Application Of Controller Research Articles

APPLICATION OF LM 35 TEMPERATURE SENSOR FOR SEAWATER DISTILLATION

Indonesia is an archipelago, so many settlements close to the beach or sea. People living in the area are still many who lack clean water. One way to get clean water is sea water by distillation. Distillation is a liquid changes into steam and the steam is cooled back into a liquid. The process of designing and manufacturing distillation in this paper serves to make sea water into fresh water and also to stabilize the temperature during heating. Given these problems required temperature control system. Application of fuzzy logic controllers are expected to solve the problem of temperature stability in sea water distillation equipment. The sensor used is LM 35 sensors as readers seawater temperature is used as a reference to maintain a temperature in the range of 80° -100°C. Fuzzy logic rules are applied has a 9, which is obtained from the membership functions of 3 error and 3 delta error membership functions. Application of Fuzzy Logic Controller When the set point temperature of 85°C temperature response curve is obtained which has a performance controller Error Steady-state = 0.35%, Max Overshoot = 0, settling time = 103 minutes, peak time = 0, rise time = 37 minutes. With the set point of 75°C -99°C then obtained a qualified water quality water temperature of 100°C is worth to drink and has a good time efficiency for distillation temperature.

Behaviour of electric drive of roller-bit drilling rig swivel head with fuzzy control

The efficient consumption of electric power in mining is an important task in power consumption optimization. The use of high-performance drilling rigs requires special attention to the development of energy-saving electric drive for open-cut mining operations. The increase of the efficiency factor and energy performance of a drilling rig is achieved through controlling the electric drive which allows the specific resonance frequency and limiting the current and velocity amplitudes to be regulated. The main idea of the study lies in the application of fuzzy controllers in the systems of automatic control of processes and equipment modes in mining production. The use of fuzzy controllers is aimed at improving the characteristics of PI and PID controllers. The calculations and simulation of transients based on simulation models in the MatLab 7.11 Simulink software package allowed reliable analysis of modes of a swivel head electric drive operation to be carried out. In the course of simulating the transients of swivel head velocity varying with the use of a fuzzy controller, fuzzy variables including mismatch of rotation velocity, mismatch change speed, velocity setting voltage were justified. The analysis allowed for the term-sets of fuzzy variables and the membership functions for each term-set of fuzzy variable to be defined. The simulation results showed that the control time (response) of transients of the swivel head motor torque and current change when using the swivel head velocity control by a fuzzy controller with increasing load depending on the rock hardness decreased by a factor of 2. Implementation of a system of automatic control of swivel head velocity with the application of a fuzzy controller allows drilling rig vibration to be reduced and provided effective protection of the swivel head electric (motor) drive from overload, thus increasing reliability of the equipment, and increasing drilling productivity.

Practical Application of Fractional-Order PID Controller based on Evolutionary Optimization Approach for a Magnetic Levitation System

This article proposes a state-of-the-art design procedure of integer-order PID (IOPID) and fractional-order PID (FOPID) controller applied to a well-established and diversified engineering application of the Magnetic Levitation System (Maglev). Controller design and implementation for the Maglev system are quite complicated and difficult since the system dynamics exhibits non-linearity with a wide variation of operating points. Also, the system is highly unstable which rules out the possibility to accomplish conventional tuning techniques. Thus in this work, the controller tuning methodology is framed as a complex optimization problem by incorporating a new transient specification-based objective function. For designing and tuning of proposed controller parameters, modern meta-heuristic and evolutionary optimization algorithms are deployed; those are Bird Swarm Algorithm, Elephant Herding Optimization, Equilibrium optimizer and Grey Wolf Optimization. The software and hardware results demonstrate that FOPID controllers yield better time-domain and frequency-domain performance specifications and exhibit excellent reference tracking capability than IOPID controllers. The performance robustness of the proposed controllers is greatly enhanced subjected to a vast range of parametric uncertainties along with a significant minimization of the control effort.

