Designing Intelligent MIMO Nonlinear Controller Based on Fuzzy Cognitive Map Method for Energy Reduction of the Buildings

Abstract

Designing a suitable controller for air-conditioning systems to reduce energy consumption and simultaneously meet the requirements of the system is very challenging. Important factors such as stability and performance of the designed controllers should be investigated to ensure the effectiveness of these controllers. In this article, the stability and performance of the fuzzy cognitive map (FCM) controller are investigated. The FCM method is used to control the direct expansion air conditioning system (DX A/C). The FCM controller has the ability to do online learning, and can achieve fast convergence thanks to its simple mathematical computation. The stability analysis of the controller was conducted using both fuzzy bidirectional associative memories (FBAMs) and the Lyapunov function. The performances of the controller were tested based on its ability for reference tracking and disturbance rejection. On the basis of the stability analysis using FBAMS and Lyapunov functions, the system is globally stable. The controller is able to track the set point faithfully, maintaining the temperature and humidity at the desired value. In order to simulate the disturbances, heat and moisture load changed to measure the ability of the controller to reject the disturbance. The results showed that the proposed controller can track the set point and has a good ability for disturbance rejection, making it an effective controller to be employed in the DX A/C system and suitable for a nonlinear robust control system. Nowadays, the use of different controllers on HVAC systems is considered as an important issue, with the aim of increasing the system performance, by reason of their high installation demand in buildings and their huge energy consumption.The direct expansion air-conditioning (DX A/C) system is considered as a subgroup of HVAC systems. The mentioned system has two types of units, window units and split units, which are frequently employed in small-to medium-sized buildings by reason of having a simple configuration, low cost maintenance, and higher energy efficiency [3,4].According to the authors of [2], energy efficacy and indoor thermal conditions are the main objectives one needs to take into account when designing HVAC or A/C systems. Because of the complicated features of HVAC and A/C systems, attaining the mathematical model of HVAC and air conditioning systems is intricate and difficult. Likewise, designing an appropriate controller becomes a big challenge [5,6]. HVAC system is a multiple-Input and multiple-output (MIMO) system, sometimes with coupled parameters [7]. Moreover, it is a complex nonlinear system, which makes deriving the exact mathematical model a challenging task [3]. Consequently, the control system must be able to deal simultaneously with cross-coupling effects, nonlinearity, and uncertainty of the system. The high energy consumption of these systems calls for an intelligent control system that can adjust the parameters according to the systems' demands, so that it could prevent energy loss and b...

Noise as a Factor of Green Areas Soundscape Creation

“…Therefore, it can support the decision-making process and affect the achievement of optimal results of the conducted spatial policy in the field of sustainable socio-economic development [34]. Methods based on fuzzy cognitive maps are used to control and plan the optimal work of engineering structures [35], to design transfer and financial systems [36], design intelligent solutions to reduce energy consumption in buildings [36], or to optimize urban policy development [37]. As emphasized [37,38], Fuzzy Cognitive Maps (FCM) are a method well adapted to solving problems related to sustainable urban development.…”

“…Methods based on fuzzy cognitive maps are used to control and plan the optimal work of engineering structures [35], to design transfer and financial systems [36], design intelligent solutions to reduce energy consumption in buildings [36], or to optimize urban policy development [37]. As emphasized [37,38], Fuzzy Cognitive Maps (FCM) are a method well adapted to solving problems related to sustainable urban development. These problems are uncertain, and the information is usually incomplete and confusing to model and does not lead directly to the optimal solution.…”

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

“…Figure 9 presents the maximum computation time G Tmax changes for each objective function with respect to N. In both load cases, the maximum computation time for Obj1-3 has similar incrementally increasing tendencies as N increases: when N ≤ 10, the computation time is less than 0.2 s; when 10 < N ≤ 15, the computation time is less than 1s; and when 15 < N, the computation time will go above 1 s with the maximum value of less than 8 s. This indicates that the proposed MPC method could be adopted for real-time applications with selected sample times T s larger than 10 s, during which the optimization would be completed, with the additional time being used for data measuring and communicating [49]. It is worth noting that a maximum computation time can be set in CPLEX.…”

“…tions with selected sample times Ts larger than 10 s, during which the optimization would be completed, with the additional time being used for data measuring and communicating [49]. It is worth noting that a maximum computation time can be set in CPLEX.…”

Intelligent Control Based on Usage Habits in a Domestic Refrigerator with Variable Speed Compressor for Energy-Saving

“…Control plays a primary role in the operation of the refrigeration system, both to maintain operating conditions and to reduce energy consumption. Various novel control methods have been implemented in refrigeration systems, allowing the controller to perform more robustly [15][16][17][18]. Among the control strategies, control systems based on fuzzy logic present attractive advantages over conventional controls, thus achieving energy improvements, robustness, and rapid response under dynamic operating conditions [19].…”