Proportional, Integral And Derivative Controller Research Articles

Research on Maximum Power Control of Direct-Drive Wave Power Generation Device Based on BP Neural Network PID Method

Ocean wave energy is a new type of clean energy. To improve the power generation and wave energy conversion efficiency of the direct-drive wave power generation system, by addressing the issue of large output errors and poor system stability commonly associated with the currently used PID (proportional, integral, and derivative) control methods, this paper proposes a maximum power control method based on BP (back propagation) neural network PID control. Combined with Kalman filtering, this method not only achieves maximum power tracking but also reduces output ripple and tracking error, thereby enhancing the system’s control quality. This study uses a permanent magnet linear generator as the power generation device, establishes a system dynamics model, and predicts the main frequency of irregular waves through the Fast Fourier Transform method. It designs a desired current tracking curve that meets the maximum power strategy. On this basis, a comparative analysis of the control accuracy and stability of three control methods is conducted. The simulation results show that the BP neural network PID control method improves power generation and exhibits better accuracy and stability.

Bio-Inspired Jumping Spider Optimization for Controller Tuning/ Parameter Estimation of an Uncertain Aerodynamic MIMO System

The practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative, heuristic simulation procedure. The convergence of the gains values to the local or global solutions occur with ease. In designing controllers for the Twin-Rotor MIMO System (TRMS), Jumping Spider Optimization Algorithm (JSOA), a novel neoteric population-based bio-inspired metaheuristic approach is used to obtain optimum values for the Proportional, Integral and Derivative (PID) controllers. With the k,p,i controller gains as the decision variables, the JSOA solution to a nonlinear multi-objective optimization problem subject to some intrinsic constraints spawned optimal values for the controllers’ variables. Counter to other algorithms (deterministic and stochastic) that get caught in local minima, JSOA evolved a solution after searchingly rummaging the entire solution search space in a vectorized fashion for an optimal value. Compared with several other versatile controllers (using GA, PSO, Pattern Search and Simulated Annealing), statistical results obtained showed JSOA technique provided a unique solution and found the gains of the PID controllers, marginally in relation to the others (optimization methods).

Prototipe Sistem Pencegah Banjir dengan Pengontrolan Pintu Air Waduk menggunakan Kontrol PID berbasis PLC & HMI

The purpose of research on this flood prevention system prototype design is to prevent flood caused by overflowing reservoir water. This system prototype uses the Modicon M340 Programmable Logic Controller as a controller and is equipped with a Human Machine Interface feature as a medium for managing and monitoring system performance virtually. This system is supported by a Water-Level Sensor to measure the water level in the reservoir and also the rate of addition of water in the reservoir. The sluice mechanism is driven by a Stepper Motor assisted by the ULN2003 Driver Module based on Proportional, Integral and Derivative (PID) control to maintain the water level in the reservoir. PID control in this system successfully runs optimally at constant KP = 3.5, KI = 2, and KD = 1, with an error result of 0,048%. This constant has the best response time compared to other constants, where rise time = 11.76 s, settling time = 13.95 s, and steady state time = 14.07 s.

Defect detection of metallic samples by electromagnetic tomography using closed-loop fuzzy PID-controlled iterative Landweber method

ABSTRACT Electromagnetic tomography (EMT) uses the mutual inductance of the coil to visualise the conductivity distribution of interesting regions. Since the conductivity of defects and metal samples are different, the metal samples with defects can be treated as binary-valued material distributions. This paper investigates the closed-loop fuzzy proportional, integral and derivative (PID)-controlled iterative Landweber method. The whole method includes fuzzy PID controller, the Landweber reconstruction method, and the Dirichlet-to-Neumann map. Specifically, the differential signal between the mutual inductance of the coil and the feedback signal is used as the input of the fuzzy PID controller. The fuzzy controller can automatically adjust three parameters ( K p , K i and K d ) of PID controller. Subsequently, the output of the PID controller can serve as the input of the Landweber algorithm to reconstruct the distribution of conductivity. Furthermore, the Dirichlet-to-Neumann map is used to calculate the mutual inductance, acting as the feedback signal based on the reconstruction conductivity distribution. Finally, both the numerical simulation and experiments are applied to verify the proposed method. The results indicate that the proposed method can reconstruct the image with a clear edge, and the average correlation coefficient can reach 0.792.

