PSO-PID Controller Research Articles

Optimal digital PID controller design for regulating blood glucose level of type-I diabetic patients

The main objective of this paper is the design and implementation of an optimised PID controller using Xilinx System Generator for the regulation of blood glucose level of type-I diabetic patients. In this work, the Bergman model is used to describe diabetic patient model. The particle swarm optimisation algorithm is used to find the optimal values of the PID controller parameters. To determine parameters that meet closed loop system of the blood glucose regulation performance specification and to show its effectiveness, the proposed PSO-PID controller is compared to the classical PID. Finally, Xilinx System Generator is used to implement the digital optimal PID controller and verify its capability.

Optimization of Two Area AGC based Power System Using PSO Tuned Fuzzy PID Controller and PSO Trained SSSC And TCPS

Automatic generation control or AGC system is significant controlling system that operates efficiently to balance the load and generation in power system at minimum cost for economical operation. System frequency will vary from nominal value if there is mismatch occurs between generation and demand. Due to this high frequency deviation system may breakdown. A very fast, reliable and accurate controller is needed to maintain the system frequency within the range to maintain stability. In this paper the proposed model consisting of PID controller whose parameters have been optimized using PSO tuned Fuzzy Logic Controller and it’s been compared with conventional PSO-PID controller. Each control area in power systems includes the dynamics response of the systems. The results contained in this paper present the strength of the particle swarm optimizer for tuning the Fuzzy based PID controller parameter for two area power system network, for better performance PSO trained SSSC and TCPS has been introduced to the system. The enhancement in the dynamic response of the power system network is verified. The output response of the proposed work is compared with conventional PSO-PID & PSO Fuzzy-PID based AGC system. Simulation experiments so conducted in MATLAB showed that the proposed system outperformed the conventional one by achieving better response.

PSO Based PID Controller for Quadrotor with Virtual Sensor

Recent development and implementation on intelligent control system has lead to the development of virtual sensing system technology. Virtual sensing system allows immeasureable state variables to be accurately predicted, which is very beneficial to reduce the amount of sensors required to monitor and control a system, especially for the case of controlling a quadrator. This paper proposes a novel technique to design a PID control using virtual sensing system, consisting of Diagonal Recurrent Neural Network (DRNN) and Extended Kalman Filter (EKF), which predicts the immeasureable states of the quadrator system based on the current states and control inputs. A bio-inspired optimization technique, Particle Swarm Optimization (PSO), is proposed to be applied in DRNN to avoid any possibilities from local extreme condition. Further, a PSO based PID position controller is also developed to be integrated with the designed virtual sensing system to control a quadrator.

PSO-Based PID Controller for AVR Systems Concerned with Design Specification

PSO-Based PID Controller for AVR Systems Concerned with Design Specification

Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the fuel cell systems. In this paper an efficient method based on the particle swarm optimization (PSO) and PID controller (PSO-PID) is proposed for MPPT of the proton exchange membrane (PEM) fuel cells. The closed loop system includes the PEM fuel cell, boost converter, battery and PSO-PID controller. PSO-PID controller adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are compared with perturb and observe (P&O) and sliding mode (SM) algorithms under different operating conditions. PSO algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system.

AN EFFICIENT ASYMMETRICAL 15-LEVEL SMART INVERTER TOPOLOGIES FOR MICRO-GRID SYSTEM USING PSO-PID TUNING ALGORITHM

A unique fruitful problem solving platform in a global optimizing area has been implemented via particle swarm optimization. So far, most of the algorithms operate by ultimate usage of effective learning pattern, which process all the swarms used in a sufficient strategy. The grid connected AC module plays a perfect stand-by solution to co-power generation scheme. It combines both PV/FC as a single arrangement for proposed power conditioning topologies via effective control objective. This single module produces the constant output voltage as 100v to 400v from 10v to 40v input source by highly recognized boost converter and interfacing to gird system by proposed asymmetrical 15-level inverter topologies. The PSO-PID controller predicts the optimal modulation index & perfect switching angles for regulating the sudden variations from grid, eradicate the harmonics, improved voltage profile, better THD resolution. In this evaluation, proposed schemes support a pure sinusoidal current which is in-phase with grid outcome voltage. The proposed inverter topologies are validated with PSO-PID controller by advanced multi-carrier modulation schemes are evaluated by Matlab/Simulink platform, pertaining results are conferred.

