Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems

Jassar, S.; Liao, Zaiyi; Zhao, Lian

doi:10.1016/j.buildenv.2008.10.002

Cited by 48 publications

(43 citation statements)

References 20 publications

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“…Recently, neuro-fuzzy networks have been demonstrated in a lot of researches such as control application and information processing [34][35][36]. Neuro-fuzzy networks have the advantages of fuzzy systems and neural networks, simultaneously: one is the inference characteristic of the fuzzy system; and the other one is the learning ability of the neural network which can be applied for the adjustment of the fuzzy rules.…”

Section: Neuro-fuzzy Network Estimatormentioning

confidence: 99%

Robust adaptive intelligent sliding model control for a class of uncertain chaotic systems with unknown time-delay

Farid

Bigdeli

2011

Nonlinear Dyn

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In this paper, a robust adaptive intelligent sliding model control (RAISMC) scheme for a class of uncertain chaotic systems with unknown time-delay is proposed. A sliding surface dynamic is appropriately constructed to guarantee the reachability of the specified sliding surface. Within this scheme, neurofuzzy network (NFN) is utilized to approximate the unknown continuous function. The robust controller is an adaptive controller used to dispel the unknown uncertainty and approximation errors. The adaptive parameters of the control system are tuned on-line by the derived adaptive laws based on a Lyapunov stability analysis. Using appropriate Lyapunov-Krasovskii (L-K) functional in the Lyapunov function candidate, the uncertainty caused by unknown time delay is compensated and the global asymptotic stability of the error dynamics system in the specified switching surface is accomplished. Finally, the proposed RAISMC system is applied to control a Hopfield neural network, Cellular neural networks, Rössler system, and to achieve synchronization between the Chen system with two time delays with Rössler system without time delay. The results are representative of outperformance of the proposed method in all cases.

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Section: Neuro-fuzzy Network Estimatormentioning

confidence: 99%

Robust adaptive intelligent sliding model control for a class of uncertain chaotic systems with unknown time-delay

Farid

Bigdeli

2011

Nonlinear Dyn

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“…On the other hand, the MPC design can use an identified nonlinear model of the process, as remarked at the end of Section 3.4. The overall methodology is based on the optimisation of the cost function of Equation (17). However, once the description of the controlled process has been available as a linear or nonlinear dynamic model, the MPC design is quite simple and straightforward.…”

Section: Control Solution Sensitivity Evaluationmentioning

confidence: 99%

“…The scheme of Figure 12 assumes that the disturbance signal d(t) = T i (t) can be measured and exploited by the MPC block. It is worth noting that in this case, the MPC design exploits a prediction horizon of N p = 10 and a control horizon of N c = 2 for the minimisation of the cost function J of Equation (17). Moreover, the weighting coefficients of this cost function J are settled to w y k = 0.1 and w u k = 1 in order to minimise possible abrupt changes of the control input u(t k ) that would increase the energy consumption and the controlled system efficiency.…”

Section: Anfis Controller E Kmentioning

confidence: 99%

“…The main advantage of ANN controllers is represented by their interesting features of automatic learning, easy adaptation and straightforward generalisation. However, more efficient solutions were proposed and based on the ANFIS tool [15][16][17][18]. In particular, in [15], ANFIS was successfully exploited as an alternative control strategy with heating, ventilation and air conditioning (HVAC) systems to achieve accurate tracking errors.…”

Section: Introductionmentioning

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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“…An overview of different inferential sensor modelling approaches is given in [7]. The most common techniques used in designing the inferential data-driven sensor are the Artificial Neural Network (ANN) [8], Fuzzy systems including clustering method [9], Partial Least Square (PLS) [10], Neuro-Fuzzy systems (NFS) [11], Principle Component Analysis (PCA) [12] and Support Vector Regression (SVR) [13,14]. Additionally, the data-driven inferential sensor can be designed using hybrid techniques [15].…”

Section: Introductionmentioning

confidence: 99%

Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach

Ghanaati¹

2017

AJCHE

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An inferential sensor is a computer program used for inferring the process variables, which are very hard to measure from the available measurement data. Measurement noises can affect the quality of the data which can be improved by wavelet denoising method. The objective of this paper is to design an inferential sensor for estimation of Benzene concentration in a typical distillation column. Selection of the most relevant input variables for estimation can improve the performance of inferential sensor which is done by Principal Component Analysis (PCA) technique. In this paper an inferential sensor is proposed based on a novel modification of the nearest neighbor distance-based clustering for developing a TakagiSugeno-Kang (TSK) fuzzy model optimized by the Particle Swarm Optimization (PSO) algorithm. The proposed technique was compared against the conventional nearest neighbor distance-based clustering approach optimized by PSO. The simulation results confirm that the designed inferential sensor based on the proposed method is more accurate even for a noisy data set.

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Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems

Cited by 48 publications

References 20 publications

Robust adaptive intelligent sliding model control for a class of uncertain chaotic systems with unknown time-delay

Robust adaptive intelligent sliding model control for a class of uncertain chaotic systems with unknown time-delay

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

Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach

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