Adaptive PID Control for Autopilot Design of Small Fixed Wing UAVs

The development of a flight control system for the class of small fixed wing Unmanned Aerial Vehicle (UAV) imposes a challenge to the designers to achieve acceptable and stable performance characteristics across the defined flight envelope. This paper focuses on developing a dynamic model of the Aerosonde UAV, incorporating it with an adaptive Proportional, Integral and Derivative (PID) control system for designing both lateral and longitudinal autopilots. The PID gains for each control loop are self-tuned based on an analytical approach, making sure that the desired command value is achieved at each time interval as closely as possible. To check the robust nature of the designed control system in the presence of external wind disturbance, a Dryden wind gust model was developed, and the entire simulation results were carried out in MATLAB®/Simulink environment. The proposed adaptive PID controller proves the effectiveness and robustness of the autopilot control system.

Two-link lower limb exoskeleton model control enhancement using computed torque

Robotic technology has recently been used to help stroke patients with gait and balance rehabilitation. Rehabilitation robots such as gait trainers are designed to assist patients in systematic, repetitive training sessions to speed up their recovery from injuries. Several control algorithms are commonly used on exoskeletons, such as proportional, integral and derivative (PID) as linear control. However, linear control has several disadvantages when applied to the exoskeleton, which has the problem of uncertainties such as load and stiffness variations of the patient’s lower limb. To improve the lower limb exoskeleton for the gait trainer, the computed torque controller (CTC) is introduced as a control approach in this study. When the dynamic properties of the system are only partially known, the computed torque controller is an essential nonlinear controller. A mathematical model forms the foundation of this controller. The suggested control approach’s effectiveness is evaluated using a model or scaled-down variation of the method. The performance of the suggested calculated torque control technique is then evaluated and contrasted with that of the PID controller. Because of this, the PID controller’s steady-state error in the downward direction can reach 5.6%, but the CTC can lower it to 2.125%.

Design and implementation of PID based flow rate control using PLC

Industrial systems require efficient techniques to observe the stable operation of various industrial processes and to achieve optimal control. Considering the importance of industrial processes, Proportional Integral and Derivative (PID), Adaptive PID, and fuzzy logic are the most utilized control systems. Programmable Logic Controller (PLC) is a low-cost solution for the industrial processes requiring control and having the flexibility of graphical user interface. In this paper, an experimental study on flow rate control system for water flowing in a vessel is realized and implemented using Proportional (P), Proportional Derivative (PD), Proportional Integral (PI), and PID controllers with Programming Logic Controller (PLC). For optimal control, the constants for the PID controller are calculated based on Zeigler-Nichols (ZN) rules. ZN tuning rules can be used to find controller constants where the plant dynamics are not available. The experimental analysis is performed to validate the theoretical concepts. The achieved results and analysis demonstrate that the process variable, which is water inflow rate 4.92L/minute, is equal to the set point without any overshoot and remains controllable at every set point change.

ATTITUDE CONTROL OF THE NANOSATELLITE USING A HYBRID FUZZY ALGORITHM BY MEANS OF THE REACTION WHEELS

Numerous application fields of automation the industrial processes have demonstrated favorable outcomes with modern control algorithms that rely on artificial intelligence. Therefore, some researchers have endeavored to implement these algorithms in the attitude control of satellites by evaluating their performance in simulated scenarios. However, due to the associated cost and risks, there is a scarcity of experimental data available for testing new attitude control algorithms on a real satellite. To address this issue, simulation of satellite positioning based on the dynamic model of the satellite with reaction wheels was carried out to perform a comparative analysis of a hybrid proportional integral and derivative (PID) fuzzy control algorithm and a classical PID control algorithm, regarding the analysis of the obtained performances. The obtained results indicate that using a Hybrid Fuzzy PID control algorithm produces better performances, than the PID control algorithm.

