Fuzzy PID control of nonlinear plants

Petrov, Michail; Ганчев, Иван; Taneva, Albena

doi:10.1109/is.2002.1044224

Cited by 84 publications

(42 citation statements)

References 5 publications

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“…There exists some ideal controller (30) where is defined the same as in indirect adaptive control case. Let (31) Where is a known part of the controller and (32) So that is the approximation error.…”

Section: Controller Approximatormentioning

confidence: 99%

See 1 more Smart Citation

Takagi-Sugeno Fuzzy System based Stable Direct Adaptive Control of Nonlinear Systems

Bhushan¹,

Valluru²,

Singh³

2013

IJCA

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This paper proposes a novel idea for stable direct adaptive control of nonlinear systems using Lyapunov function with fuzzy approach. Stable direct adaptive control law consists of an ideal control, and a sliding mode control. Sliding mode controller is used to ensure the stability of Lyapunov function. Stability of direct adaptive control law is tested on two nonlinear systems. Non linear systems analyzed are ball beam system and cart pole system. A computer simulation is performed on nonlinear systems by using MATLAB. KeywordsFuzzy system, Lyapunov function, adaptive control, ball beam system and cart pole system.

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Section: Controller Approximatormentioning

confidence: 99%

“…Exact fuzzy modeling and optimal control [29] has been used on inverted pendulum. The nonlinear fuzzy PID [30] controller has been applied successfully in control systems with various nonlinearities. The uncertain nonlinear system [31] has been represented by uncertain Takagi-Sugeno fuzzy model structure.…”

Section: Introductionmentioning

confidence: 99%

Takagi-Sugeno Fuzzy System based Stable Direct Adaptive Control of Nonlinear Systems

Bhushan¹,

Valluru²,

Singh³

2013

IJCA

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“…But the PID controller does not prefer if the system has a higher degree of nonlinearity and uncertainty because controller designed for such system does not give a good response. To improve controller performance, an intelligent controller such as a fuzzy logic controller (FLC), neural network and neuro-fuzzy controller have been used (Yuzhen et al, 2010;Petrov et al, 2005). The conventional PID controller requires a mathematical model of the plant, however in the design of FLC dynamics of the system is not necessary.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the control industries are used intelligent and auto-tuning techniques to tune the hybrid controllers. The intelligent techniques, robust control (Shi et al, 2010), adaptive control, fuzzy logic (Petrov et al, 2005), optimal control (Prasad et al, 2011) and predictive control (Huang et al, 2012) techniques are used to design the controller. Nature inspired optimisation techniques are the modern development for engineers and researchers used to tune the controller parameters such that genetic algorithm (Ayala et al, 2012), particle swarm optimisation (Kennedy and Eberhart, 1995), ant colony optimisation (Qingdong and Guanzheng, 2007), PD type FLC (Ahmad et al, 2009) and PSO-fuzzy (Rajeswari and Lakshmi, 2010), PSO-based FLC (Khuntia and Panda, 2010) and Fuzzy sliding mode control (Jer and Cheng, 2003), etc.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-cuckoo controller for nonlinear system

Nema

Padhy

Prabhash

2017

IJSCC

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This paper presents the novel fuzzy-cuckoo controller for the nonlinear system. The scaling factor of the fuzzy logic controller (FLC) has determined by using nature inspired cuckoo search optimisation. Controller design using FLC is a model-free approach. Auto-tuning of the controller parameters have been done using cuckoo optimisation. In this design, Sugeno type's fuzzy inference system has used to design the controller for the improvement of system's performance, which takes less computational time. Sum of the integral square error (ISE) and maximum overshoot (M p ) have been taken as an objective function for optimisation purpose. Tracking problem of inverted pendulum and stability analysis of the Van der Pol oscillator (VDPO) have considered as an examples to validate the proposed design and real time experiment has also performed on single conical tank system.

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“…It is worth noting that the controlled plants in [16,[18][19][20][21][22] are linear systems rather than nonlinear systems. In [23], a neural networkbased learning method is used for tuning the parameters of the fuzzy PID controllers. However, the stability analysis is not considered.…”

Section: Introductionmentioning

confidence: 99%

fuzzy PID control synthesis for Takagi–Sugeno fuzzy systems

Cao

Gao

Lam

et al. 2016

IET Control Theory & Applications

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This paper proposes a kind of H ∞ fuzzy PID control synthesis method for Takagi-Sugeno (T-S) fuzzy systems. The basic idea of the presented method is to transform the fuzzy PID controller design problem into that of the fuzzy static output feedback (SOF) controller design. Based on an iterative linear matrix inequality (ILMI) algorithm, the fuzzy SOF control laws can be obtained. After that, the fuzzy PID controller is recovered from the fuzzy SOF controller. Simulation examples are given to show the effectiveness of the proposed method.

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Fuzzy PID control of nonlinear plants

Cited by 84 publications

References 5 publications

Takagi-Sugeno Fuzzy System based Stable Direct Adaptive Control of Nonlinear Systems

Takagi-Sugeno Fuzzy System based Stable Direct Adaptive Control of Nonlinear Systems

Fuzzy-cuckoo controller for nonlinear system

fuzzy PID control synthesis for Takagi–Sugeno fuzzy systems

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