Optimal Control for Mobile Agents Considering State Unpredictability
Chendi Qu, Jianping He, J. Li, Xiaoming Duan, Yilin Mo · IEEE Transactions on Automatic Control · 2023
This paper studies the optimal control for mobile agents, aiming at achieving a trade-off between the control performance and state unpredictability over a long time horizon. The main challenge lies in incorporating the state unpredictability requirement into the optimization problem and generalizing the algorithm to various models. Utilizing random perturbations to maximize the attackers' prediction errors of future states, we formulate the problem as a multi-period convex stochastic optimization problem and solve it via dynamic programming. We design the State unPredictable Optimal Control (SPOC) algorithm for both unconstrained and input-constrained systems. Moreover, we extend the algorithm to nonlinear affine systems by linearization. The analytical iterative expressions of the control inputs are further provided. Simulation illustrates that the algorithm increases the prediction errors under Kalman filter while satisfying the control performance requirements successfully.