Vibration Control and Ride Comfort Analysis of a Full Car with a Driver Model Using Big-Bang Big-Crunch Optimized FLC

The main motive of the active suspension system is to enhance the ride comfort by suppressing the vibrations induced due to road irregularities. The actuator is getting more significance in the automotive industry due to its superior ride comfort for passengers. In this study, an eight Degrees of Freedom (DOF) full car with a driver model is considered for the ride comfort analysis (ISO2631 Standard) and vibration control. The real road terrain is not uniform in nature. Hence an effective intelligent control technology is required to improve the quality of travel. Initially, a conventional Proportional Integral and Derivative (PID) controller is designed for the system. Then a Fuzzy Logic Controller (FLC) is designed for the 8 DOF vehicle model to control the unwanted vibration produced in the vertical and angular directions. The FLC is designed with suitable firing rules, membership functions, and scaling factors using MATLAB/Simulink 2019 environment. The performance of FLC is further enhanced by the Big Bang Big Crunch (B3C) optimization technique, and it can suppress vibrations when the vehicle is travelling over bumpy and imperfect random road terrain as per ISO 8608. The Root Mean Square (RMS), Frequency Weighted RMS (FWRMS), and Vibration Dose Value of seat acceleration are the performance indices to investigate the potency of B3C-tuned FLC against the PID controller, FLC and passive suspension system.

Output performance evaluation of the automatic voltage regulator system on pre-filter control technique

<p>Automatic voltage regulator (AVR) is used at each generating station or synchronous generators to maintain voltage supply at steady state or at a constant value otherwise the performance of the generator will be affected. The combination of AVR system with controllers clears fluctuation due to variation in load, speed, temperature, and power factor. This causes deviation in generator’s voltage and damage to power equipment. This work presents performance enhancement of AVR system using Proportional Integral and Derivative (PID) with pre-filter control technique. The enhanced performance was achieved by designing a nonlinear model of a synchronous generator, a PID controller and a low pass filter or pre-filter using MATLAB/Simulink model. The introduction of the proposed PID controller with LPF at load variation of 20 seconds, reduces the rise time and the peak time to 5.2975 seconds and 12.31 seconds respectively. This increases the overshoot and the settling time to 4.28 seconds and 17.60 seconds. However, the developed scheme provides a stable time response performance for various desired generator voltages considered. The performance of the proposed scheme was compared with conventional PID controlled AVR system without LPF. The proposed scheme provides more stability as indicated by the percentage overshoot, which is 4.5788% (for PID) and 4.2765% (for PID with prefilter). This has contributed to knowledge an AVR control system with better performance in terms of rise time, peak time, overshoot and settling time for stabilized generator output voltage.</p>

Optimal Pneumatic Actuator Positioning and Dynamic Stability using Prescribed Performance Control with Particle Swarm Optimization: A Simulation Study

This paper introduces an optimal control strategy for pneumatic servo systems (PSS) positioning using Finite-time Prescribed Performance Control (FT-PPC) with Particle Swarm Optimization (PSO). Pneumatic servo systems are widely used in industrial automation, as well as medical and cybernetics systems that involve robotics applications. Precision in pneumatic control is crucial not only for the sake of efficiency but also safety. The primary goal of the proposed control strategy is to optimize the convergence rate and finite time of the prescribed performance function in error transformation of the FT-PPC, as well as the Proportional, Integral and Derivative (PID) controller as the inner-loop controller for this system. The study utilizes a dynamic model of a pneumatic proportional valve with a double-acting cylinder (PPVDC) as the targeted plant and performs simulations with a multi-step input trajectory. This offline tuning method is essential for such nonlinear systems to be safely optimized, avoiding major damage to the real-time fine-tuned works on the controller. The results demonstrate that the proposed control strategy surpasses the performance of FT-PPC with a PID controller alone, significantly improving the system's performance, including suppressing overshoot and oscillation in the responses. Further validation through the actual system of PPVDC using the fine-tuned values of FT-PPC and PID with PSO is a future task and more challenging to come, as hardware constraints may vary with different environments such as temperatures.

