Conventional PID Controller Research Articles

Longitudinal and lateral stability control for autonomous vehicles in curved road scenarios with road undulation

PurposeThis research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.Design/methodology/approachIn addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.FindingsThe framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.Originality/valueThe proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

Comparative Study of PID, PD-FLC and PID-FLC for Active Magnetic Bearing

In this manuscript, a closed loop active magnetic bearing system (AMB) is proposed, dynamically modelled and linearized in form of unstable transfer function. To achieve proper bearing operation, the proposed AMB system is controlled by two separate controllers, one to control the current in the electromagnet coils by forming an inner closed loop and second to stabilize the position of the suspending object at equilibrium. To maintain the position of suspending object at equilibrium, for the proposed AMB system (considering inner closed loop as unity) a conventional PID controller, a PD-fuzzy logic controller (PD-FLC) and a PID-fuzzy logic controller (PID-FLC) is designed and their performance is observed and compared. Later, the effect of designed controllers on the complete proposed AMB system is studied with the help of control system plots and improvement among their performances is observed. The plotted step responses and calculated transient state parameters will verify that by changing the conventional PID controller to a PID-FLC could result in 48.34% improvement in overshoot, 41.52% increment in speed of response and 32.23% increment in the relative stability.

An adaptive fuzzy control technique for a high‐speed vehicular platoon experiencing communication delays

AbstractVehicle platooning is seen as an enabler to improve driving safety, traffic congestion and stress‐free driving. A platoon is a highly non‐linear system and fuzzy control is able to deal with those systems. This paper presents a pioneering solution to the complex problem of vehicle platooning. Our adaptive controller seamlessly integrates fuzzy logic with conventional PID control technology, thereby ensuring safe and efficient travel for vehicles within a platoon. The key objectives of this controller encompass the maintenance of a consistent inter‐vehicle time gap and the consideration of time‐varying parameter uncertainties and communication delays between vehicles. It provides mathematical proof of the stability of the platoon formation using the Routh–Hurwitz theorem and a set of equations that describe the platoon's overall performance. Simulation results show that the proposed controller is effective in controlling vehicle platoons, even in the presence of random communication delays, resulting in improved driving safety and reduced traffic congestion.

A fractional order PID tuning tool for automatic voltage regulator using marine predators algorithm

The fractional-order proportional-integral-derivative (FOPID) controller stands as a widely embraced choice for the task of automatic voltage regulation (AVR) when it comes to maintaining the voltage output of synchronous generators. Nevertheless, fine-tuning the FOPID controller presents a formidable challenge, mainly because it possesses five tuning gains, in contrast to the conventional PID controller, which has three gains. Consequently, this paper introduces a novel tuning tool tailored to the AVR system by utilizing the marine predators algorithm (MPA). To gauge the effectiveness of the proposed approach, two key evaluation criteria are employed: step response analysis and trajectory tracking analysis. The results of this research reveal that the MPA-FOPID controller demonstrates exceptional performance criteria, notably enhancing the AVR transient response in comparison to other FOPID controllers optimized through recent metaheuristic algorithms.

Pressure Swing Adsorption Plant for the Recovery and Production of Biohydrogen: Optimization and Control

New biofuels are in demand and necessary to address the climate problems caused by the gases generated by fossil fuels. Biohydrogen, which is a clean biofuel with great potential in terms of energy capacity, is currently impacting our world. However, to produce biohydrogen, it is necessary to implement novel processes, such as Pressure Swing Adsorption (PSA), which raise the purity of biohydrogen to 99.99% and obtain a recovery above 50% using lower energy efficiency. This paper presents a PSA plant to produce biohydrogen and obtain a biofuel meeting international criteria. It focuses on implementing controllers on the PSA plant to maintain the desired purity stable and attenuate disturbances that affect the productivity, recovery, and energy efficiency generated by the biohydrogen-producing PSA plant. Several rigorous tests were carried out to observe the purity behavior in the face of changes in trajectories and combined perturbations by considering a discrete observer-based LQR controller compared with a discrete PID control system. The PSA process controller is designed from a simplified model, evaluating its performance on the real nonlinear plant considering perturbations using specialized software. The results are compared with a conventional PID controller, giving rise to a significant contribution related to a biohydrogen purity stable (above 0.99 in molar fraction) in the presence of disturbances and achieving a recovery of 55% to 60% using an energy efficiency of 0.99% to 7.25%.

