Opinion dynamics with decentralized proportional–integral control strategy

The El Farol bar problem is a simple model of congestion and coordination behaviours that occur with shared resources. From a control-theoretic point of view, this problem can be viewed as a decentralized control problem. This work is a first step towards solving the El Farol bar congestion problem based on control theory. A decentralized proportional-integral (PI) control strategy is proposed and investigated via simulation. This strategy provides a simple mechanism for a large collection of decentralized decision makers to solve a complex congestion problem.

Multiport DC-DC converters have been most commonly used in stand-alone renewable power systems to provide smooth and constant power to the loads. To supply the load without any interruption under various disturbances, the centralised controller in various typologies of multiport converter has been implemented only by using conventional proportional integral (PI) controller. This study presents the output voltage control of the three-port full-bridge converter interfacing photovoltaic (PV) system with three different controllers. The dynamic response of the proposed converter is analysed with the PI controller, fuzzy logic controller (FLC), and the sliding mode controller (SMC). The state space model of the three-port full-bridge converter is developed to analyse the performance using SMC. PI, FLC, and SMC-based control scheme is developed using the MATLAB/Simulink environment. The response of the PI, FLC, and SMC are analysed for line regulation, load voltage regulation, and for the converter parameter variation. The simulation results illustrate that the SMC gives a better steady state and dynamic response when compared with the PI and FLC. The results are presented to confirm the proposed control schemes.

The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.

Proportional Integral (PI) controller has attracted researchers in industrial control processes because of its simplicity and robust performance in a wide range of operating conditions. It has been used to provide proportional delay differentiation on web servers in previous work. However, PI controller cannot achieve satisfactory results due to (1) the web server's non-linearity properties, and (2) the difficulty of building an accurate model for the web server. To address these issues, a nonlinear fuzzy PI controller is proposed in this paper, which has the advantage of fuzzy controller while maintaining the simplicity and robustness of PI controller. The proposed controller are self-tuned according to the periodical online performance measurement. The experimental results demonstrate that our fuzzy PI controller outperforms the PI controller in several aspects.

This paper proposes a design of modified Guo Tao based optimized Proportional Integral (PI) controller for the level control system. The performance of a designed system is compared with Fuzzy PI and conventional PI controller. The parameters of PI controller are optimized using Genetic Algorithm (GA) with Elite Preservation Strategy (EPS). Guo Tao proposed a subspace searching GA (GTA) in his research. Limitations of Guo Tao's algorithm (GTA) motivate us to propose a modified GTA using elite preservation strategy. Multi-parent crossover is used with EPS to enhance the performance of controller. A transfer function of a level loop of fabricated experimental setup is obtained by using the step response analysis. The performance of proposed controller is checked and verified by transfer function analysis using Ziegler Nicholas (Z-N) method in MATLAB environment. The proposed controller algorithm possesses an excellent transient response as compare to Fuzzy PI and conventional PI controller.

The main objective of this paper is to allow renewable energy sources (RES) to actively participate within hybrid microgrid by proposing a new control system based on fractional order proportional integral (FOPI) controller. Fractional order proportional integral controller is a classical proportional integral (PI) in which the integral part is a fraction instead of integer numbers. The paper introduces a hybrid photovoltaic (PV), wind turbine and battery storage system connected to a three-phase grid. Three types of controller are considered and compared for a hybrid renewable energy system (HRES), namely, FOPI, PI, and the fractional order integral control (FIC). For the PV resource, maximum power point tracking (MPPT) controller was designed using the incremental conductance plus integral regulator technique. A DC/DC boost converter was utilized to connect the renewable energy resources to a point of common coupling. MATLAB/Simulink is adopted to perform the simulation results of the developed HRES. The results show that the FOPI controller outperforms other controllers under several operating conditions. The paper also includes experimental results from a prototype real scale.