Design and Implementation of Iterative Learning Control for an Electro-Hydraulic Servo System

In order to produce an accurate movement of double-acting hydraulic cylinder of the Electro-Hydraulic Servo System (EHSS), an Iterative Learning Controller (ILC) approach is implemented in this work. Nonlinearity and imprecision occurs in the hydraulic systems because of friction. Traditional controllers are incapable of providing effective control performance throughout the entire operating range and insufficient to handle repetitive task. To manage the repetitive task, a memory-based learning control analysis is suitable. This research focuses to construct the ILC for governing the servo spool valve, which is responsible for the hydraulic cylinder displacement. The proposed ILC consists of learning filter, learning gain and robustness filter. Proportional, Integral and Derivative (PID) controller is devised to validate the outcome of ILC. Controllers are constructed based on the system modelling. The effectiveness of the suggested controller is shown through simulation and experimental findings. ILC provides an average of 50% minimal overshoot and settles 7 sec before the PID controller is designed. Due to model uncertainty, PID controller results 0.2 sec better rise time than ILC. In ILC's architecture objective function is designed to achieve minimal overshoot and quick settling of hydraulic cylinder. So, no consideration is given to the error indices. These findings will help hydraulic stamping application, which requires the accurate displacement of piston to avoid damage of work piece. These results show that the proposed controller achieves desired outcome displacement of hydraulic cylinder.

Simulation model of ANN and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink

<p>The wireless network transmission control protocol/active queue management (TCP/AQM) is a network that was chosen as a topic for research a basis is laid to simulate the proposed network and to conduct the simulation under certain conditions. To manage the queue control protocol (TCP/AQM) was chosen. The solution for many modern systems depends on placing additional units called controllers, which work to improve the performance of the work of the systems. The current simulation system can be described according to the test cases that were conducted, where four test cases were identified with sequential steps. In this work there are two control methods by simulation and mathematical model of wireless network TCP/AQM with proportional, integral and derivative (PID) controller and neural network. Simulation is conducted for cases in order to determine the performance of each case through comparison according to appropriate criteria to determine the best. The first case is a wireless communication network system with with a traditional controller PID type. The second is a wireless communication network system, a large neural network controller. Simulations were conducted to choose the best methods among those suggested for nonlinear systems and to enhance and achieve the possibility of adopting MATLAB to perform the required simulation.</p>

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms.

Quantum behaved artificial bee colony based conventional controller for optimum dispatch

<p>Since a multi area system (MAS) is characterized by momentary overshoot, undershoot and intolerable settling time so, neutral copper conductors are replaced by multilayer zigzag graphene nano ribbon (MLGNR) interconnects that are tremendously advantageous to copper interconnects for the future transmission line conductors necessitated for economic and emission dispatch (EED) of electric supply system giving rise to reduced overshoots and settling time and greenhouse effect as well. The recent work includes combinatorial algorithm involving proportional integral and derivative controller and heuristic swarm optimization; we say it as Hybrid- particle swarm optimization (PSO) controller. The modeling of two multi area systems meant for EED is carried out by controlling the conventional proportional integral and derivative (PID) controller regulated and monitored by quantum behaved artificial bee colony (ABC) optimization based PID (QABCOPID) controller in MATLAB/Simulink platform. After the modelling and simulation of QABCOPID controller it is realized that QABCOPID is better as compared to multi span double display (MM), neural network based PID (NNPID), multi objective constriction PSO (MOCPSO) and multi objective PSO (MOPSO). The real power generation fixed by QABCOPID controller is used to estimate the combined cost and emission objectives yielding optimal solution, minimum losses and maximum efficiency of transmission line.</p>

Three‐time scale multi‐objective optimal PID control with filter design

AbstractThis paper presents the design of a multi‐objective PID (proportional, integral, and derivative) controller in three time scales for a system with load disturbances and sensor noise. The key to this design method is to divide the problem into three time scales by following a singular perturbation approach, which allows for the optimization of fewer parameters in each time scale instead of all the three PID gains at once, hence less computation and design effort. The optimization objectives are the minimization of the overshoot and peak time and the maximization of the closed‐loop system's ability to reject noise and load disturbances. The impact of the integral action and filter on the optimal results is investigated by tuning the singular perturbation parameters. The obtained results show that the performance of the closed‐loop system recovers the optimal solution, which is based on the fast subsystem, as the singular perturbation parameters get sufficiently small.

