A new reinforcement learning algorithm is introduced which can be applied over a continuous range of actions. Reinforcement learning benefits over other types of learning systems in requiring no dedicated training and evaluation phases of learning; instead, such systems progressively adapt to the given environment. The learning algorithm is reward-inaction based, with a set of probability density functions being used to determine the action set. The learning sub-system sends an action or set of actions to the environment which then returns a scalar value, via the performance evaluation function, indicating the quality of that action. The performance evaluation function encodes the explicit goals and objectives of the learning system and returns a scalar value, indicating the quality of the applied actions. An experimental study is presented, based on the control of a semi-active suspension system on a road-going, four wheeled, passenger vehicle. The control objective is to minimize the mean square acceleration of the vehicle body, thus improving the ride isolation qualities of the vehicle. This represents a difficult class of learning problems, owing to the stochastic nature of the road input disturbance together with unknown high order dynamics, sensor noise and the non-linear (semi-active) control actuators. The learning algorithm described here operates over a bounded continuous action set, is robust to high levels of noise and is ideally suited to operating in a parallel computing environment. A standard passenger vehicle fitted with continuously variable controllable semi-active dampers and wheel and body accelerometers was mounted on a servo-hydraulic four-poster road simulator. The learning methodology was applied to the problem of improving the ride comfort of the vehicle with a performance objective of reducing the vertical body accelerations. Suspension deflection, wheel velocity and body velocity at each corner of the vehicle were measured and used for feedback control.


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    Titel :

    Continuous action reinforcement learning applied to vehicle suspension control


    Weitere Titelangaben:

    Zeitkontinuierliche lernende Schwingungsregelung einer Radaufhängung


    Beteiligte:
    Howell, M.N. (Autor:in) / Frost, G.P. (Autor:in) / Gordon, T.J. (Autor:in) / Wu, Q.H. (Autor:in)

    Erschienen in:

    Mechatronics ; 7 , 3 ; 263-276


    Erscheinungsdatum :

    1997


    Format / Umfang :

    14 Seiten, 10 Bilder, 1 Tabelle, 10 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




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