Integral Derivative Controller Research Articles

Design and Development of ROSMC-Based Single-Stage Z Source Inverter for Power Quality Enhancement in Hybrid Electric Vehicle Applications

This research work focuses on the design and analysis of the single-stage reduced order sliding mode control (ROSMC)-based Z-source inverter (ZSI) using sinusoidal pulse width modulation (SPWM) technique for improving the output power quality. To enhance the ZSI output voltage, the shoot-through (ST) mode is executed. Reduced 2nd order model of ZSI is derived from original 6th order model using moment matching method to reduce the sensor requirements and complexity in controller design. The closed loop control is accomplished using cascade control technique to investigate the system’s dynamic response. A Genetic Algorithm (GA)-tuned Proportional Integral (PI) controller is used for the outer voltage control. GA-tuned PI/ROSMC-based SPWM control technique has been attempted for inner current control. Sinusoidal reference signal is generated by the inner current control loop. Internal Model Control (IMC)-tuned Proportional Integral derivative (PID) controller is used to minimise the effect of Right Half-Plane zero, and it also produces required shoot-through duty ratio (STDR). Simulation results illustrate that the ROSMC-based SPWM technique provides superior performance than GA-tuned PI-based SPWM control technique. An experimental prototype model of 2 kW ZSI, ROSMC-based SPWM control is implemented using SPARTAN-6 field programmable gate array. The simulation results are substantiated with the hardware results.

PI-D CONTROLLER BASED ON AN IMPROVED CROW SEARCH ALGORITHM FOR CANCER GROWTH TREATMENT

The number of cancer diagnoses and deaths worldwide is rising every year despite technological advancements in diagnosing and treating multiple forms of cancer. An oncolytic virus is a type of tumor-killing virus that can infect and analyze cancer cells while mostly preserving normal cells. The oncolytic Vesicular-Stomatitis Virus therapeutic's cell cycle-specific action mathematically investigated. An optimal Proportion Integral-Derivative (PI-D) controller is introduced in this paper based on a suggested Improved Crow Search Algorithm (ICSA) to enhance the outcome of oncolytic virotherapy. The control technique was tested in a computer using MATLAB simulation. The suggested ICSA is used to tune the parameters of the PI-D controller. The ICSA used the inertia factor and boundary handle mechanism in the position update equation to balance exploration and exploitation. The simulation results show that decrease in total dose, tumor cells to 30%, the tumor remain in the treatment area from day 30 onwards. Furthermore, the ICSA algorithm outperforms the CSA and PSO algorithms by 34.5497×10-6 and 15.2573 ×10-6, respectively, indicating the robustness of treatment methods that can accomplish tumor reduction through biological parameters ambiguity.

Design of a robust active fuzzy parallel distributed compensation anti-vibration controller for a hand-glove system.

Undesirable vibrations resulting from the use of vibrating hand-held tools decrease the tool performance and user productivity. In addition, prolonged exposure to the vibration can cause ergonomic injuries known as the hand-arm vibration syndrome (HVAS). Therefore, it is very important to design a vibration suppression mechanism that can isolate or suppress the vibration transmission to the users’ hands to protect them from HAVS. While viscoelastic materials in anti-vibration gloves are used as the passive control approach, an active vibration control has shown to be more effective but requires the use of sensors, actuators and controllers. In this paper, the design of a controller for an anti-vibration glove is presented. The aim is to keep the level of vibrations transferred from the tool to the hands within a healthy zone. The paper also describes the formulation of the hand-glove system’s mathematical model and the design of a fuzzy parallel distributed compensation (PDC) controller that can cater for different hand masses. The performances of the proposed controller are evaluated through simulations and the results are benchmarked with two other active vibration control techniques-proportional integral derivative (PID) controller and active force controller (AFC). The simulation results show a superior performance of the proposed controller over the benchmark controllers. The designed PDC controller is able to suppress the vibration transferred to the user’s hand 93% and 85% better than the PID controller and the AFC, respectively.

