On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata

Howell, Mark; Best, Matt C.

doi:10.1016/s0967-0661(99)00141-0

Cited by 105 publications

(53 citation statements)

References 8 publications

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“…They have been used in game playing [4], [15], [16], pattern recognition [23], [47], object partitioning [36], [37], parameter optimization [7], [25], [45], [46], multiobjective analysis [22], telephony routing [24], [26], and priority assignments in a queuing system [20]. They have also been used in statistical decision making [21], [25], distribution approximation [1], natural language processing, modeling biological learning systems [57], string taxonomy [32], graph partitioning [33], distributed scheduling [51], network protocols (including conflict avoidance [39]) for LANs [40], photonic LANs [42], star networks [41], broadcast communication systems [43], dynamic channel allocation [43], tuning proportional-integral differential controllers [13], assigning capacities in prioritized networks [38], map learning [8], digital filter design [14], controlling client/server systems [44], adaptive signal processing [52], vehicle path control [58], the control of power systems [60], and vehicle suspension systems [10].…”

Section: B Stochastic Lamentioning

confidence: 99%

Modeling a Student's Behavior in a Tutorial-Like System Using Learning Automata

Oommen

Hashem²

2010

IEEE Trans. Syst., Man, Cybern. B

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Abstract-This paper presents a new philosophy to model the behavior of a student in a tutorial-like system using learning automata (LAs). The model of the student in our system is inferred using a higher level LA, referred to as a meta-LA, which attempts to characterize the learning model of the students (or student simulators), while the latter use the tutorial-like system. The meta-LA, in turn, uses LAs as a learning mechanism to try to determine if the student in question is a fast, normal, or slow learner. The ultimate long-term goal of the exercise is the following: if the tutorial-like system can understand how the student perceives and processes knowledge, it will be able to customize the way by which it communicates the knowledge to the student to attain an optimal teaching strategy. The proposed meta-LA scheme has been tested for numerous environments, including the established benchmarks, and the results obtained are remarkable. Indeed, to the best of our knowledge, this is the first published result that infers the learning model of an LA when it is externally treated as a black box, whose outputs are the only observable quantities. Additionally, our paper represents a new class of multiautomata systems, where the meta-LA synchronously communicates with the students, also modeled using LAs. The meta-LA's environment "observes" the progress of the student LA, and the response of the latter to the meta-LA actions is based on these observations. This paper also discusses the learning system implications of such a meta-LA.Index Terms-Learning automata (LAs), modeling of adaptive systems, student modeling, tutorial-like systems.

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Section: B Stochastic Lamentioning

confidence: 99%

Modeling a Student's Behavior in a Tutorial-Like System Using Learning Automata

Oommen

Hashem²

2010

IEEE Trans. Syst., Man, Cybern. B

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“…They have been used in game playing [1]- [3], pattern recognition [10], [21], object partitioning [17], [18], parameter optimization [9], [28], [35], [54] and multi-objective analysis [43], telephony routing [11], [12], and priority assignments in a queuing system [7]. They have also been used in statistical decision making [9], [31], distribution approximation [40], natural language processing, modeling biological learning systems [26], string taxonomy [56], graph partitioning [57], distributed scheduling [39], network protocols (including conflict avoidance [44]) for LANs [36], photonic LANs [45], star networks [37], broadcast communication systems [38], dynamic channel allocation [38], tuning PID controllers [30], assigning capacities in prioritized networks [32], map learning [41], digital filter design [42], controlling client/server systems [46], adaptive signal processing [49], vehicle path control [50], and even the control of power systems [51] and vehicle suspension systems [52]. The beauty of incorporating LA in any particular application domain, is indeed, the elegance of the technology.…”

Section: A Fundamentals Of Learning Automatamentioning

confidence: 99%

Generalized pursuit learning schemes: new families of continuous and discretized learning automata

Agache

Oommen

2002

IEEE Trans. Syst., Man, Cybern. B

152

163

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Abstract-The fastest learning automata (LA) algorithms currently available fall in the family of estimator algorithms introduced by Thathachar and Sastry [24]. The pioneering work of these authors was the pursuit algorithm, which pursues only the current estimated optimal action. If this action is not the one with the minimum penalty probability, this algorithm pursues a wrong action. In this paper, we argue that a pursuit scheme that generalizes the traditional pursuit algorithm by pursuing all the actions with higher reward estimates than the chosen action, minimizes the probability of pursuing a wrong action, and is a faster converging scheme. To attest this, we present two new generalized pursuit algorithms (GPAs) and also present a quantitative comparison of their performance against the existing pursuit algorithms. Empirically, the algorithms proposed here are among the fastest reported LA to date.Index Terms-Estimator algorithms, learning automata (LA), pursuit algorithms.

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“…Some methods minimise an appropriate performance index (Astrom et al 1998;Ham and Kim 1998;Kookos et al 1999;Liu and Daley 1999;Leva and Colombo 1999;He et al 2000;Howell and Best 2000;Tan et al 2000;Wang and Cluett 2000;Leva and Colombo 2001;Campi et al 2002;Panagopoulos et al 2002;Hwang and Hsiao 2002;Robbins, 2002b;Sung et al 2002;Tan et al 2002;Yang et al 2002a). Alternatively, a direct synthesis strategy may be used to determine the controller parameters.…”

Section: Analytical Techniquesmentioning

confidence: 99%

PID compensation of time delayed processes 1998-2002: a survey

O’Dwyer¹

Proceedings of the 2003 American Control Conference, 2003.

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A time delay may be defined as the time interval between the start of an event at one point in a system and its resulting action at another point in the system. Delays are also known as transport lags or dead times; they arise in physical, chemical, biological and economic systems, as well as in the process of measurement and computation. Methods for the compensation of time delayed processes may be broadly divided into proportional integral derivative (PID) based controllers, in which the controller parameters are adapted to the controller structure, and structurally optimised controllers, in which the controller structure and parameters are adapted optimally to the structure and parameters of the process model. The purpose of this paper is to extract the essence of the developments in design, tuning and implementation of PID controllers for delayed processes over the five years 1998-2002, concentrating on journal publications. The paper will provide a framework against which the literature may be viewed.

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On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata

Cited by 105 publications

References 8 publications

Modeling a Student's Behavior in a Tutorial-Like System Using Learning Automata

Modeling a Student's Behavior in a Tutorial-Like System Using Learning Automata

Generalized pursuit learning schemes: new families of continuous and discretized learning automata

PID compensation of time delayed processes 1998-2002: a survey

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