Authors:
Bei Kang
;
Chukwuemeka Aduba
and
Chang-Hee Won
Affiliation:
Temple University, United States
Keyword(s):
Statistical Optimal Control, Cumulant Minimization, Neural Networks, Cost Cumulants, Performance Shaping.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Optimization Algorithms
Abstract:
The performance shaping method is addressed as a statistical optimal control problem. In statistical control, we shape the distribution of the cost function by minimizing n-th order cost cumulants. The n-th cost cumulant, Hamilton-Jacobi-Bellman (HJB) equation is derived as the necessary condition for the optimality. The proposed method provides an approach to control a higher order cost cumulant for stochastic systems, and generalizes the traditional linear-quadratic-Gaussian and Risk-Sensitive control methods. This allows the cost performance shaping via the cost cumulants. Moreover, the solution of general n-th cost cumulant control is provided by numerically solving the HJB equations using neural network method. The results of this paper are demonstrated through a satellite attitude control example.