Optimal Tuning of the SMC Parameters for a Two-Link Manipulator Co-Simulation Control

This paper presents the trajectory tracking control of a two-link planar robot manipulator using MSC Adams and MATLAB co-simulation which enables the innovative virtual prototyping of the systems without any mathematical expressions. Firstly, the tracking control performance of the planar manipulator is investigated using the Sliding Mode Control (SMC) controller and the Proportional Integral Derivative (PID) controller in terms of the performance analysis. As a result, the SMC demonstrates effective control performances compared to the PID controller according to the required trajectory, settling time, and end position of the system. Then, the SMC controller parameters are determined using the different optimization methods offered as open source by MATLAB/Response Optimization Toolbox and compared to each other. In the virtual co-simulation, the trajectory tracking control performance is observed to be improved by optimizing the parameters of the SMC controller using Simplex Search (SS) method. All control results are examined and presented with graphics and international error standards.

Motion synchronization for the SHA/EMA hybrid actuation system by using an optimization algorithm

The current research develops a mathematical model and control strategies to address two major problems force fighting and precise position tracking for a hybrid actuation system composed of servo-hydraulic actuator and electro-mechanical actuator (SHA/EMA). The force fighting and desired position tracking are two essential problems of the SHA/EMA actuation system for a large civil aircraft. The trajectory-based fractional order proportional integral derivative (FOPID) control for the SHA/EMA actuation system is proposed, tuned with the help of the particle swarm optimization (PSO) technique and implemented with the support of the FOMCON toolbox in Matlab. The experiments are performed under different external aerodynamic loads that the aircraft usually experiences during flight operations. The results show that the proposed method shows better results for tracking performance, force fighting and load rejection ability.

Performance Comparison of ANFIS, FOPID-PSO and FOPID-Fuzzy Tuning Methodology for Optimizing Response of High-Performance Drilling Machine

A fractional-order proportional integral derivative (FOPID)controller has replaced the classical PID controller used in industries for process control application. FOPID is less sensitive to the change of control parameters than PID Controller, due to which better results are obtained. The FOPID provides a robust and stable system for a higher-order system as of iso- damping property. This study aims to find a stable and controlled structure by tuning the FOPID controller with the Particle Swarm Optimization (PSO) algorithm, fuzzy-based logic approach, and Adaptive Neuro-fuzzy inference system (ANFIS). The tuning of FOPID is done to overcome deficits of PID controller using the different types of optimization method to overcome large overshoot and large settling time. This paper presented useful techniques based on PSO, fuzzy logic, and ANFIS to optimize FOPID controlled high-performance drilling machines. On the analysis and comparisons of simulation findings, it is observed that the FOPID-PSO approach provides better performance over the Ziegler-Nichols (ZN)-FOPID and the above-mentioned intelligent techniques in terms of less settling time (ts=0.823sec.) & optimized peak overshoot (Mp=2.44%) for the mentioned target.

Особенности методов проведения межлабораторных сличений при измерении виброакустических показателей

External quality control in the form of interlaboratory comparisons (ILCs) is an important criterion of the testing laboratory competence. The study was aimed to summarize the approaches to developing objects for proficiency testing (OPT) based on physical simulation of acoustic noise sources, airborne ultrasound, vibration, and the practice of their use for ILC. Analysis of the OPT effectiveness based on physical simulation of factors, the test benches (TBs), was performed based on their testing and certification results, as well as on the results of appropriate ILCs. The results of using TB as OPT are considered for the following factors: acoustic noise, airborne ultrasound, and vibration. When measuring acoustic noise, TB played back the acoustic noise record with high stability. ILC involving measurement of airborne ultrasound was performed the same way, however, the frequency of the acoustic signal being reproduced was in the range of 11–22 kHz. TBs, based on a manual mechanized tool and a platform equipped with electromechanical agitator, were developed for ILC involving the measurement of local and general vibration. Stability of vibration generated was provided by means of the automated system for maintaining the set level with feedback and proportional integral derivative (PID) controller. When arranging and performing ILCs involving measurement of noise and vibration, a crucial role is played by the methods developed specifically for ILCs, allowing one to take into account all the conditions that affect the measurement results.