Augmenting effectiveness of control loops of a PMSG (permanent magnet synchronous generator) based wind energy conversion system by a virtually adaptive PI (proportional integral) controller

In this work, comparative analysis between various maximum power point tracking (MPPT) control schemes and pitch angle control methodologies are presented. In first place, proportional integral (PI), Sliding mode (SMC), Backstepping (BS) and Fuzzy logic (FLC) control strategies applied for MPPT to maximize the power during the wind energy conversion are developed. Then, the blades pitch angle control using PI and FLC controllers in order to limit the output power of the wind turbine (WT) are presented. The systems performances were simulated and compared using Matlab/Simulink environment. Simulation results show the effectiveness of the proposed control strategies compared to the PI one, regarding reference tracking, inaccessibility of turbine parameters and sensibility to high variations of wind speed.

The primary function of microgrid (MG) is to share the power accurately among different distributed generation (DG) units according to the load demands. The droop control methods are normally used for appropriate power sharing. However, these control techniques suffer badly due to the feeder impedances and uncontrollable power coupling issues. Many techniques have been proposed to share power accurately, most of them rely on communication link. In this work a localized/communication free signal generating mechanism is proposed. The mechanism injects the transient reactive power in frequency droop characteristics by observing the changes in microgrid. This injection disturbs the actual active power sharing which is helpful for the computation of reactive power sharing errors. These errors are minimized by utilizing the proportional integral (PI) controller. The PI controller modifies the y-intercept and the slop of Q-E (Reactive Power –Voltage) droop characteristic for the correction of reactive power sharing error. Moreover, the parameters of droop and PI controller of inner control loop are optimized by Particle Swarm Optimization (PSO) to obtain high precision and reliability. The proposed technique has the following salient features:1. Accurately shares the reactive power without the information of feeder line impedances2. This accurate reactive power sharing scheme has been started locally without the need of communication signal from the central controller. Thus, there is no delay in monitoring the notable changes in microgrid caused by DGs. 3. The careful selection of PI & droop controller parameters through PSO makes the system more stable as compared to the conventional methods in terms of transients and steady state responses.

This study aims to determine the improvement effect on the delay margin if fractional-order proportional integral (PI) controller is used in the control of a single-area delayed load frequency control (LFC) system. The delay margin of the system with fractional-order PI control has been obtained for various fractional integral orders and the effect of them has been shown on the delay margin as a third controller parameter. Furthermore, the stability of the system that is either under or over the delay margin is examined by generalized modified Mikhailov criterion. The stability results obtained have been confirmed numerically in time domain. It is demonstrated that the proposed controller for delayed LFC system provides more flexibility on delay margin according to integer-order PI controller.

Permanent magnet synchronous motor (PMSM) is a multivariable, nonlinear and strongly coupled system. It is difficult to achieve the ideal control effect by using the basic proportional integral (PI) control method in the process of speed change and load disturbance. In this paper, an adaptive PI parameter control strategy for speed and torque is proposed. First, the optimal control parameters are obtained by the iterative learning. Then, the Adaptive Neuro-Fuzzy Inference System(ANFIS) learns from iterative learning results to achieve the system membership function parameters and control rules adjustment. Finally, the simulation results show that the system is characterized by high efficiency, high speed, high accuracy, small static response error, less response time and small overshoot. It is suitable for each torque transition and different operating speeds, which is superior to the basic PI control and fuzzy PI control with high control precision, good dynamic performance and strong anti-interference ability.

This paper displays an simple method for identification of controller for wind based Quadratic Boost Converter Inverter system. India, being the fourth largest wind power generator has attracted numerous researchers towards the improvement of wind energy conversion system. This paper also presents improved controller techniques for a Permanent Magnet Synchronous Generator (PMSG) coupled with Cascaded Quadratic Boost Converter (QBC) and Space Vector Modulation Inverter (SVMI). The digital simulation and execution of PMSG based wind turbine along with QBC and SVM Inverter is presented in a closed loop system. The performance of closed loop system is realized using Proportional Integral (PI) and Fractional Order Proportional Integral (FOPI) controllers. Initially, the AC power from PMSG wind turbine is rectified to DC using rectifier circuit. The DC power from the bridge rectifier is then boosted to the required level using quadratic boost converter. The output from QBC is then given to the SVM inverter. The closed loop investigations are carried with PI and FOPI controllers. The simulation results of both PI and FOPI controlled QBC are compared. The outcome of FOPI controller represents that the steady state error and settling time are reduced when compared to PI controlled closed loop Quadratic Boost Converter. The overall Matlab/simulink model is applied to undergraduate/postgraduate course as a educational tool and assessed thoroughly.

