Tuning Fuzzy Controller Using Approximated Evaluation Function

Naba, Agus; Miyashita, Kazuo

doi:10.1007/3-540-32391-0_19

Cited by 2 publications

(4 citation statements)

References 2 publications

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“…They are not applicable to plants that cannot be represented by the assumed model structure. Previous research on the same issue as addressed in this paper is reported in [15][16][17][18]. Although the proposed systems work, their performance is poor.…”

Section: Introductionmentioning

confidence: 93%

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Evaluation-Function-based Model-free Adaptive Fuzzy Control

Naba

2016

J. Eng. Technol. Sci.

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Abstract. Designs of adaptive fuzzy controllers (AFC) are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC) using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark verified the proposed scheme's efficacy. Keywords: adaptive fuzzy control; evaluation function; Lyapunov approach; modelfree adaptive control; reinforcement learning. IntroductionMany designs of adaptive fuzzy controllers (AFC) are based on the Lyapunov approach, assuming the existence of a Lyapunov function for the control problem to be solved. The Lyapunov function getting smaller is seen as an indication of better control performance. The most important stage in the Lyapunov approach is to ensure that the first derivative of the Lyapunov function candidate (LFC) is either negative-definite or semi-definite [1][2][3][4]. This stage requires a known plant model or, at least, a known plant model structure.The regular approach in model-based design of AFCs is that the engineer a priori defines the LFC simply in terms of the distance between the current state and the goal state. A smaller error is always assumed to indicate better control performance. However, in many control problems a smaller error does not indicate better control performance, i.e. it is not always evaluative. Thus, action selection based on smaller errors can misdirect and lead to non-goal states. Generally speaking, errors are more instructional than evaluative [5]. For this reason, they are not universally suitable as an evaluative measure of control performance. This is a flaw in model-based design of AFCs.

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Section: Introductionmentioning

confidence: 93%

“…(21) with failure detector Eq. (22) has been applied in our previous researches [13][14][15][16]. Its performance suffers since the failure detector † 3 is defined using only two possible values, i.e.…”

Section: Proposed Update Rule With Lyapunov Approachmentioning

confidence: 99%

Evaluation-Function-based Model-free Adaptive Fuzzy Control

Naba

2016

J. Eng. Technol. Sci.

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“… , its parameter vector is of the size of 2. For the fuzzy controller, we define five Gaussian membership functions (i.e., 12 5 pp  ), with the centers at {-30,-20,0,20,30} deg and {-60,-30,0,30,60} deg/s, respectively, and standard deviations: {20,10,10,10,20} deg and {30,15,10,15,30} deg/s, respectively. Beyond the range [-30,30] deg for  and [-60,60] deg/s for  , the state variables will be assigned with the maximum degree of membership (i.e., 1).…”

Section: Figurementioning

confidence: 99%

“…This makes the control design inflexible for certain plants which are difficult to be represented by the assumed structure of the plant model. A preliminary research on adaptive control based on approximate evaluation function has been reported in [12][13][14]. But, it only focuses on solving adaptive control problem using a fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control with Approximated Policy Search Approach

Naba¹

2010

itbj.eng.sci.

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Most of existing adaptive control schemes are designed to minimize error between plant state and goal state despite the fact that executing actions that are predicted to result in smaller errors only can mislead to non-goal states. We develop an adaptive control scheme that involves manipulating a controller of a general type to improve its performance as measured by an evaluation function. The developed method is closely related to a theory of Reinforcement Learning (RL) but imposes a practical assumption made for faster learning. We assume that a value function of RL can be approximated by a function of Euclidean distance from a goal state and an action executed at the state. And, we propose to use it for the gradient search as an evaluation function. Simulation results provided through application of the proposed scheme to a pole-balancing problem using a linear state feedback controller and fuzzy controller verify the scheme’s efficacy

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Tuning Fuzzy Controller Using Approximated Evaluation Function

Cited by 2 publications

References 2 publications

Evaluation-Function-based Model-free Adaptive Fuzzy Control

Evaluation-Function-based Model-free Adaptive Fuzzy Control

Adaptive Control with Approximated Policy Search Approach

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