Antenna Azimuth Position Control Using Fractional Order PID Controller Based on Genetic Algorithm

This work shows the design procedure of fractional-order proportional–integral-derivative (FOPID) controllers for an antenna azimuth position system based on genetic algorithm optimization approach. FOPID controllers are considered as a special type of the classical PID controller in which the derivative and integral elements have orders of fractions between zero and one. Therefore, FOPID controllers comprise two additional variables ( μ and λ ) in comparison with the typical PID controller. The FOPID is designed to control the azimuth angle of the antenna. Genetic algorithm (GA) will be employed to find an optimal value for the five FOPID controller’s gains using different type of fitness function including mean square error (MSE), integral square error (ISE) and integral time square error (ITSE). The achieved results exhibit an excellent steady and transient state performance of system, for instant fast settling and rise times as well as low present of peak overshoot.

An Optimal Robust State Feedback Controller for the AVR System-Based Harris Hawks Optimization Algorithm

This paper presents a robust tracking controller for the automatic voltage regulator (AVR) system, where both system uncertainties and external disturbances are taken into account. The proposed control scheme combines optimal feedback control with robust control. The parameter gains of the proposed controller have been selected based on Lyapunov theorem to ensure the stability of the closed-loop control system. Moreover, the parameters of the feedback control term are optimized using the Harris Hawks Optimization (HHO) algorithm. The performance of the proposed controller is compared with several Proportional Integral Derivative (PID) controllers optimized with different optimization algorithms. In addition, the fractional order PID (FOPID) controller is used for comparison. The validity of the proposed control scheme is illustrated through the simulation of the AVR system with three cases of simulation have been discussed.

Comparative performance analysis of hybrid differential evolution and pattern search technique for frequency control of the electric power system

In the current situation, operation and control of power system is a greater challenge. The most significant situation in power system control is load frequency control. In the present work, a hybrid differential evolution and pattern search (hDE-PS) method has been suggested for frequency regulation of electrical power systems. Fractional-order proportional integral derivative (FOPID) controller is implemented for design and analysis purpose. The suggested control method has been applied for two electrical power systems model, i.e., 2-area diverse source power system with/without HVDC linkage and 2-area thermal system. The performances of the suggested controller have been evaluated with PID and optimal controller. The simulation results indicate that system performances are enhanced with the suggested approach for identical structure. Robustness of the suggested approach has been analyzed by variation in random load and the system parameters. The suggested method (hDE-PS tuned FOPID) is further investigated with a 2-area thermal system. The performance of the recommended approach is analyzed by equating the results with other newly available approaches, like Genetic Algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Particle Swarm Optimization (PSO), hybrid BFOA and PSO (hBFOA-PSO), multi-objective Non-dominated Sorting Genetic Algorithm (NSGA)-II and Firefly Algorithm for the similar structure.

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

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

Comparison of classical and reliable controller performances for seismic response mitigation

Natural hazards like earthquakes, high winds, and tsunami are a threat all the time for multi-story structures. The environmental forces cannot be clogged but the structures can be prevented from these natural hazards by using protective systems. The structural control can be achieved by using protective systems like the passive, active, semi-active, and hybrid protective systems; but the semi-active protective system has gained importance because of its adaptability to the active systems and reliability of the passive systems. Therefore, a semi-active protective system for the earthquake forces has been adopted in this work. Magneto-Rheological (MR) damper is used in the structure as a semi-active protective system; which is connected to the current driver and proposed controller. The Proportional Integral Derivative (PID) controller and reliable PID controller are two proposed controllers, which will actuate the MR damper and the desired force is generated to mitigate the vibration of the structural response subjected to the earthquake. PID controller and reliable PID controller are designed and tuned using Ziegler-Nichols tuning technique along with the MR damper simulated in Simulink toolbox and MATLAB to obtain the reduced vibration in a three-story benchmark structure. The earthquake is considered to be uncertain; where the proposed control algorithm works well during the presence of earthquake; this paper considers robustness to provide satisfactory resilience against this uncertainty. In this work, two different earthquakes are considered like El-Centro and Northridge earthquakes for simulation with different controllers. In this paper performances of the structure with and without two controllers are compared and results are discussed.