This paper is to study a Distributed Static Compensator (D-STATCOM) for voltage sag mitigation using a low-cost microcontroller in hardware in the loop (HIL) application. A framework of HIL-Matlab-Simulink simulation is developed using Raspberry Pi (RPi) and Arduino with a combination of Proportional Integral (PI) control for mitigating the voltage sag due to sudden change of load. The simulation results are presented in both offline and online modes. During the offline mode, the D-STATCOM is able to correct the voltage sag from 0.7 (p.u) to 1.0 (p.u) when the fault happens by injecting the current using the PI control strategy. As for online modes using the HIL platform, the voltage sag has also been mitigated with less response on the time response due to low bit design in the HIL, which currently is about a 3-bit model. In the meantime, the HIL system can be developed using a low-cost microcontroller as a learning process to the researchers before actual HIL is applied, which required more complicated signal processing configurations.

Given the coupling of speed and current and the nonlinearity of electronic devices, the photoelectric tracking servo system for milling machines make it difficult to determine the model parameters, and traditional proportional–integral (PI) control hardly meets the high-performance requirements of milling machine optoelectronic tracking servo systems, especially in the development of system networking. In this study, an event-triggered intelligent PI position control strategy based on a multi-innovation identification model was proposed to improve the low control accuracy and dynamic performance caused by the strong coupling and nonlinearity in the optoelectronic tracking servo system of computerized numerical control (CNC) milling machines. A discredite model of the system was established through a multi-innovation identification model, and the PI control parameter was quickly determined using an improved multiverse optimization (IMVO) algorithm. At the same time, an event-triggering mechanism was introduced, thus reducing the number of controller triggers and saving system resources while ensuring the dynamic performance of the system. Finally, experiment results were compared with typical second-order system engineering design PI (SSED-PI) control, pole placement PI (PP-PI) control, and multiverse optimization (MVO)-PI control. Results demonstrate that the proposed multi-innovation stochastic gradient identification model fully utilizes the historical turning angle information of the optoelectronic tracking servo system and has higher accuracy than traditional stochastic gradient identification (parameter accuracy improved by 6.9 times, quantization error reduced by 6.7 times). The proposed event-triggered IMVO-PI (ET-IMVO-PI) has a triggering frequency of 3.5% compared with time-triggered IMVO-PI, with an overshoot of less than 0.5%, which can meet the needs of most engineering practices (less than 5%). Compared with event-triggered SSED-PI, PP-PI, and IMVO-PI, ET-IMVO-PI has higher dynamic performance and fewer triggering times, which can effectively meet the requirements of high-performance network control. The proposed method serves a crucial theoretical guide and important reference for the upgrading and transformation of the photoelectric tracking servo system of CNC milling machines. Keywords: Event-triggered control, Multiple innovative parameter identification, Multiverse optimization PI, Optoelectronic tracking servo system

Load changes happen during the feeding process to form a workpiece. The load changes affect on spindle speed response, so the controller needed to keep the speed at the specified reference value. Fuzzy Gain Scheduling is the load changes control method that used to change the proportional integral (PI) control parameter values. The change of parameter is made through the Fuzzy system. The Fuzzy system is chosen because it is simple in design and does not require complex calculation analysis. The proportional integral (PI) controller that given to a plant, provide time rise (tr) 1.424 seconds and time settling (ts) 1.450 seconds. Whereas using Fuzzy Gain Scheduling controller, the response of the plant produces time rise (tr) 0.650 seconds and time settling (ts) 0.659 seconds. At the time of loading, the speed will decrease and the controller will adapt to give the control action to make the speed return to steady state at the reference value, the time required for the plant with proportional integral (PI) controller to adapt with 0.544 seconds loading, using the Fuzzy Gain Scheduling controller is 0.212 seconds.