Active Fluid-Film Bearing With Deep Q-Network Agent-Based Control System

Abstract Despite the fact that the hydrodynamic lubrication is a self-controlled process, we designed control systems based on proportional integral (PI) controller, adaptive PI controller, and Deep Q network (DQN)-agent to minimize the friction torque in a conical fluid-film bearing. The bearing construction allows the shaft axial displacement and, as the result, the regulation of the bearing average clearance due to the controlled supply pressure. The main challenge is that the friction torque minimization may lead to the loss of load-carrying capacity and the contact in the shaft-bearing tribocouple. The other challenge is that random events may have an influence on the hydrodynamic lubrication parameters, therefore, changing the load-carrying capacity and the friction torque. So, the proposed control systems were designed and tested under the conditions of limited lateral shaft displacements and the action of a random external force. The tests were performed using simulation models of a controlled rotating machine in matlab software. The rotating machine simulation model includes modules of the rigid shaft, the coupling with linear axial reaction, and the conical bearing. The bearing module is based on the numerical solution of the generalized Reynolds equation and its nonlinear approximation with fully connected neural networks. The obtained results demonstrated that the application of an adaptive PI controller or a DQN agent allows decreasing friction torque in a bearing under the conditions of a random external force. The goal of a DQN agent is self-learning in contrast to an adaptive PI controller that needs to be tuned.

Application of S7-300 PLC and Single Axis Motion Controller in Manipulator Control System

A set of flexible manufacturing production line training equipment in the college carries materials through a manipulator, which is controlled by S7-300 PLC. The FM353 module in the control system is damaged, and the device cannot be used. In order to repair the control system, this paper proposes a low cost solution. By choosing a single axis motion controller to replace the FM353 module function and S7-300 PLC with the control system/PLC control single axis motion controller can send high speed pulse output to the stepper driver, achieve the position and speed control of the stepper motor, and realize the control of a four degrees of freedom manipulator. Based on the design scheme, the control system of the manipulator is reformed. After modification, the system can run normally, which verifies the rationality and effectiveness of the design.

An Adaptive Load Frequency Control for Power Systems with Renewable Energy Sources

The frequency of power systems is very sensitive to load variations. Additionally, with the increased penetration of renewable energy sources in electrical grids, stabilizing the system frequency becomes more challenging. Therefore, Load Frequency Control (LFC) is used to keep the frequency within its acceptable limits. In this paper, an adaptive controller is proposed to enhance the system performance under load variations. Moreover, the proposed controller overcomes the disturbances resulting from the natural operation of the renewable energy sources such as Wave Energy Conversion System (WECS) and Photovoltaic (PV) system. The superiority of the proposed controller compared to the classical LFC schemes is that it has auto tuned parameters. The validation of the proposed controller is carried out through four case studies. The first case study is dedicated to a two-area LFC system under load variations. The WECS is considered as a disturbance for the second case study. Moreover, to demonstrate the superiority of the proposed controller, the dynamic performance is compared with previous work based on an optimized controller in the third case study. Finally in the fourth case study, a sensitivity analysis is carried out through parameters variations in the nonlinear PV-thermal hybrid system. The novel application of the adaptive controller into the LFC leads to enhance the system performance under disturbance of different sources of renewable energy. Moreover, a robustness test is presented to validate the reliability of the proposed controller.

Neural Network Training Using Closed-Loop Data: Hazards and an Instrumental Variable (IVNN) Solution

An increasing trend in the use of neural networks in control systems is being observed. The aim of this paper is to reveal that the straightforward application of learning neural network feedforward controllers with closed-loop data may introduce parameter inconsistency that degrades control performance, and to provide a solution. The proposed method employs instrumental variables to ensure consistent parameter estimates. A nonlinear system example reveals that the developed instrumental variable neural network (IVNN) approach asymptotically recovers the optimal solution, while pre-existing approaches are shown to lead to inconsistent estimates.