Enhancement of Anti-locking Brake System Performance by using Intelligent Control Technique with Different Road Conditions

Recently, the vehicle brake system equipped with anti-lock braking systems (ABS) is considered one of the most important effective safety systems. The importance of ABS, to get maintains the safety of vehicles on roads during emergency braking and it enables reliable stopping whilst maintaining the vehicle stability and ease steer-ability. Therefore, the aim of this research is to investigate the vehicle braking performance of controlled brake ABS that is designed with three types of controller and compares them, they are bang-bang, Proportional Integral Derivative (PID) and Fuzzy Logic Control (FLC) on rough dry and wet roads to control longitudinal slip. The main obstacles of controller design in automobile systems are concerned to high non-linearities of the mathematical model. 2DOF longitudinal quarter vehicle model with taking into account the rational motion of the tire is used to examine the braking performance. The tire-road interface model and braking system model are included in vehicle model. By reviewing the results, it was found that FLC method has an effective and better effect compared to two methods on the performance of brake system equipped with ABS system. It was found that vehicle stopping distance was reduced by 21.77m and 10.3m with dry and wet asphalt roads respectively compared to braking without ABS for fuzzy control at velocity 100 km/hr.

Sine-cosine-algorithm-based fractional order PID controller tuning for multivariable systems

The multivariable systems, used in several industrial processing systems, are controlled using multi-loop controllers. A novel fractional order proportional integral derivative (FOPID) controller design without decoupler for the multivariable system using an optimisation algorithm is proposed. The FOPID controller is employed in each loop of two-input-two-output (TITO) system and their parameters are tuned using sine-cosine algorithm (SCA). The proposed method eliminates the interaction of loops with optimal tuning of FOPID controller parameters. The proposed FOPID design for Wood-Berry TITO system is compared with the recent state-of-the-art literature to verify its overall efficiency. The simulation result demonstrates the superiority of the proposed controller over other controller designs for the TITO system.

Dynamic Performance Improvement of Pitch Angle Control Using PID and PIDA Controllers for Wind Generation Systems

Abstract Recently, wind energy is one of energy sources most have been devoted much awareness as result of shortage of energy. This study shows loads which are formed in the rotating shaft of wind turbine (WT). These loads cause disturbances on the blades, which leads to damage the blades. these loads can be exceeded, and the amount of productive energy can be controlled by using a good control unit that adjusts the angle of the WT blade. For the previous purpose, independent electric motor is used to control blade pitch, this process called pitch control (PC) system. This paper presents a mathematical model of PC system that is controlled by using two different controllers in MATLAB/Simulink; proportional -integral-derivative controller (PID) and proportional -integral-derivative-acceleration (PIDA). In this system three different function are used as reference pitch angle; unit step function, sinewave function and step change signal which are used on control system without any controller and it is found that output system isn’t reached to a reference value for three functions. The steady state error equal to 76% when step function is used, so the controller is needed for a system. When PID and PIDA controllers are used, the time domain specification of output is improved. The steady state error in PID and PIDA controllers is 9% and 0.014% respectively. It is found that the better time domain specification is given by PIDA. PID and PIDA controllers are tested with sinewave and step change, PIDA is also produced the best response.

Access Control in NB-IoT Networks: A Deep Reinforcement Learning Strategy

The Internet of Things (IoT) is a key enabler of the digital mutation of our society. Driven by various services and applications, Machine Type Communications (MTC) will become an integral part of our daily life, over the next few years. Meeting the ITU-T requirements, in terms of density, battery longevity, coverage, price, and supported mechanisms and functionalities, Cellular IoT, and particularly Narrowband-IoT (NB-IoT), is identified as a promising candidate to handle massive MTC accesses. However, this massive connectivity would pose a huge challenge for network operators in terms of scalability. Indeed, the connection to the network in cellular IoT passes through a random access procedure and a high concentration of IoT devices would, very quickly, lead to a bottleneck. The latter procedure needs, then, to be enhanced as the connectivity would be considerable. With this in mind, we propose, in this paper, to apply the access class barring (ACB) mechanism to regulate the number of devices competing for the access. In order to derive the blocking factor, we formulated the access problem as a Markov decision process that we were able to solve using one of the most advanced deep reinforcement learning techniques. The evaluation of the proposed access control, through simulations, shows the effectiveness of our approach compared to existing approaches such as the adaptive one and the Proportional Integral Derivative (PID) controller. Indeed, it manages to keep the proportion of access attempts close to the optimum, despite the lack of accurate information on the number of access attempts.

