Evaluating controller robustness using cell mapping

Papa, Mauricio; Wood, Jason; Shenoi, Sujeet

doi:10.1016/s0165-0114(99)00168-2

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…To show the effectiveness of our approach, in this section we present experimental results related to two benchmark problems (see, e.g., [28][29][30]), the linear DC motor and the nonlinear inverted pendulum. In both cases we compare the performance of initial fuzzy, numerical, updated fuzzy and hybrid controllers.…”

Section: Resultsmentioning

confidence: 99%

A framework for the automatic synthesis of hybrid fuzzy/numerical controllers

Magazzeni

2011

Applied Soft Computing

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Section: Resultsmentioning

confidence: 99%

A framework for the automatic synthesis of hybrid fuzzy/numerical controllers

Magazzeni

2011

Applied Soft Computing

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“…The stability of the fuzzy control based on experience was studied in section III. In this appendix, the performance of the controllers (based on experience and optimized) has been verified using cell mapping [28][29][30]. The state space has been partitioned into 30x20 cells.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary Optimization of a Motorcycle Traction Control System Based on Fuzzy Logic

Carrillo

Castillo

Carabias

et al. 2015

IEEE Trans. Fuzzy Syst.

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Braking and traction control systems are fundamental vehicle safety equipments. The first ones prevent the wheels from locking, maintaining, when possible, the handling of the vehicle under emergency braking. While the second ones control wheel slip when excessive torque is applied on driving wheels. The aim of this work is to develop and implement a new control model of a traction control system to be installed on a motorcycle, regulating the slip in traction and improving dynamic behavior of two-wheeled vehicles. This paper presents a novel traction control algorithm which makes use of a fuzzy logic control block. Two strategies to create the control block have been carried out. In the first one, the parameters that define the fuzzy logic controller have been tuned according to experience. In the second one, the parameters have been obtained by means of an Evolutionary Algorithm (EA) in order to design an augmented traction controller. It has been proved that the use of EA can improve the fuzzy logic based control algorithm, obtaining better results than those produced with the control tuned only by experience.

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“…This is accomplished by checking that the discretised trajectories are a satisfactory approximation of the real ones. A similar iterative approach is advocated in the well-known cell mapping approach [38,37]. If the validation fails, the whole process is repeated with a finer discretisation.…”

Section: Optimal Controller Generationmentioning

confidence: 99%

“…Unfortunately, hybrid systems often present a very complex dynamics, thus the (optimal) controller generation is harder to achieve, and the robustness cannot be obtained using simple interpolation techniques, as happens for continuous systems (e.g., see [30]), thus more complex approaches must be applied (e.g. see [38,28]).…”

Section: Introductionmentioning

confidence: 99%

CGMurphi: Automatic synthesis of numerical controllers for nonlinear hybrid systems

Penna

Intrigila

Magazzeni

et al. 2013

European Journal of Control

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In the last years, the use of controllers has become very common, thus much work is being done to create automatic controller synthesis tools. When dealing with critical systems, most of the times such controllers are required to be optimal and robust, i.e., they must achieve their goal with minimal resource consumption and be able to handle also unexpected situations. All these requirements, which are intrinsically difficult to satisfy, become even more challenging when dealing with hybrid systems, which represent a wide range of real world systems. In this paper we propose a model checking based tool, namely CGMurphi, which assists in the generation of optimal and robust numerical controllers for systems having complex dynamics, possibly hybrid systems. The tool provides a complete controller generation solution, being also able to effectively compress the controllers and encode them so that they can be directly embedded in software/hardware systems. The tool has been widely experimented with very promising results. In particular, the present paper reports the complete experimentation results relative to two academic case studies, and the preliminary achievements obtained by applying CGMurphi to an industrial critical system. © 2013 European Control Association. Published by Elsevier Ltd. All rights reserved

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Evaluating controller robustness using cell mapping

Cited by 10 publications

References 9 publications

A framework for the automatic synthesis of hybrid fuzzy/numerical controllers

A framework for the automatic synthesis of hybrid fuzzy/numerical controllers

Evolutionary Optimization of a Motorcycle Traction Control System Based on Fuzzy Logic

CGMurphi: Automatic synthesis of numerical controllers for nonlinear hybrid systems

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