A Hybrid Approach for Design of Stable Adaptive Fuzzy Controllers Employing Lyapunov Theory and Particle Swarm Optimization

Sharma, Kaushik Das; Chatterjee, Amitava; Rakshit, Anjan

doi:10.1109/tfuzz.2008.2012033

Cited by 96 publications

(57 citation statements)

References 21 publications

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“…3 In HS algorithm based system identification, a candidate solution vector in solution space is a vector containing all required information to obtain the model of the air heater process. This candidate vector Z is formed as [14,15]:…”

Section: Modeling and Parameter Estimation Of Air Heater Processmentioning

confidence: 99%

“…(ii) The tracking error e(t) should be as small as possible under the constraints in (i) and the e − Â space should be stable in the large for the system. Now the ideal control law for the system in (5) and a reference signal y m (t) is given as [12,14,17]:…”

Section: Design Of Stable Adaptive Fuzzy Controllers For Air Heater Pmentioning

confidence: 99%

“…Thus a suitable solution can be to design a fuzzy logic system to approximate this optimal control law. Now, to ensure the stability, it has been assumed that the control u(t) is given by the summation of a fuzzy control, u c ( x Â), and an additional supervisory control u s (x) [12,14]. Thus u(t) is given as:…”

Section: Design Of Stable Adaptive Fuzzy Controllers For Air Heater Pmentioning

confidence: 99%

“…5(a) [12,14,17], is utilized under the supervision of the HS algorithm based approach to design stable fuzzy controller to get a superior systematic procedure for a class of SISO systems. Now the adaptation law as in (11) is revised in light of the Lyapunov theory based adaptation of the output singletons Â as [12]:…”

Section: Hybrid Strategy Based Approach (Hydsba)mentioning

confidence: 99%

“…The air mass flow rate is primarily dependent on the blower speed, thus a change in that speed effectively acts as a load disturbance, influencing the heat transfer dynamics of the air heater process. In this work, our designed hybrid stable adaptive fuzzy controllers (AFLCs), which combine the good features of Lyapunov theory [12] and harmony search (HS) optimization based strategies to evolve superior control alternatives [14], [15], are applied to effectively control the temperature of this air heater process at different cross sections of the air flow duct. These proposed hybrid controllers can effectively control the temperature of a process with such variable heat transfer dynamics and the same controllers are also able to cope with the transportation delay inherently present in the process due to the physical separation between the heating element and temperature sensors.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Harmony search algorithm and Lyapunov theory based hybrid adaptive fuzzy controller for temperature control of air heater system with transport-delay

Sharma

Chatterjee

Rakshit

2014

Applied Soft Computing

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Section: Modeling and Parameter Estimation Of Air Heater Processmentioning

confidence: 99%

Section: Design Of Stable Adaptive Fuzzy Controllers For Air Heater Pmentioning

confidence: 99%

Section: Design Of Stable Adaptive Fuzzy Controllers For Air Heater Pmentioning

confidence: 99%

Section: Hybrid Strategy Based Approach (Hydsba)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Harmony search algorithm and Lyapunov theory based hybrid adaptive fuzzy controller for temperature control of air heater system with transport-delay

Sharma

Chatterjee

Rakshit

2014

Applied Soft Computing

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Sensitivity‐based fuzzy multi‐objective portfolio model with Value‐at‐Risk

Zhang

Watada

Wang

2019

IEEJ Transactions Elec Engng

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To quantitatively discuss the effects and uncertainties of yield changes in each stock for portfolio selection results and then to provide a more reliable portfolio solution for investors, sensitivity analysis is introduced to improve the multi-objective portfolio model with fuzzy VaR (SA-VaR-FMOPSM). Compared with the existing fuzzy VaR multi-objective portfolio model (VaR-FMOPSM), the calculation formulas of expected and VaR value of parabolic fuzzy variable are derived when stock yields are set as a more generalized parabolic fuzzy variable as well as the sensitivity of the total objective to the VaR-FMOPSM model is defined. Meanwhile, based on the fuzzy simulation technique, the model adapted to a series of different distributed fuzzy variable, an improved particle swarm optimization algorithm (IPSO) is used for numerical simulations. To illustrate the proposed model, New York Stock Exchange and National Association of Securities Dealers Automated Quotations-Global Select Market (NASDAQ-GS) stock data were selected for empirical testing, and to better reflect the true value of each stock for each day, we selected the ex-right price data in the experiment, a comparison with the existing fuzzy VaR multi-objective portfolio model (VaR-FMOPSM) is performed. The results show that the fuzzy VaR multi-objective portfolio model based on the sensitivity analysis method can effectively identify and quantitatively analyze the sensitivity of individual stocks to yield changes, i.e. that can identify which stock has more stable yields and that can calculate the degree of stability, then to obtain stable portfolio solutions. In addition, compared with the VaR-FMOPSM model, our sensitivity-based improved model with the IPSO algorithm also performs better than Genetic Algorithm and Simulate Anneal Algorithm (SA), it provides the same performance on this point. . His research interests include portfolio selection, power system planning, economics analysis, fuzzy set theory, and metaheuristic algorithms.

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