Abstract
AbstractIn the optimal control of dynamic systems, e.g., in the control of industrial or service robots, the standard procedure is to determine first off‐line an optimal open loop control, and correct then the inevitable deviation of the trajectory or performance of the system from the prescribed values by on‐line measurement and control actions which may be very expensive. By adaptive stochastic path planning and control (ASPPC), i.e. by incorporating the available a priori and statistical information on the unknown model parameters of the dynamic system and its working environment into the off‐line and on‐line control process by means of stochastic optimization methods, the deviation between the actual and prescribed trajectory can be reduced to a large extent.
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Schedule a callMore From: ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik
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