Alleviation of power fluctuation in a microgrid by electrolyzer based on optimal fuzzy gain scheduling PID control

Chaiyatham, Theerawut; Ngamroo, Issarachai

doi:10.1002/tee.21951

“…Currently, these CGRs are calculated in an ad hoc manner by the designer, which is a complex procedure especially for time-varying and nonlinear systems. The issue was addressed in the literature, and several methods were presented to determine the CGRs in FGS-PID controller [112,[115][116][117][118]. For example, in [117] CGRs are determined in FGS-PID by rule of thumb based on extensive simulation studies and by using the gain and period of oscillation at the stability limit under proportional control.…”

Section: Defuzzifiermentioning

confidence: 99%

“…However, the simulation results show a considerable overshoot at the transient response of the controlled system. An optimal FGS-PID control is also presented in [115] to enhance the performance of conventional PID controller. The method uses Bee Colony Optimization technique to automatically tuning the scaling factors, membership functions and control rules of the FGS-PID controller automatically.…”

Section: Defuzzifiermentioning

confidence: 99%

Subspace predictive control: stability and performance enhancement

Sedghizadeh¹

2021

Preprint

0

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In the absence of prior knowledge of a system, control design relies heavily on the system identifi- cation procedure. In real applications, there is an increasing demand to combine the usually time consuming system identification and modeling step with the control design procedure. Motivated by this demand, data-driven control approaches attempt to use the input-output data to design the controller directly. Subspace Predictive Control (SPC) is one popular example of these algorithms that combines Model Predictive Control (MPC) and Subspace Identification Methods (SIM). SPC instability and performance deterioration in closed-loop implementations are majorly caused by either poor tuning of SPC horizons or changes in the dynamics of the system. Stability and performance analysis of the SPC are the focus of this dissertation. We first provide the necessary and sufficient condition for SPC closed-loop stability. The results introduce SPC stability graphs that can provide the feasible prediction horizon range. Consequently, these stability constraints are included in SPC cost function optimization to provide a new method for determining the SPC horizons. The novel SPC horizon selection enhances the closed-loop performance effectively. Note that time-delay estimation and order selection in system modeling have been a challenging step in applications and industry. Here, we propose a new approach denoted by RE-based TDE that simultaneously and fficiently estimates the time-delay for the SIM framework. In addition, we use the recently developed MSEE approach for estimating the system order. Moreover, we propose an arti- ficial intelligence approach denoted by Particle Swarm Optimization Based Fuzzy Gain-Scheduled SPC (PSO-based FGS-SPC). The method overcomes the issue of on-line adaptation of SPC gains for systems with variable dynamics in the presence of the noisy data. The approach eliminates existing tuning problem of controller gain ranges in FGS and updates the SPC gains with no need to apply any external persistently excitation signals. As a result, PSO-based FGS-SPC provides a time efficient control strategy with fast and robust tracking performance compared to conventional and state of the art methods.

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“…Currently, these CGRs are calculated in an ad hoc manner by the designer, which is a complex procedure especially for time-varying and nonlinear systems. The issue was addressed in the literature, and several methods were presented to determine the CGRs in FGS-PID controller [112,[115][116][117][118]. For example, in [117] CGRs are determined in FGS-PID by rule of thumb based on extensive simulation studies and by using the gain and period of oscillation at the stability limit under proportional control.…”

Section: Defuzzifiermentioning

confidence: 99%

“…However, the simulation results show a considerable overshoot at the transient response of the controlled system. An optimal FGS-PID control is also presented in [115] to enhance the performance of conventional PID controller. The method uses Bee Colony Optimization technique to automatically tuning the scaling factors, membership functions and control rules of the FGS-PID controller automatically.…”

Section: Defuzzifiermentioning

confidence: 99%

Subspace predictive control: stability and performance enhancement

Sedghizadeh¹

2021

Preprint

0

View full text Add to dashboard Cite

In the absence of prior knowledge of a system, control design relies heavily on the system identifi- cation procedure. In real applications, there is an increasing demand to combine the usually time consuming system identification and modeling step with the control design procedure. Motivated by this demand, data-driven control approaches attempt to use the input-output data to design the controller directly. Subspace Predictive Control (SPC) is one popular example of these algorithms that combines Model Predictive Control (MPC) and Subspace Identification Methods (SIM). SPC instability and performance deterioration in closed-loop implementations are majorly caused by either poor tuning of SPC horizons or changes in the dynamics of the system. Stability and performance analysis of the SPC are the focus of this dissertation. We first provide the necessary and sufficient condition for SPC closed-loop stability. The results introduce SPC stability graphs that can provide the feasible prediction horizon range. Consequently, these stability constraints are included in SPC cost function optimization to provide a new method for determining the SPC horizons. The novel SPC horizon selection enhances the closed-loop performance effectively. Note that time-delay estimation and order selection in system modeling have been a challenging step in applications and industry. Here, we propose a new approach denoted by RE-based TDE that simultaneously and fficiently estimates the time-delay for the SIM framework. In addition, we use the recently developed MSEE approach for estimating the system order. Moreover, we propose an arti- ficial intelligence approach denoted by Particle Swarm Optimization Based Fuzzy Gain-Scheduled SPC (PSO-based FGS-SPC). The method overcomes the issue of on-line adaptation of SPC gains for systems with variable dynamics in the presence of the noisy data. The approach eliminates existing tuning problem of controller gain ranges in FGS and updates the SPC gains with no need to apply any external persistently excitation signals. As a result, PSO-based FGS-SPC provides a time efficient control strategy with fast and robust tracking performance compared to conventional and state of the art methods.

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