A hybrid fuzzy logic and PID controller for control of nonlinear HVAC systems

Rahmati, A.; Rashidi, Fariborz; Rashidi, Mehran

doi:10.1109/icsmc.2003.1244218

Cited by 21 publications

(17 citation statements)

References 14 publications

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“…Several works [5][6][7]12,30] highlighted that standard PID controllers can be commonly used in general HVAC applications. In fact, it is shown that simple PID controllers are able to achieved interesting results based on the direct and straightforward computation of the tracking error e(t) computed as the difference between the reference and the measured values of the output, respectively, i.e., e(t) = r(t) − y(t).…”

Section: Standard Pid Controller Designmentioning

confidence: 99%

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Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

et al. 2017

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Abstract:The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically-meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC) analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model-based and data-driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

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Section: Standard Pid Controller Designmentioning

confidence: 99%

“…Different control schemes exploited classic regulation approaches, such as on-off control laws and proportional, integral and differential (PID) standard compensators [6][7][8]. These control schemes are simple with low-cost implementations, but sometimes unable to achieve accurate solutions.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

et al. 2017

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“…The first one is to use standard PI-controllers with a set of default parameters, which often leads to insufficient performance, waste of energy and unacceptable comfort violations [Rahmati, 2003]. The other approach is to use specifically developed and adapted controllers [Seidel et al, 2015], which have the drawback in a time-consuming and expensive development.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Influence of Controller Approaches on Room Thermal Behaviour A Simulation Study

Majetta

Clauß

Nytsch‐Geusen

2017

Linköping Electronic Conference Proceedings

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To control the indoor temperature of rooms two kinds of approaches are common. The first one is to use standard PI-controllers with a set of default parameters, which often leads to insufficient performance, waste of energy and unacceptable comfort violations [Rahmati, 2003]. The other approach is to use specifically developed and adapted controllers [Seidel et al., 2015], which have the drawback in a time-consuming and expensive development. Therefore, this paper investigates on finding rules and guidelines to find a suitable controller for a given room without the need of expensive controller adaption via simulation. To provide those rules a simulation study will be performed. This paper presents the first preparatory steps of this investigation, which includes the choice and development of four different room models equipped with different heating systems, which are an electrical radiator, a floor heating system, and a water supplied radiator. The authors present five types of controller models of different controller types to control the operative temperature of a room. Simulations of well-defined scenarios analyze the eligibility of the controller models regarding net energy consumption and comfort for the considered room models. First optimization results to improve the quality of the controllers are shown and further steps are explained.

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“…Typically, FLCs have been applied to HVAC systems where several criteria are individually considered [5][6][7][11][12][13][14][15][16], thermal regulation, energy consumption or comfort improvement (separately). However, these and other criteria must be considered jointly [8][9][10] in order to obtain the best global performance in terms of energy consumption, desired comfort level, air quality, system stability, etc.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems

2010

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This paper focuses on the use of multi-objective evolutionary algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems, energy performance, stability and indoor comfort requirements. This problem presents some specific restrictions that make it very particular and complex because of the large time requirements needed to consider multiple criteria (which enlarge the solution search space) and the long computation time models required in each evaluation.In this work, a specific multi-objective evolutionary algorithm is proposed to obtain more compact fuzzy logic controllers as a way of finding the best combination of rules, thus improving the system performance to better solve the HVAC system control problem. This method combines lateral tuning of the linguistic variables with rule selection. To this end, two objectives have been considered, maximizing the performance of the system and minimizing the number of rules obtained. This algorithm is based on the well-known SPEA2 but uses different mechanisms for guiding the search towards the desired Pareto zone. Moreover, the method implements some advanced concepts such as incest prevention, that help to improve the exploration/exploitation trade-off and consequently its convergence ability.The proposed method is compared to the most representative mono-objective steady-state genetic algorithms previously applied to the HVAC system control problem, and to generational and steady-state versions of the most interesting multi-objective evolutionary algorithms (never applied to this problem) showing that the solutions obtained by this new approach dominate those obtained by these methods. The results obtained confirm the effectiveness of our approach compared with the rest of the analyzed methods, obtaining more accurate fuzzy logic controllers with simpler models.

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A hybrid fuzzy logic and PID controller for control of nonlinear HVAC systems

Cited by 21 publications

References 14 publications

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

Investigation of the Influence of Controller Approaches on Room Thermal Behaviour A Simulation Study

A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems

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