics are also discussed and a control scheme for the grid-side converter is proposed to make the Wind Energy Conversion System insensitive to external disturbances and parametric variations. The Selective Harmonic Elimination Pulse Width Modulation technique based on Genetic Algorithm optimization is employed to overcome over-modulation problems, reduce the amplitudes of harmonics, and thus reduce the Total Harmonic Distortion in the current and voltage waveforms. Furthermore, to compensate for the fluctuations of the wind speed due to turbulence at the blades of the turbine, a fuzzy Proportional-Integral-Derivative controller with adaptive gains is proposed to control the converter on the generator side.

Optimal Design of Excitation Systems of Synchronous Condensers for HVDC Systems in Power Grid Environment Based on Variable Universe Fuzzy Adaptive PID Controller

In a gesture to optimize the reactive power properties of synchronous condensers and improve capabilities of condensers to support the voltages of AC systems, the outer loop control of the reactive power of condensers and the outer loop control of the voltages of AC systems are introduced into the conventional design of the main excitation systems of condensers in high voltage direct current (HVDC) systems in this study. Moreover, variable universe fuzzy adaptive PID controller is proposed to serve excitation systems of condensers to improve the response ability of condensers in power grid faults and the recovery speed of condensers after power grid faults, and to reduce overvoltage of power grids generated by condensers in the process of fault recovery. To verify its experimental validation, the model of ±100 kV HVDC system containing a condenser is set up in MATLAB/Simulink. The verified results reveal that the optimal design strategy of excitation systems of synchronous condensers in the proposal can ensure rapid regulation of the voltages of AC systems arising from condensers, and reduce overcompensation of the voltages and reactive power of AC systems arising from condensers when faults recover.

A combined backstepping and adaptive fuzzy PID approach for trajectory tracking of autonomous mobile robots

A combined backstepping and adaptive fuzzy PID approach for a nonholonomic autonomous mobile robot to follow the desired path is proposed in this paper. Two adaptive fuzzy PID controllers are adopted at the dynamic control level for velocity tracking and steering control of the robot. The fuzzy PID controller consists of a PID controller which is designed by a trial-and-error approach, optimized using the cross-entropy method, and a fuzzy controller based on relational models with two inputs and three outputs. Adaptive adjustment of the PID controllers is implemented by means of the fuzzy controllers. The pose deviations of the robot when trajectory tracking will be eliminated by the backstepping control technique at the kinematic level using a kinematic model. The simulation validation results demonstrate that the proposed control system can offer good performances for the robot in terms of small distance error, rapid response, high stability, and trajectory tracking more accuracy.

Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control

An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.

Development of an Adaptive Trajectory Tracking Control of Wheeled Mobile Robot

Classical modeling and control methods applied to differential locomotion mobile robots generate mathematical equations that approximate the dynamics of the system and work relatively well when the system is linear in a specific range. However, they may have low accuracy when there are many variations of the dynamics over time or disturbances occur. To solve this problem, we used a recursive least squares (RLS) method that uses a discrete-time structure first-order autoregressive model with exogenous variable (ARX). We design and modify PID adaptive self-adjusting controllers in phase margin and pole allocation. The main contribution of this methodology is that it allows the permanent and online update of the robot model and the parameters of the adaptive self-adjusting PID controllers. In addition, a Lyapunov stability analysis technique was implemented for path and trajectory tracking control, this makes the errors generated in the positioning and orientation of the robot when performing a given task tend asymptotically to zero. The performance of the PID adaptive self-adjusting controllers is measured through the implementation of the criteria of the integral of the error, which allows to determine the controller of best performance, being in this case, the PID adaptive self-adjusting type in pole assignment, allowing the mobile robot greater precision in tracking the trajectories and paths assigned, as well as less mechanical and energy wear, due to its smooth and precise movements.

Design and Implementation of Adaptive PID and Adaptive Fuzzy Controllers for a Level Process Station

This proposed work proposes the design and real-time implementation of an adaptive fuzzy logic controller (FLC) and a proportional-integral-derivative (PID) controller for adaptive gain scheduling that can be configured for any complex industrial nonlinear application. Initially, the open-loop test of the single-input single-output (SISO) system, with nonlinearities and disturbances, is conducted to represent the mathematical model of the process around a set of equilibrium points. The adaptive controllers are then developed and deployed by using the national instruments reconfigurable input/output data acquisition device (NI RIO), NI myRIO-1900, and the control parameters are adapted in real-time corresponding to the changes in the process variable. The resulting servo and regulatory performance of the controllers are compared in MATLAB® software. The adaptive fuzzy controller is deduced to be the better controller as it can generate the desired output with quicker settling times, fewer oscillations, and negligible overshoot.