Development of a low-cost PID setup for engineering technology students

PID controllers are the most frequently used controllers. Theoretical understanding of the system to be controlled and the working principle of PID controllers are crucial for closed-loop control. This fundamental knowledge is - in principle - learned by all engineering students at the undergraduate level. However, understanding the practical aspects of the application of a PID controller and their connections to theoretical concepts must be combined in a clever way. This paper explores the process of the development of a low-cost, easy to manufacture demonstration setup which can act as the bridge between the theoretical and practical world of the control system knowledge. As a reaction to the COVID epidemy, this project is made with the philosophy that the students should be able to take these setups home or even replicate them themselves. The proposed setup comes with an interactive block diagram-based graphical user interface which allows the user to observe the physical changes of the process in -nearly- real-time. The aim of the provided exercises is to comprehend the similarities and the difference between the theoretical and practical aspects of closed-loop control. This paper explains the components of the setup, the development process, the recommended exercises, and feedback from our students.

Application of a Nonlinear Model Predictive Controller to the Anaerobic Digestion of Readily Biodegradable Wastes

A nonlinear model predictive controller for the anaerobic digestion of readily biodegradable substrates is presented. The controller aims to achieve a planned methane production, following a reference trajectory for the whole operation. Using an existing dynamic model of anaerobic digestion, the controller optimizes the operation conditions by conveniently manipulating a set of process variables such that the methane flow rate follows the reference trajectory. The controller works in a sequential approach, i.e., the plant trajectory is estimated over a prediction horizon with a simplified dynamic model of the process that includes only two biological reactions: acidogenesis and methanogenesis; then, the model predictions are optimized via a sequential quadratic programming method to match the desired trajectory. Due to the simplicity of the process model, the iterative optimization process is solved in a relatively short time. Both the dynamic model of the process and the optimization algorithm are implemented in MATLAB. The controller is tested in a simulation case study treating a readily biodegradable liquid effluent, where the same process model is used to mimic the measurements of the real plant.

Синтез системы управления асинхронным двигателем главного движения токарного станка с применением нейроконтроллера с прогнозированием

The paper presents an analysis of the applications of neural network controllers for controlling various non-linear dynamic objects. A model of vector control of an asynchronous motor is constructed, taking into account the moment of inertia of the workpiece. A neural network controller with NN Predictive Controller was synthesized to control the drive of the main movement of a lathe in order to improve the dynamic characteristics of the machine drive. Recommendations are given for selecting the parameters of the neurocontroller to control the object under consideration. The results of computer simulation were performed in the MatLab/Simulink environment to assess the performance of the neurocontroller with prediction under a single step control action. A graph for comparing the control system of an object with and without a neuroregulator is presented and analyzed.

Reformative artificial bee colony algorithm based PID controller for radar servo system

<abstract> <p>This paper proposes a PID controller optimized by a reformative artificial bee colony algorithm (RABC-PID) for the radar servo system (RSS). The RABC algorithm is an enhancement of the artificial bee colony (ABC) algorithm by introducing the best-positioned food source and modifying the food source probability. The RABC algorithm is validated by simulation with six benchmark functions, and the results show that the RABC algorithm is superior to the other variants of the ABC algorithm in terms of convergence speed and accuracy. The RABC-PID controller is then used for the RSS. The RSS is presented to illustrate the application of the RABC-PID controller. The simulation results, which are also compared to PID optimized by particle swarm optimization, differential evolution, and genetic algorithm (PSO-PID, DE-PID, and GA-PID) respectively, are shown to illustrate the effectiveness and robustness of the RABC-PID controller.</p> </abstract>

Research on system simulation based on fuzzy self-tuning

This paper introduces the principle of fuzzy control. On this basis, a fuzzy self-tuning PID controller is designed to the requirements of SRD system. The fuzzy self-tuning PID controller is introduced into the switched reluctance motor speed regulation system, the switched reluctance motor speed regulation system based on fuzzy self-tuning PID is simulated and compared with the conventional PID switched reluctance motor speed regulation system. The comparison results show that the application of fuzzy self-tuning PID controller in the switched reluctance motor speed regulation system can make the speed regulation system obtain good dynamic Static performance.