Enhanced MPPT-Based Fractional-Order PID for PV Systems Using Aquila Optimizer

This paper proposes a controller to track the maximum power point (MPP) of a photovoltaic (PV) system using a fractional-order proportional integral derivative (FOPID) controller. The employed MPPT is operated based on a dp/dv feedback approach. The designed FOPID-MPPT method includes a differentiator of order (μ) and integrator of order (λ), meaning it is an extension of the conventional PID controller. FOPID has more flexibility and achieves dynamical tuning, which leads to an efficient control system. The contribution of our paper lies is optimizing FOPID-MPPT parameters using Aquila optimizer (AO). The obtained results with the proposed AO-based FOPID-MPPT are contrasted with those acquired with moth flame optimizer (MFO). The performance of our FOPID-MPPT controller with the conventional technique perturb and observe (P&O) and the classical PID controller is analyzed. In addition, a robustness test is used to assess the performance of the FOPID-MPPT controller under load variations, providing valuable insights into its practical applicability and robustness. The simulation results clearly prove the superiority and high performance of the proposed control system to track the MPP of PV systems.

Design and optimal tuning of fraction order controller for multiple stage evaporator system

The tight control of the process parameters through appropriate tuning of controllers is an art that imperatively employed to various process industries. Most of these industries are influenced by the nonlinearity that occurred due to the input parameter variation and presence of disturbances. The aim of this work is to investigate the nonlinear dynamics of a paper industry based energy intensive unit named Multiple Stage Evaporator (MSE) in presence of different Energy Reduction Schemes. MSE is used to concentrate the weak Black Liquor (BL), a biomass based byproduct. Hence, to extract the bioenergy from the BL, the quality of the product liquor needs to be appropriately controlled. The quality of BL is measured by two process parameters, product concentration and temperature. Hence, in this work, an intelligent controller Fraction Order Proportional-Integral-Derivative controller has been studied and employed to resolve the servo and the regulatory problem occurred during the process. A state-of-art metaheuristic approach, Black Widow Optimization Algorithm has been proposed here to tune the controller parameters and compared with another optimization approaches named Water Cycle Algorithm. The simulated result demonstrates the usefulness of the proposed strategy and confirm the performance improvement for the process parameters. To enlighten the advantages of the proposed control scheme, a comparative analysis have also been performed with conventional PID, 2-DOF-PID and FOPID controllers.

A Particle Swarm Optimization tuned nonlinear PID controller with improved performance and robustness for First Order Plus Time Delay systems

The Proportional, Integral, and Derivative (PID) controller is a ubiquitous controller within industry. The conventional PID controller can struggle to provide a satisfactory response for the nonlinear systems faced by industry. In addition, conventional PID controllers have a trade-off between performance and robustness, where they cannot compensate for both without compromising stability or speed. In this paper, a novel Nonlinear gains Proportional, Integral, and Derivative (NLPID) control algorithm is proposed as a practical control strategy that shows improvements in the simultaneous set-point tracking and disturbance rejection, to control nonlinear systems. The paper shows the performance and robustness of the proposed controller for the case of a First Order Plus Time Delay (FOPTD) system, which heavily exists in industry. The Particle Swarm Optimization (PSO) algorithm is used to tune the proposed NLPID controller. The performance of the proposed NLPID controller is simulated and compared against established controllers in literature such as conventional PID, two degree of freedom PID, and Smith Predictor PID controllers in MATLAB/Simulink for an FOPTD system, with various uncertainties and disturbances. This study shows that the proposed NLPID controller maintains faster settling and rise time, with no overshoot and excellent disturbance rejection, without compromising stability or speed, and is robust against parametric, additive, and multiplicative uncertainties.

