A novel real-time fuzzy adaptive auto-tuning scheme for cascade PID controllers

Fadaei, A.; Salahshoor, Karim

doi:10.1007/s12555-011-0502-y

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…Therefore, a fuzzy self-tuning PID controller is proposed to improve the control performance in this research. The fuzzy self-tuning method provides an opportunity to adjust the PID parameters when a system is running with variable input conditions [29,30]. The structure of the fuzzy self-tuning PID controller is shown in Fig.…”

Section: Conventional Pid Controllermentioning

confidence: 99%

Control of Supercritical Organic Rankine Cycle based Waste Heat Recovery System Using Conventional and Fuzzy Self-tuned PID Controllers

Chowdhury

Thornhill

Soulatiantork

et al. 2019

Int. J. Control Autom. Syst.

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This research develops a supercritical organic Rankine cycle (ORC) based waste heat recovery (WHR) system for control system simulation. In supercritical ORC-WHR systems, the evaporator is a main contributor to the thermal inertia of the system, which is greatly affected by transient heat sources during operation. In order to capture the thermal inertia of the system and reduce the computation time in the simulation process, a fuzzy-based dynamic evaporator model was developed and integrated with other component models to provide a complete dynamic ORC-WHR model. This paper presents two control strategies for the ORC-WHR system: evaporator temperature control and expander output control, and two control algorithms: a conventional PID controller and a fuzzy-based self-tuning PID controller. The performances of the proposed controllers are tested for set point tracking and disturbance rejection ability in the presence of steady and transient thermal input conditions. The robustness of the proposed controllers is investigated with respect to various operating conditions. The results show that the fuzzy self-tuning PID controller outperformed the conventional PID controller in terms of set point tracking and disturbance rejection ability at all conditions encountered in the paper.

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Section: Conventional Pid Controllermentioning

confidence: 99%

Control of Supercritical Organic Rankine Cycle based Waste Heat Recovery System Using Conventional and Fuzzy Self-tuned PID Controllers

Chowdhury

Thornhill

Soulatiantork

et al. 2019

Int. J. Control Autom. Syst.

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“…The conventional PID controller with fixed gains may usually deteriorate the controller performance. Various types of modified PID controllers have been developed such as intelligent PID controller [12], self-tuning discrete PID controller [13], self-tuning predictive PID controller [14] and fuzzy adaptive [15]. However, if severe nonlinearity is involved in the controller process, an adaptive control scheme will be more useful, particularly in the case of power systems.…”

Section: Adaptive Self-tuning Pid Controllermentioning

confidence: 99%

Intelligent control of static synchronous series compensator via an adaptive self-tuning PID controller for suppression of torsional oscillations

Farahani

Ganjefar

2012

Int. J. Control Autom. Syst.

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In this paper, an intelligent controller is proposed to control a static synchronous series compensator (SSSC) in order to mitigate subsynchronous resonance (SSR) oscillations in a power system. This intelligent controller is an adaptive self-tuning PID controller. To train the PID controller, the gradient descent method is employed where the learning rate is adapted in every iteration in order to accelerate the speed of convergence. This control scheme also requires a wavelet neural network (WNN) to identify the controlled system dynamic. To update the parameters of WNN, the gradient descent (GD) along with the adaptive learning rates derived by the Lyapunov method is used. The computer simulations are used to show the ability of the proposed controller. In addition, the performance of the proposed controller is compared with another self-tuning PID controller. The results demonstrate that the proposed controller has a successful performance in minimizing the SSR.

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“…Some of the tuning methods use the modified PID controller such as Fractional Order PID [8,9] and Internal Model Control [10,11]. Artificial intelligence techniques could be used to adapt PID gains such as Fuzzy PID [12,13], Genetic Algorithm [14,15] and Neural Network [16]. Additionally, other advanced PID tuning techniques are available based on multi-objective optimization [17] or predictive control [18,19].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications

et al. 2020

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Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative (PID) controller is a common industrial controller known for its simplicity and robustness. Adapting the parameters of the PID considering system constraints is a challenging task. Both controllers, MPC and PID, merged in a hierarchical structure in this work to improve the industrial processes performance considering the operational constraints. The proposed control system is simulated and implemented on a three-tank benchmark system as a Multi-Input Multi-Output (MIMO) system. Since the main industrial goal of the proposed configuration is to be easily implemented using the available automation technology, PID controller is implemented in a PLC (Programable Logic Controller) controller as a lower controller level, while MPC controller and the adaptation mechanism are implemented within a SCADA (Supervisory Control And Data Acquisition) system as a higher controller level.

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A novel real-time fuzzy adaptive auto-tuning scheme for cascade PID controllers

Cited by 13 publications

References 26 publications

Control of Supercritical Organic Rankine Cycle based Waste Heat Recovery System Using Conventional and Fuzzy Self-tuned PID Controllers

Control of Supercritical Organic Rankine Cycle based Waste Heat Recovery System Using Conventional and Fuzzy Self-tuned PID Controllers

Intelligent control of static synchronous series compensator via an adaptive self-tuning PID controller for suppression of torsional oscillations

Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications

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