PSO-based PID controller design for an energy conversion system using compressed air

PSO-based PID controller design for an energy conversion system using compressed air

A fully tuned RACS for flight control system

This paper presents the design of a roll attitude control system (RACS) based on bacterial foraging particle swarm optimised (BFPSO) proportional integral and derivative (PID) autopilot that controls the roll angle of an aircraft. To achieve the high degree of spiral stability desired in aircraft flight control system (AFCS) requires the use of RACS. The RACS uses feedback mechanism to maintain the roll attitude in presence of disturbances and responds rapidly to the commands from the pilot. Here a new algorithm is proposed which combines the advantages of both PSO and BFO to form a new algorithm called BFPSO. The parameters such as Kp, Ki and Kd of PID-based RACS controller are tuned by BFPSO algorithm to achieve the dynamic stability as required by the pilot while spinning an aircraft. Finally, it is found from simulation, proposed controller gives the best performance compared to PSOPID, BFOPID and RBFNC controller.

A fully tuned RACS for flight control system

This paper presents the design of a roll attitude control system (RACS) based on bacterial foraging particle swarm optimised (BFPSO) proportional integral and derivative (PID) autopilot that controls the roll angle of an aircraft. To achieve the high degree of spiral stability desired in aircraft flight control system (AFCS) requires the use of RACS. The RACS uses feedback mechanism to maintain the roll attitude in presence of disturbances and responds rapidly to the commands from the pilot. Here a new algorithm is proposed which combines the advantages of both PSO and BFO to form a new algorithm called BFPSO. The parameters such as Kp, Ki and Kd of PID-based RACS controller are tuned by BFPSO algorithm to achieve the dynamic stability as required by the pilot while spinning an aircraft. Finally, it is found from simulation, proposed controller gives the best performance compared to PSOPID, BFOPID and RBFNC controller.

PSO based Energy Efficient IR Heating System

Objectives: Maintaining uniform temperature with low power consumption is an important factor in various household and industrial applications. This paper presents the development of energy efficient IR heating system using particle swarm optimization. Methods/Statistical Analysis: The IR heating system model is developed by process identification method using step input, controllers are designed, simulated using MATLAB/Simulink and implemented using Arduino. Findings: Experimentally, the PSO based PID controller is proven to be a method for saving of power for temperature control of IR heating system and also shows superiority over the conventional PID controller in terms of performance indices. PSO based energy efficient IR heating system has overcome the drawbacks associated with temperature control of IR heating system like repeated two position switching and inferior time domain specifications which leads to more power consumption. Application/Improvements: PSO based energy efficient IR heating system can be used in various commercial and industrial applications to maintain the temperature with low power consumption.

Intelligent techniques for optimization of a two tank system controller

In this paper, a design methodology which optimizes PID parameters of a two tank system controller via the particle swarm optimization (PSO) algorithm is proposed. The simulation results prove that the PSO-PID controller is more efficient than the genetic algorithm method in seeking a much better time response. In addition, PID controller designed by the conventional Zeigler–Nichols technique has also been presented.

Temperature Controller for Continuous Stirred Tank Reactor using Adaptive PSO Based PID Controller

Temperature Controller for Continuous Stirred Tank Reactor using Adaptive PSO Based PID Controller

Modeling and Force Control of Thin Soft McKibben Actuator

This paper presents the modeling of a thin soft McKibben actuator using the system identification (SI) method and its force control. Procedures from the system identification method are used to create a mathematical model (transfer function) from the test data. The autoregressive with exogenous input (ARX) model was chosen as the model structure of the system. Next, a PSO-PID controller was proposed for the force control of the actuator. The simulation data were verified against the test data for the force control using PSO-PID and conventional PID. Results showed that the developed model represents the actual system by giving the same characteristics in the force control analysis in step, multi-step, and sinusoidal input.

Swarm intelligence based robust active queue management design for congestion control in TCP network

Active queue management (AQM) is an effective solution for the congestion control problem. It can achieve high quality of service (QoS) by reducing the packet dropping probability and network utilization. Three robust control algorithms are proposed in this paper in order to design robust AQM schemes: conventional controller, robust particle swarm optimization (PSO)‐based PID (proportional–integral–derivative) (PSOPID) controller, and robust ant‐colony optimization (ACO)‐based PID (ACOPID) controller. PSO and ACO methods are used to tune the PID controller parameters subject to constraints to achieve the required robustness of the network. Robust PSOPID and ACOPID controllers can achieve desirable time‐response specifications with a simple design procedure and low‐order controller in comparison to the conventional controller. Wide ranges of system parameters change are used to show the robustness of the designed controllers. The ability of the designed controllers to meet the specified performance is demonstrated using MATLAB 7. 11, (R2010b): The MathWorks, Inc.3 Apple Hill Drive Natick, MA USA. On the other hand, to verify the effectiveness of the designed controller, nonlinear simulation is performed using the NS2 package. Finally, it is shown by comparison that the proposed robust ACOPID can achieve more desirable performance than the PSOPID controller and the controllers that have been proposed in previous works. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Critical Evaluation of Advanced Multi-Carrier Modulation Scheme for 15-Level Inverter via PSO-PID Controller

