Fast Tuning of the PID Controller in An HVAC System Using the Big Bang–Big Crunch Algorithm and FPGA Technology

Almabrok, Abdoalnasir; Psarakis, Mihalis; Dounis, Anastasios I.

doi:10.3390/a11100146

Cited by 32 publications

(14 citation statements)

References 23 publications

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“…ITSE = t. e(t) 2 .dt (36) Equations (33)-(36) present the Integral of the Square Error (ISE), the Integral of Absolute Error (IAE), the Integral of the Time-weighted Absolute Error (ITAE), and the Integral of the Time Square Error (ITSE), respectively [44]. As already announced, the control of PMSM requires the use of five control block for both of currents and rotor speed, with an adjustment of a significant number of gains.…”

Section: Simulation Resultsmentioning

confidence: 99%

ADRC-Based Robust and Resilient Control of a 5-Phase PMSM Driven Electric Vehicle

et al. 2020

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The selection of electric machines for an Electric Vehicle (EV) is mainly based on reliability, efficiency, and robustness, which makes the 5-phase Permanent Magnet Synchronous Motor (PMSM) among the best candidates. However, control performance of any motor drive can be deeply affected by both: (1) internal disturbances caused by parametric variations and model uncertainties and (2) external disturbances related to sensor faults or unexpected speed or torque variation. To ensure stability under those conditions, an Active Disturbance Rejection Controller (ADRC) based on an online dynamic compensation of estimated internal and external disturbances, and a Linear ADRC (LADRC) are investigated in this paper. The control performance was compared with traditional controller and evaluated by considering parametric variation, unmodeled disturbances, and speed sensor fault. The achieved results clearly highlight the effectiveness and high control performance of the proposed ADRC-based strategies.

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Section: Simulation Resultsmentioning

confidence: 99%

ADRC-Based Robust and Resilient Control of a 5-Phase PMSM Driven Electric Vehicle

et al. 2020

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“…To study this, the parameters of the controller are tuned by minimizing the ITAE of ACE as objective function. The ITAE is considered as the cost function to reduce overshoots and oscillations of system response, where it offers quicker settling time than any other performance criterion [36], [37]. Nonetheless, the analysis of frequency stability of HPS is limited in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Matignon’s Theorem Based Stability Analysis of Hybrid Power System for Automatic Load Frequency Control Using Atom Search Optimized FOPID Controller

et al. 2020

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The large-scale penetration of intermittent renewable energy (RE) sources such as wind and solar power generation may cause a problem of frequency aberration, power quality and instability to the system. This occurs when the load frequency control of interconnected system is unable to compensate the power balance between generation and load demand. To overcome these issues, this paper proposes a method to analyze the frequency stability of Hybrid Power System (HPS) through adaptive intelligent control techniques for delivering reliable power supply under the stochastic nature of RE and load demand. A recently developed physics-inspired Atom Search Optimization algorithm was applied for tuning the parameters of Fractional Order Proportional-Integral-Derivative controller for Automatic Load Frequency control of HPS. In addition, an effort was made to analyze the frequency stability of HPS using Matignon's theorem. The interconnected HPS consists of reheat thermal power system, RE sources such as wind and solar thermal power generation associated with energy storage devices namely aqua electrolyzer, fuel cell and electric vehicle. The gain and fractional terms of the controller were obtained by minimizing the Integral Time Absolute Error of interconnected system. The robustness of ASO-tuned FOPID controller is tested on two-area HPS that was modelled using MATLAB/Simulink. The results obtained were then compared with other fractional order and classical integer order controllers. From the simulation results, it is inferred that the proposed ASO-tuned FOPID controller gives superior transient and steady-state response compared with other controllers. Moreover, the self-adaptiveness and robustness of the controller was validated to account for the change in RE power generations and system parameters. Furthermore, the effectiveness of the method is proved by comparing its performance with the recent literature works. The real-time applicability of proffered controller is validated in hardware-in-the-loop simulation using Real Time Digital Simulator.

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“…In spite of all recent advances in control technology, the old proportional-integral-derivative (PID) algorithm is still by far the most widely used in practice due to its simplicity, feasibility, and capacity to control almost all plant types [1][2][3]. However, for many complex processes, especially with overshoot, time delay, non-minimum phase, and/or non-linear characteristics, the PID algorithm cannot achieve good and very good control performances.…”

Section: Introductionmentioning

confidence: 99%

A Practical Unified Algorithm of P-IMC Type

Cirtoaje

2020

Processes

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The paper presents a practical algorithm of the proportional-internal model control (P-IMC) type that can be applied to control a wide class of processes: Stable proportional processes, integral processes and some unstable processes. The P-IMC algorithm is a suitable combination between the P0-IMC algorithm and the P1-IMC algorithm, which are characterized by a too weak and a too strong impact of the tuning gain on the control action, respectively. The overall controller has five parameters: A tuning parameter K, three model parameters (steady-state gain, settling time, and time delay) and a process feedback gain used only for integral or unstable processes, to turn them into a compensated process (stable and of proportional type). For a step setpoint, the initial value of the compensated process input is approximately K times its final value. Furthermore, for K = 1 , the compensated process input is close to a step shape (step control principle). These properties enable a human operator to check and adjust online the model parameters. Due to its control performance, robustness to modeling error, and capability to be easily tuned and applied for all industrial processes, the P-IMC algorithm could be a viable alternative to the known PID algorithm. Numerical simulations are given to highlight the performance and the flexibility of the algorithm.

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Fast Tuning of the PID Controller in An HVAC System Using the Big Bang–Big Crunch Algorithm and FPGA Technology

Cited by 32 publications

References 23 publications

ADRC-Based Robust and Resilient Control of a 5-Phase PMSM Driven Electric Vehicle

ADRC-Based Robust and Resilient Control of a 5-Phase PMSM Driven Electric Vehicle

A Matignon’s Theorem Based Stability Analysis of Hybrid Power System for Automatic Load Frequency Control Using Atom Search Optimized FOPID Controller

A Practical Unified Algorithm of P-IMC Type

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