The generation of a sequence of control actions to move a system from an initial state to a final one is an ill-posed problem because the solution is not unique. Soft constraints like the minimization of a cost associated to control actions makes the problem mathematically solvable in the framework of optimal control theory. We present here a method to approximate the solution of the problems of this category based on heuristic dynamic programming proposed by Werbos: local dynamic programming. Its main features are the exploration of a volume around the actual trajectory and the introduction of a set of correcting functions. Its application to the generation of a trajectory whose kinematics is minimum jerk is presented," in this situation, the introduction of a short-term temporal credit assignment improves the convergence tackling the lack of controllability in the plant.

Generation of Temporal Sequences using Local Dynamic Programming / N.A. Borghese, M.Arbib. - In: NEURAL NETWORKS. - ISSN 0893-6080. - 8:1(1995), pp. 39-54. [10.1016/0893-6080(94)00053-O]

Generation of Temporal Sequences using Local Dynamic Programming

N.A. Borghese
Primo
;
1995

Abstract

The generation of a sequence of control actions to move a system from an initial state to a final one is an ill-posed problem because the solution is not unique. Soft constraints like the minimization of a cost associated to control actions makes the problem mathematically solvable in the framework of optimal control theory. We present here a method to approximate the solution of the problems of this category based on heuristic dynamic programming proposed by Werbos: local dynamic programming. Its main features are the exploration of a volume around the actual trajectory and the introduction of a set of correcting functions. Its application to the generation of a trajectory whose kinematics is minimum jerk is presented," in this situation, the introduction of a short-term temporal credit assignment improves the convergence tackling the lack of controllability in the plant.
Adaptation; Controllability; Convergence; Critic; Dynamic programming; Hierarchy; Neuro-control; Optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/58134
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