Particle swarm optimizing mechanism has been shown as a fruitful problem solving tool in a global optimization areas. So far, most of this algorithm is working properly by utilizing an imperative learning pattern, which process the all swarms utilizes in the unique strategy. The grid integrated system is interpretation of stand-by module to co-generation scheme; it combines a PV/FC arrangement with a high recognized power conditioning. Utilization of high voltage gain power conditioning units which plays a crucial role in a grid connected system with a single module. The module ranges from 100V to 300V getting from 10V to 40V input, then incorporated to micro-grid system via proposed asymmetrical inverter with PSO-PID controller. The formal tuning of the PID controller generates an unreasonable peak over-shoot; more error content will be counteracting by meta-heuristics approaches by a swarm optimizer. This controller forecasts the optimal modulation index & switching angles for attaining enhanced output voltages, in-limit THD resolution & averts the sudden variations. In this behaviour, the proposed scheme provides perfect sinusoidal grid voltages which are in-phase with the current, good harmonic limitation values by using PSO-PID controller. A simulink model is implemented to validate the appearance of proposed 15-level asymmetrical inverter topology with PSO-PID controller by advanced modulation schemes using Matlab/Simulink platform & results are conferred.

Optimal Control of Automatic Generation with Automatic Voltage Regulator Using Particle Swarm Optimization

A simultaneous study of load frequency control and automatic voltage regulation is considered. The single-area and two-area power systems have been considered with a combined model of AGC and AVR for analysis of frequency and voltage deviation. The primary aspect of the design is to keep real power control as to preserve frequency of the system in a prescribed limit. Reactive power control is used to keep the voltage magnitude of synchronous generator at a tolerable limit. The transients produced in dynamic responses are controlled using intelligent and soft-computing technique. The fuzzy logic controller and particle swarm optimization based PID controllers are considered. The steady state error and settling time of the dynamic responses are observed and found to be reduced very effectively. The frequency and terminal voltage results of a single-area power system with proposed PSO-PID controller is compared to the responses of system with controllers presented in literature and found to be more effective. The comparison of fuzzy logic controller with PSO-PID is done by observing the nature of two-area responses. The result obtained by soft computing algorithm and intelligent controller is analyzed on the basis of settling-time, undershoot and ISE Error.

Effect of Load Parameters Variation on AGC of Two Area Thermal Power System In the Presence of Integral and PSO-PID Controllers

Effect of Load Parameters Variation on AGC of Two Area Thermal Power System In the Presence of Integral and PSO-PID Controllers

Gravitational Search Algorithm Optimization for PID Controller Tuning in Waste-water Treatment Process

This paper presents a new approach of optimization technique in the controller parameter tuning for waste-water treatment process (WWTP) application. In the case study of WWTP, PID controller is used to control substrate (S) and dissolved oxygen (DO) concentration level. Too many parameters that need to be controlled make the system becomes complicated. Gravitational Search Algorithm (GSA) is used as the main method for PID controller tuning process. GSA is based on Newton's Law of Gravity and mass interaction. In this algorithm, the searcher agents survey the masses that interact with each other using law of gravity and law of motion. For WWTP system, the activated sludge reactor is used and this system is multi-input multi-output (MIMO) process. MATLAB is used as the platform to perform the simulation, where this optimization is compared to other established optimization method such as the Particle Swarm Optimization (PSO) to determine whether GSA has better features compared to PSO or vice-versa. Based on this case-study, the results show that transient response of GSA-PID was 20%-30% better compared to transient response of the PSO-PID controller.

System Identification and PID-PSO Force Control of Thin Soft Actuator

This paper presents the System Identification (SI) method for modeling of thin soft actuator and its force control using PID-PSO. System identification is used to obtain the mathematical model (transfer function) from the measured experimental data using the SI procedure. Auto Regressive with Exogenous Input (ARX) model is chosen as model structure of the system. The result from SI model shows linear discrete model to obtain a discrete transfer function for the soft actuator system. Next, PSO-PID controller was proposed for the force control of the actuator. Validation was made between simulation and experimental data for the force control. Results show that the developed model represents the actual system by giving same characteristics in the force control analysis.

Two regional power system PSO PID Control Research

In this paper, we propose a PID parameter tuning of particle swarm optimization for multi-objective optimization characteristics of two regional power system PID controller design. By defining a comprehensive consideration of system output overshoot, rise time and the fitness function term steady-state error indicators, such as the ITAE, and in accordance with the performance requirements of the actual control system, appropriate weighting of each index item. Use with base and improved particle swarm algorithm for multi-objective optimization PID. PSO optimization algorithm has good global search ability and high convergence rate. You can respond to the PID controller tuning parameters directly from the output of the PID control system easily balance between speed and stability control systems. After using PSO-PID control in two regional power system ，the amount of overshoot in the step total response downs from 65% to 5%. Adjust time downs from 12 seconds to 3 seconds. DOI: http://dx.doi.org/10.11591/telkomnika.v13i1.6835