Self-Learning Salp Swarm Optimization Based PID Design of Doha RO Plant

In this investigation, self-learning salp swarm optimization (SLSSO) based proportional- integral-derivative (PID) controllers are proposed for a Doha reverse osmosis desalination plant. Since the Doha reverse osmosis plant (DROP) is interacting with a two-input-two-output (TITO) system, a decoupler is designed to nullify the interaction dynamics. Once the decoupler is designed properly, two PID controllers are tuned for two non-interacting loops by minimizing the integral-square-error (ISE). The ISEs for two loops are obtained in terms of alpha and beta parameters to simplify the simulation. Thus designed ISEs are minimized using SLSSO algorithm. In order to show the effectiveness of the proposed algorithm, the controller tuning is also accomplished using some state-of-the-art algorithms. Further, statistical analysis is presented to prove the effectiveness of SLSSO. In addition, the time domain specifications are presented for different test cases. The step responses are also shown for fixed and variable reference inputs for two loops. The quantitative and qualitative results presented show the effectiveness of SLSSO for the DROP system.

An Adaptive Digital Neural Network-Like-PID Control Law Design for Fuel Cell System Based on FPGA Technique

This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH2) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fuel cell system and to achieve the stability of the desired output voltage of fuel cell. The numerical simulation results (MATLAB) package along with the schematic design experimental work using Spartan-3E xc3s500e-4fg320 board with the Xilinx development tool Integrated Software Environment (ISE) version 14.7 and using Verilog hardware description language for design testing are illustrated the performance enhancement of the proposed an adaptive intelligent FPGA-PID-NN controller in terms of error voltage reduction and generating optimal value of the hydrogen partial pressure action (PH2) without oscillation in the output and no saturation state when these results are compared with other controllers.

Design of Intelligent Levelling Control System for Grader

For the problem of grader intelligent levelling control system with much uncertainty and serious nonlinearity, precise mathematical model based traditional Proportional Integral-Derivative (PID) controller cannot meet the requirements of angle control. In order to improve dynamic performances of grader intelligent levelling control system in the laser transmitter platform of laser sources positioning system based on dynamic triangle principle, the obliquity sensor to detect the angle of laser transmit platform, and angle sensor data processed based on the data fusion algorithm through federal Kalman fusion, which is compared with Kalman algorithm. And the Radial Basis Function (RBF) Neural Networks based PID control strategy is proposed for the grader level control in this paper. This method can identify the mathematical model of the grader levelling angle via the RBF neural networks, and then the PID parameters can be optimized automatically to accommodate the characteristic variation of the process. The simulation results show that the PID controller designed based on the RBF neural networks has good control performance on the steam generator level control.

Event-Triggered Fuzzy Flight Control of a Two-Degree-of-Freedom Helicopter System

In this article, the problem of flight control for a two-degree-of-freedom helicopter system is studied. Since the helicopter is a multiinput, multioutput nonlinear control system, a Takagi–Sugeno (T–S) fuzzy model is applied to approximate the system. All submodels of the new T–S fuzzy model contain constant terms due to the nonlinear characteristics of the helicopter system. In this article, sampled-data control is considered and the sampled data are transmitted to the system over a communication network. A large amount of sampled data transmitted over the network can significantly increase the computational and communication burdens for the network with a limited bandwidth. To overcome this difficulty, an event-triggered mechanism is introduced. In order to validly control the T–S fuzzy system, a fuzzy proportional integral-derivative (PID) controller is designed based on the Lyapunov method and practical stability criteria, which are obtained by using improved integral inequalities and the linear matrix inequality technique. Finally, a numerical example is given to show the effectiveness of the obtained results.