The Application of Various PID Controllers and the Effect of AHVDC and DSTS on Dynamics Responses in a Multi-Area AGC

The Application of Various PID Controllers and the Effect of AHVDC and DSTS on Dynamics Responses in a Multi-Area AGC

Performance Improvement of Grid Interfaced Hybrid System Using Distributed Power Flow Controller Optimization Techniques

The main aim of this paper is to introduce a framework for the design and modelling of a photovoltaic (PV)-wind hybrid system and its control strategies. The purpose of these control techniques is to regulate continuous changes in the operational requirements of the hybrid system;currently, in power system networks, the distribution of energy plays a major role in maintaining power reliability in distribution systems. In this study, the proposed hybrid system was incorporated with a combined PV and wind energy system. Maximum power point tracking (MPPT) methods have been proposed to achieve maximum efficiency from the designed system. In addition, this study focused on improving the stability of the hybrid system. To improve the power quality and transient stability of the proposed system, we introduce a novel control strategy called the distributed power flow controller (DPFC) implementation with an optimization technique called the lion optimization algorithm(LOA)technique. This LOA control technique was developed for the first time in the application of a DPFC controller in a grid-connected system. The control technique was developed using signals from the system parameters, that is, voltage and current. To tune these parameters, this study used fuzzy logic and lion optimization techniques. The proposed system with controllers was tested in MATLAB/Simulink and the results were compared.

Application of Fuzzy FoPID Controller for Energy Reshaping in Grid Connected PV Inverters for Electric Vehicles

By utilizing Fuzzy based FOPID-controller, this work is designed via the energy reshaping concept for Grid connected Photovoltaic (PV) systems for electric vehicles and this PV module has its own inverter which is synchorised with the utility grid. In grid connected PV system, the mitigation plays an important role where the capacity of PV arrays increases it maintains power quality and it is necessary to comply with some requirements such as harmonic mitigation. Unless a maximum power point tracking (MPPT) algorithm is used, PV systems do not continuously produce their theoretical optimal power. Under various atmospheric conditions, MPPT is obtained by using Perturb and Observe (P&O) techniques. A storage function in terms of DC-link voltage and current, and q-axis current is created, by means of which physical characteristics are studied. Further the energy reshaping is done with the aid of the proposed Fuzzy Fractional Order PID (Fuzzy-FoPID) framework. The FOPID control parameters λ and μ are optimally tuned by Grey Wolf Optimization technique (GWO). The results obtained verify the advantages of using Fuzzy-FoPID control as compared to conventional PID (Proportional Integral Derivative) control and FoPID control. The injected reactive power into the grid and the efficiency of the proposed systems is studied by extensive simulations.

Self-synchronization and control-synchronization of dual-rotor space vibration system

Since the intrinsic characteristics of self-synchronization vibration system, the implementation of in-phase synchronization state is restricted, which leads to unsatisfactory force of dual-rotor system driven by two coaxial motors in the space. Thus, control synchronization is proposed in this paper to address the problems above. Firstly, to explore the defects of the self-synchronization vibration system, dynamic model of the dual-rotor space vibration system is established, and the self-synchronization mechanism of two eccentric rotors (ERs) is analyzed by small parameter average method. Subsequently, to implement the in-phase synchronization between the two ERs, controllers based on master–slave control structure (MSCS) and sliding mode control (SMC) algorithm are systematically designed. Finally, the effectiveness of self-synchronization mechanism and control-synchronization method is validated by simulations in Matlab/Simulink. The results show that the anti-phase synchronization state is appeared on account of small horizontal distance or larger installation height of the two ERs; however, with the application of controllers, the phase difference (PD) of the two ERs is successfully controlled at zero, and the strong robustness of the controllers is further demonstrated by the variations of rotation velocity and structural parameter.

Experimental demonstration of grid-supportive scheduling of a polygeneration system using economic-MPC

Drawing off the technical flexibility of building polygeneration systems to support a rapidly expanding renewable electricity grid requires the application of advanced controllers like model predictive control (MPC) that can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints amongst other features. In this original work, an economic-MPC-based optimal scheduling of a real-world building energy system is demonstrated and its performance is evaluated against a conventional controller. The demonstration includes the steps to integrate an optimisation-based supervisory controller into a standard building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms for solving complex nonlinear mixed integer optimal control problems. With the MPC, quantitative benefits in terms of 6–12% demand-cost savings and qualitative benefits in terms of better controller adaptability and hardware-friendly operation are identified. Further research potential for improving the MPC framework in terms of field-level stability, minimising constraint violations, and inter-system communication for its deployment in a prosumer-network is also identified.