Servo and Regulatory Response of an Industrial Fluid Catalytic Cracking (FCC) Unit under Fuzzy Logic Supervisory Control

A self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented in this paper. The controller has been applied to a control study of Fluid Catalytic Cracking unit (FCCU) riser temperature, and regenerator temperature respectively. Comparison between the performance of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID (12.6%) / Fuzzy-PID (6%)); settling time (PID (80 seconds) / Fuzzy-PID (20seconds)); Integral absolute error (PID (14.29) / Fuzzy-PID (8.63)); integral square error (PID(6.713). Fuzzy-PID (4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)) . Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Integral Fuzzy Sliding Mode Controller for Hydraulic System Using Neural Network Modelling

In this paper, a hydraulic motor controller is designed with a fuzzy supported integral sliding mode algorithm. The hydraulic system used in the study was modeled using artificial neural networks. Ability of handling nonlinearity of systems makes sliding mode controller to be a good choose for this system. The integral sliding mode controller can supply the robustness the system against the uncertainties. The basic idea of the proposed control method is to use fuzzy logic for the adaptation of the integral sliding mode control switching gain. Such adjustment reduces the chattering that is the most problem of classical sliding mode control. The equivalent control is computed using the radial basis function neural network. Simulation results of the presented method were compared with conventional PID controller results. It proved that it is more efficient to control the hydraulic system with integral fuzzy sliding mode control using neural network.

Development of a Control System for Double-Pendulum Active Spray Boom Suspension Based on PSO and Fuzzy PID

During the operation of boom sprayers in the field, it is crucial to ensure that the entire boom is maintained at an optimal height relative to the ground or crop canopy. Active suspension is usually used to adjust the height. A control system for double-pendulum active suspension was developed in this paper. The control system consisted of a main control node, two distance measurement nodes, a vehicle inclination detection node, and an execution node. Communication between nodes was carried out using a CAN bus. The hardware was selected, and the interface circuits of the sensors and the actuator were designed. The transfer functions of the active suspension and electric linear actuator were established. In order to enhance the efficiency of the control system, the particle swarm optimization (PSO) algorithm was employed to optimize the initial parameters of the fuzzy PID controller. The simulation results demonstrated that the PSO-based fuzzy PID controller exhibited improvements in terms of reduced overshoot and decreased settling time when compared to conventional PID and fuzzy PID controllers. The experimental results showed that the active suspension system equipped with the control system could effectively isolate high-frequency disturbances and follow low-frequency ground undulations, meeting the operational requirements.

Novel QO-PFA governed FO-type-II fuzzy controller for LFC of thermo-electric generator based hybrid power system

The present article addresses various novel and advanced approaches to mitigate frequency oscillation issues in an integrated power system. The suggested model of the multi-source power system is basically a two-area power system, where area2 is completely a microgrid system. The diesel generator, thermo-electric generator, wind generator, solar PV system, micro-turbine and fuel cell are the basic power generating sources in the microgrid system. The microgrid also has been associated with various energy saving equipments like battery and flywheel energy storage (FES) system to improve the power quality of the system. The unpredictable wind speed variant and fluctuation in solar power incorporates oscillations in voltage and frequency profile of the interconnected power system. In challenging to above issues, this research article has designed an innovative Fractional Order-type-2 fuzzy controller (FO-T2FC) to supress unwanted errors in the frequency and voltage of the system. Further, the proposed controller parameters are selected optimally with suggesting a novel Quassi- Oppositional Path Finder Algorithm (QO-PFA) under different operatings. An analysis on relative study is exercised to validate strength of the FO-T2FC control strategy and QO-PFA algorithm. Owing to the performance analysis, it is justified that proposed QO-PFA designed FO-T2FC controller is proved to be most potential candidate and able to improve the dynamic frequency response of area1 (Δf1) by 251.74% and 418.74% as compared to Type1 fuzzy controller and conventional PID controller respectively.

