Direct digital control, auto-tuning and supervision using fuzzy logic

Ojeda, Alfredo Ollero; García-Cerezo, Alfonso

doi:10.1016/0165-0114(89)90076-6

Cited by 71 publications

(10 citation statements)

References 20 publications

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“…The hardware cost reduction coefficient for some fuzzy inference systems (used as benchmarks) is presented in Table 1. The knowledge bases of the systems describe respectively fuzzy controllers (denominated as 1, 3, 4) (Baturone et al, 1997;Kim and Cho, 1999;Yager and Filev, 1994), an ENOR gate (denominated as 2) (Lee et al, 1995), a truck park controller (denominated as 5) (Rutkowska et al, 1997;Kim, 2000), a temperature controller of a heated air-stream (denominated as 6) (Ollero and Garcia-Cerezo, 1989), a fuzzy controller for stabilization of an inverted pendulum (denominated as 7) (Yamakawa, 1989), a fan controller (denominated as 8) (Hurdon, 1993) and a fuzzy system for identification of nonlinear systems (denominated as 9) (Rovatti et al, 1995). For the primary decomposition technique (p = 1), the hardware cost is lower if the system is built as a hierarchical structure, and it is the highest for FATI systems.…”

Section: Comparison Of the Primary And The Decomposed Model Of Fuzzy mentioning

confidence: 99%

Decomposition of the fuzzy inference system for implementation in the FPGA structure

Hrynkiewicz¹

2013

International Journal of Applied Mathematics and Computer Science

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The paper presents the design and implementation of a digital rule-relational fuzzy logic controller. Classical and decomposed logical structures of fuzzy systems are discussed. The second allows a decrease in the hardware cost of the fuzzy system and in the computing time of the final result (fuzzy or crisp), especially when referring to relational systems. The physical architecture consists of IP modules implemented in an FPGA structure. The modules can be inserted into or removed from the project to get a desirable fuzzy logic controller configuration. The fuzzy inference system implemented in FPGA can operate with a much higher performance than software implementations on standard microcontrollers.

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Section: Comparison Of the Primary And The Decomposed Model Of Fuzzy mentioning

confidence: 99%

Decomposition of the fuzzy inference system for implementation in the FPGA structure

Hrynkiewicz¹

2013

International Journal of Applied Mathematics and Computer Science

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“…Fuzzy control has been employed with success in many diverse practical applications: control of a cement kiln [8], 2-dimensional motion control [9], traffic control [22] and temperature control of air streams [18] to name but a few. Based on Zadeh's theory of fuzzy sets [32], a typical fuzzy controller *Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Evolving fuzzy rule based controllers using genetic algorithms

Carse

Fogarty

Munro

1996

Fuzzy Sets and Systems

203

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The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (P-FCS1) is proposed. P-FCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rule-sets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using P-FCS l are reported and compared with other published results. Application of P-FCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented.

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“…To translate the closed-loop system equation to a vector form. Define On the basis of sliding mode control [ l 11, we define the sliding surface (4) where Z=[cl, C?,'.., Cn-', llT is the sliding surface coefficient vector prespecified by designer. In the design of sliQng mode controller an equivalent control is gwen so that the states can be slided on sliding surface while states reach to S [ 111.…”

Section: Smc-based Fuzzy Controllermentioning

confidence: 99%

“…where the fuzzy sets zero (20) and nonzero (NZ); input variable S is given in (4). The control rule (9) states that if the value S is zero then the control law of fuzzy controller is dominated by the equivalent control ; , , .…”

Section: Construct the Fuzzy Controllermentioning

confidence: 99%

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Design of a SMC-based fuzzy controller for nonlinear systems

Chen¹

Proceedings of 6th International Fuzzy Systems Conference

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In this paper, a fuzzy controller based on sliding mode control (SMC) is proposed. The control strategy of the proposed sliding mode control is characterized by the linguistic terms in the fuzzy control rules. All membership functions offuzzy control rules are well defined to guarantee the stability ofcontrol systems. The design strategy relies on the following stages: First, develops an adaptive law to approximate the equivalent control of sliding mode control, while the mathematical model of nonlinear system is unknown. Second. adding a hitting control term to translate arbitray state to a prespecIfied sliding surface.Finally, a novel fizzy controller with two fuzzy control rules, which not only applies the sliding mode control rationale but also guarantees the stability of fuzzy control system, are constructed. A nonlinear simulation example is applied to confrm the validity of the fuzzy controller.

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Direct digital control, auto-tuning and supervision using fuzzy logic

Cited by 71 publications

References 20 publications

Decomposition of the fuzzy inference system for implementation in the FPGA structure

Decomposition of the fuzzy inference system for implementation in the FPGA structure

Evolving fuzzy rule based controllers using genetic algorithms

Design of a SMC-based fuzzy controller for nonlinear systems

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