Master-Slave Synchronization of Robotic Arm using PID Controller

This paper analyzes the position control of a master-slave synchronization robotic arm driven by a D.C. motor using a PID (Proportional, Integral, and Derivative) controller with software and hardware design. This controller works to achieve the exact desired position simultaneously for the master and slave robot arm with minimal defects. The transfer function of the D.C. motor for the robotic arms used in this research is calculated with black box modelling. MATLAB Simulink block is used to test the software result. The MATLAB built-in auto-tuning method obtains Kp, Ki and Kd gain. These gains are adjusted with manual tuning to get precise angular positions for two robotic arms. This research uses Arduino Uno to act as a controller in the experiment. First, the position control of one robotic arm is tested with the same PID gain in MATLAB Simulink at different input degrees. Then, the hardware experiment of position control in one robotic arm is operated with only one PID gain at various reference degrees. Finally, the I2C communication protocol connects the master and slave robot arms. The main work that the PID controller hardware experiment with controls different level angular positions of two robotic arms.

Control of robot-assisted gait trainer using hybrid proportional integral derivative and iterative learning control

<p><span lang="EN-US">An inexpensive exoskeleton of the lower limb was designed and developed in this study. It can be used as a gait trainer for persons with lower limb problems. It plays an essential role in lower limb rehabilitation and aid for patients, and it can help them improve their physical condition. This paper proposes a hybrid controller for regulating the lower limb exoskeleton of a robot-assisted gait trainer that uses a proportional integral and derivative (PID) controller combined with an iterative learning controller (ILC). The direct current motors at the hip and knee joints are controlled by a microcontroller that uses a preset pattern for the trajectories. It can learn how to monitor a trajectory. If the trajectory or load is changed, it will be able to follow the change. The experiment showed that the PID controller had the smallest overshoot, and settling time, and was responsible for system stability. Even if there are occasional interruptions, the tracking performance improves with the ILC.</span></p>

A novel meta-heuristic algorithm based optimized load frequency controller

This work proposes a 2-degree-of-freedom proportional-integral-double-derivative (2-DOF PIDD) controller to improve frequency profiles of an IEEE 39 bus 10 generator 3 area New England interconnected power system during step load perturbations. To increase the system’s dynamic response, parallel derivative components are used in the secondary controller mechanism, and comparisons are made with proportional, integral and derivative (PID) and 2-DOF PID controllers to illustrate the advantage of the proposed controller scheme. The ideal gain values of the PID, 2-DOF PID, and 2-DOF PIDD controllers are achieved using a novel evolutionary algorithm called the intelligent water drops (IWD) algorithm, which is used for the first time for the load frequency control problem for a 3-area power system. The results proved that the proposed controller is superior to the existing controllers. Simulations are done in the MATLAB-Simulink® environment.

Optimization of rwanda power system protection in power blackouts and cascading events

Rwanda's power system security is the most important in optimization of grid frequency to prevent power blackouts caused by load disturbances and power imbalances. To manually stabilize and restore network frequency, a tuned PID (Proportional, Integral, and Derivative) controller was used, but it was inefficient and unreliable. The objective of this article is to develop a system that can be used to balance generation—demand powers during power outages by alleviating grid frequency in load disturbance cascaded events. By balancing power generation and demand, the balanced steady-state approach was proposed and developed to restore and optimize grid frequency to its normal state. This technique used PID-Power System Automatic Stabilization (PID-PSAS) technique based on load frequency control. The load disturbances of ± 20%, ± 10%, and ± 5% of a 250 MW power load were considered. MATLAB/Simulink was utilized to model and simulate the Rwanda power and controller systems. The results showed that the frequency responses of single and two area western and northern grids were reduced to {13*10}^{-5} Hz, {15*10}^{-6} Hz, and 0.251 s for overshoot, steady-state error, and settling time respectively. The proposed control system performance of 99.86% success rate was achieved and compared with the current control techniques of 70.6% performance rate. A stable frequency was observed at any disturbances and more than 300 megawatt losses were mitigated. Finally, the developed control technique rapidly stabilize the frequency and balance the generation-demand powers after 0.251 s. Future works have to focus on 0 Hz of steady state errors using cyber-physical power optimization systems.Article HighlightsThe steadiness of connected loads and power utility supplyReduction of frequency instability and stead-state valuesPrevention and mitigation of power blackouts and power interruptions