Deep learning with PID residual elimination network for time-series prediction of water quality in aquaculture industry

Time-series prediction of water quality is the most critical component of water quality monitoring in the aquaculture industry. Accurate multi-step ahead prediction of water quality can provide reasonable support for production decisions and reduce breeding risks. However, because of the nonlinearity and strong coupling between water quality factors, it is difficult to obtain accurate prediction results and reduce prediction errors. For solving this problem, we innovatively propose a methodology wherein deep neural networks (DNN) coupled with a proportional–integral–derivative residual elimination network (PID-RENet) are used for time-series prediction of water quality. In the proposed method, PID-RENet is mainly composed of two parts: a conventional PID controller and a backpropagation (BP) neural network. The role of the PID controller is to calculate the control amount according to the predicted historical deviation and to correct the prediction result of the benchmark DNN model. The BP neural network is used to dynamically adjust the three parameters of the PID controller for improving its adaptive ability. To verify the effectiveness and practicability of the proposed method, a series of multi-step ahead prediction experiments were performed on a publicly available dataset and two self-constructed water quality datasets. The results for the time-series prediction of all types of datasets suggested that the error performance indicators of the model corrected by PID-RENet were all better than those of the evaluated benchmark DNN models, indicating that PID-RENet can provide more accurate predictions.

An Improved Model-Free Adaptive Nonlinear Control and Its Automatic Application

In order to enhance the performance of model-free adaptive control (MFAC) in solving the control problem caused by interference and improve the tracking speed, this paper focuses on the analysis and research of the system affected by interference using the MFAC method. This method is based on dynamic linearization technology, with system data which are represented by a full format dynamic linearization (FFDL) model that is very similar to actual industrial processes. In this work, a control law is derived by incorporating and assigning weights to both the output error and the output error rate (OER) as the performance index. Rigorous proofs are provided to establish convergence and stability. Considering the inherent complexity of actual systems, this paper also presents the MFAC-OER scheme for multiple-input–multiple-output (MIMO) systems. Furthermore, the effectiveness and practicality of the improved control strategy are evaluated through numerical arithmetic examples and control processes involving water level regulation in a circulating fluidized bed (CFB). Comparisons with conventional MFAC and PID control methods show that the enhanced control method is capable of quickly and accurately tracking the desired signal. Additionally, it exhibits superior anti-interference characteristics and is able to respond in a timely manner to changes in the operating conditions of the circulating fluidized bed system. As a result, it ensures the normal operation of the coal saver and water supply pipe without damage.

A robust PIλDµ controller for enhancing the width of the molten pool and the tracking of welding current in gas metal arc welding (GMAW) processes

ABSTRACT By utilizing advanced power electronic devices and digital control techniques in a high-speed welding power supply, it is possible to achieve self-regulation of the electrode contact tube to work-piece distance. This innovation offers a range of customizable features that can enhance and automate the gas metal arc welding (GMAW) process. The focus of this paper is on investigating the dynamic characteristics of a self-regulated consumable electrode, under step changes in the distance between the torch and the work-piece over time. The key components of arc welding processes have been examined, and a fractional PID (PIλDµ) regulator has been developed to maintain the welding current within a predetermined range during both the melting phase and the short-circuiting of the electrode wire. In this study, a weighted sum multi-objective fitness function is utilized to optimize the parameters of a fractional PID regulator using the Bacterial Foraging Optimization (BFO) algorithm. The fitness function incorporates various error criteria, such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Time Square Error (ITSE), and Integral Square Error (ISE), each with its own assigned weight. To demonstrate the effectiveness of the proposed PIλDµ controller over a conventional PID controller, numerical simulations are performed using MATLAB/SIMULINK software. The simulations also aim to provide insights into the mechanisms that govern the changes in arc voltage and current as the arc length varies, also shedding light on the underlying mechanisms involved. Based on the simulation outcomes, it can be concluded that the automatic arc welding control system utilizing the optimal PIλDµ controller is capable of delivering excellent performance.

Analysis and enhancement of MPPT technique to increase accuracy and speed in photovoltaic systems under different conditions

