Decentralized linear quadratic systems with major and minor agents and nonGaussian noise
Abstract
A decentralized linear quadratic system with a major agent and a collection of minor agents is considered. The major agent affects the minor agents, but not vice versa. The state of the major agent is observed by all agents. In addition, the minor agents have a noisy observation of their local state. The noise processes is \emph{not} assumed to be Gaussian. The structures of the optimal strategy and the best linear strategy are characterized. It is shown that major agent's optimal control action is a linear function of the major agent's MMSE (minimum mean squared error) estimate of the system state while the minor agent's optimal control action is a linear function of the major agent's MMSE estimate of the system state and a "correction term" which depends on the difference of the minor agent's MMSE estimate of its local state and the major agent's MMSE estimate of the minor agent's local state. Since the noise is nonGaussian, the minor agent's MMSE estimate is a nonlinear function of its observation. It is shown that replacing the minor agent's MMSE estimate by its LLMS (linear least mean square) estimate gives the best linear control strategy. The results are proved using a direct method based on conditional independence, commoninformationbased splitting of state and control actions, and simplifying the perstep cost based on conditional independence, orthogonality principle, and completion of squares.
 Publication:

arXiv eprints
 Pub Date:
 April 2020
 DOI:
 10.48550/arXiv.2004.11856
 arXiv:
 arXiv:2004.11856
 Bibcode:
 2020arXiv200411856A
 Keywords:

 Electrical Engineering and Systems Science  Systems and Control;
 Computer Science  Multiagent Systems;
 Computer Science  Robotics
 EPrint:
 16 pages, submitted to the IEEE Transactions on Automatic Control