Abstract
AbstractThis paper proposes an inverse optimality design method for nonlinear deterministic system and nonlinear stochastic system with multiplicative and additive noises. The new method is developed based on the general Hamilton-Jacobi-Bellman(HJB) equation, and it constructs an estimated cost function using a linear function approximator—Gaussian Radial Basis function, which can recover the original performance criterion for which the given control law is optimal. The performance of the algorithm is illustrated on scalar systems and a complex biomechanical control problem involving a stochastic model of the human arm.
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