There has been tremendous growth in the use of renewable energy sources (RES) over the last few years. Solar systems, because of their unique properties, such as being noiseless, eco-friendly, and free of pollutants, are the most feasible source of energy among all RESs. However, a major drawback of photovoltaic (PV) systems is their low energy conversion efficiency, unstable and intermittent nature. In order to extract electrical energy from solar panels, it is necessary to connect them to the power system through DC-DC converters. As a result, maximum power point tracking (MPPT) algorithms are necessary in order to increase the amount of power harvested from solar PV systems. Accordingly, a method is presented in this paper for the design of an MPPT control approach for PV systems using fuzzy gain scheduling for proportional-integral-derivative (FGS-PID) controllers coupled with adaptive scaling factors for fuzzy input signals. The developed adaptive FGS-PID controller uses a two-level control system structure that incorporates the advantages of fuzzy logic and conventional PID control. Additionally, to fine-tune the member functions (MFs) of FGS, an integration of the fuzzy logic controller (FLC) and the modified fluid search optimization (MFSO) algorithm is employed. In order to evaluate the efficiency of the developed control systems, MATLAB software has been chosen for the simulations. The developed approach has proven highly effective in automatically regulating the triangular MFs of inputs and outputs and has shown superior performance. One of the key benefits of the proposed technique is that it enables fast and accurate tracking of the PV system's highest generated power in a short period of time.

Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems.

Quadrotors are one of the popular unmanned aerial vehicles (UAVs) due to their versatility and simple design. However, the tuning of gains for quadrotor flight controllers can be laborious, and accurately stable control of trajectories can be difficult to maintain under exogenous disturbances and uncertain system parameters. This article introduces a novel robust adaptive control synthesis methodology for a quadrotor robot's attitude and altitude stabilization. The proposed method is based on the fuzzy reinforcement learning and strictly negative imaginary (SNI) property. The first stage of our control approach is to transform a nonlinear quadrotor system into an equivalent negative-imaginary (NI) linear model by means of the feedback linearization (FL) technique. The second phase is to design a control scheme that adapts online the SNI controller gains via fuzzy Q-learning. The performance of the designed controller is compared with that of a fixed-gain SNI controller, a fuzzy-SNI controller, and a conventional PID controller in a series of numerical simulations. Furthermore, the proofs for the stability of the proposed controller and the adaptive laws are provided using the NI theorem.

DESIGN OF A VARIABLE GAIN NONLINEAR FUZZY CONTROLLER AND PERFORMANCE ENHANCEMENT DUE TO GAIN VARIATION

In this paper, variable gain nonlinear PD and PI fuzzy logic controllers are designed and the effect of the variable gain characteristic of these controllers is analyzed to show its contribution in enhancing the performance of the closed loop system over a conventional linear PID controller. Simulation results and time domain performance characteristics show how these fuzzy controllers outperform the conventional PID controller when used to control a nonlinear plant and a plant that has time delay.

Variable-structure PD controller for satellite attitude: Stability analysis and design for optimal performance

A novel variable structure controller is investigated for satellite attitude control. This novel controller does not lead to a sliding mode, which avoids the chattering problem, making it suitable for practical implementation. Detailed formulation of the controller is presented along with analytical stability analysis for a second order system. The system having this nonlinear controller is homogeneous, which leads to a similar response at large and small deviations from the equilibrium. A procedure for tuning homogeneous controllers for all initial conditions is proposed. A detailed model of the satellite attitude dynamics, actuator dynamics, and delays in the system is utilized to optimize controller gains numerically. A comparative analysis shows the superiority of the proposed controller over a conventional PID controller in terms of better transients and lower energy consumption.

Performance Evaluation of Networks Using Gain Scheduling PID Networked Control System for Nonlinear Systems

Aims: Though Networked Control Systems (NCS) have great benefits, such as remote control, low costs, and better flexibility, it also has several drawbacks such as network-induced time delay and packet loss, which may result in performance degradation and system instability in nonlinear systems. This paper proposes a gain scheduling Proportional-Integral-Differential (GS-PID) controller design to address the performance degradation issues associated with NCS.
 Study Design: TrueTime toolbox simulator in MATLAB is used for the NCS simulation and network comparison.
 Place and Duration of Study: Department of Electronic and Computer Engineering, Faculty of Nigeria, University of Nigeria, Nsukka Enugu State, Nigeria, between March 2022 and March 2023.
 Methodology: The performance of the proposed GS-PID and conventional PID controllers is compared through simulations on three different networks (Ethernet, Controlled Area Network (CAN), and switched ethernet).
 Results: The GS-PID controller outperforms the traditional PID controller with improvements in settling time and overshoot ranging from 15% to 22%.
 Conclusion: The results of the GS-PID over the conventional PID on these three networks highlight the effectiveness of the proposed approach and show potential in improving system performance.