On Improving Algorithm Efficiency of Gas-Kick Simulations toward Automated Influx Management: A Robertson Differential-Algebraic-Equation Problem Approach

Summary Improved numerical efficiency in simulating wellbore gas-influx behaviors is essential for realizing real-time model-prediction-based gas-influx management in wells equipped with managed-pressure-drilling (MPD) systems. Currently, most solution algorithms for high-fidelity multiphase-flow models are highly time consuming and are not suitable for real-time decision making and control. In the application of model-predictive controllers (MPCs), long calculation time can lead to large overshoots and low control efficiency. This paper presents a drift-flux-model (DFM)-based gas-influx simulator with a novel numerical scheme for improved computational efficiency. The solution algorithm to a Robertson problem as differential algebraic equations (DAEs) was used as the numerical scheme to solve the control equations of the DFM in this study. The numerical stability and computational efficiency of this numerical scheme and the widely used flux-splitting methods are compared and analyzed. Results show that the Robertson DAE problem approach significantly reduces the total number of arithmetic operations and the computational time compared with the hybrid advection-upstream-splitting method (AUSMV) while maintaining the same prediction accuracy. According to the “Big-O notation” analysis, the Robertson DAE approach shows a lower-order growth of computational complexity, proving its good potential in enhancing numerical efficiency, especially when handling simulations with larger scales. The validation of both the numerical schemes for the solution of the DFM was performed using measured data from a test well drilled with water-based mud (WBM). This study offers a novel numerical solution to the DFM that can significantly reduce the computational time required for gas-kick simulation while maintaining high prediction accuracy. This approach enables the application of high-fidelity two-phase-flow models in model-prediction-based decision making and automated influx management with MPD systems.

Bilateral backstepping control of coupled linear parabolic PDEs with spatially varying coefficients

This paper considers the backstepping state feedback control of coupled linear parabolic PDEs with spatially varying coefficients and bilateral actuation. By making use of the folding technique, a system representation with unilateral actuation is obtained, allowing to apply the standard backstepping transformation. To ensure the regularity of the solution, the folded system is subject to unusual folding boundary conditions, which lead to additional boundary couplings between the PDEs. Therefore, the solution of the corresponding kernel equations determining the transformations is a very challenging problem. A systematic approach to derive the corresponding integral equations is proposed, allowing to solve them with the method of successive approximations. By making use of a Volterra and a Volterra–Fredholm transformation, the closed-loop system is mapped into a cascade of stable parabolic systems. This allows a simple proof of exponential stability in the L2-norm with the decay rate as design parameter. The bilateral state feedback stabilization of an unstable system of two coupled parabolic PDEs and the comparison to the application of an unilateral controller demonstrates the results of the paper.

Design of a Research Laboratory Drive System for a Synchronous Reluctance Motor for Vector Control and Performance Analysis

Motor-drive systems have the most significant share in industrial energy consumption, which requires a deep study in every aspect of the field. This paper presents a synchronous reluctance motor (SynRM) drive system based on Plecs RT box 1. The system’s design provides the opportunity for the open-loop and closed-loop control of the motor and a characteristic performance analysis of the motor. This paper focuses on the hardware implementation of a research laboratory setup and the precise vector control of the SynRM in real-time. The application of the digital controller and inverter to drive SynRM is examined. The voltage, current, and speed transducers were employed for monitoring the protective measures and to control the motor in the closed-loop. The design of the signal conditioning and the intermediary cards for isolation and data acquisition are described in detail. An algorithm is proposed to measure the whole system parameters, including motor, inverter, and cables. Thanks to the RT box 1, the principle of real-time simulation of control algorithms is investigated, and the rapid control prototyping of field-oriented control (FOC) of SynRM was implemented. The simulation of the system was carried out in the Plecs platform, and the results are presented. The experimental results of the implemented control algorithms validate the setup’s performance and the control algorithm. Finally, as a study of the motor’s performance, the efficiency map of the motor is drawn in different speed and torque ranges.