This article proposes a stabilizing control scheme based on partial-state decomposition to exponentially stabilize multi-input and multioutput nonminimum phase nonlinear systems in a general normal form with a general relative degree. The scheme reveals that partial-state variables can play an essential role in stabilizing the whole system. Specifically, partial-state variables are decomposed into a sum of two vector signals$s$and$N$. Then, setting$s$as the output vector, a new normal form is derived. For the new normal form,$N$and an auxiliary signal are designed to exponentially stabilize the unstable zero/internal dynamics of the auxiliary system; and the real input is designed to ensure that$s $is exponentially stable. In particular, the zero/internal dynamics dependence on the input is fully considered in this article, and the proposed method ensures global stabilization of the closed-loop system without relying on the existence condition of Lyapunov functions. Finally, a high fidelity hypersonic vehicle model is given to show the design procedure and verify the feasibility and validity of the proposed stabilizing control scheme.

Making rational decisions for sequential decision problems in complex environments has been challenging researchers in various fields for decades. Such problems consist of state transition dynamics, stochastic uncertainties, long-term utilities, and other factors that assemble high barriers including the curse of dimensionality. Recently, the state-of-the-art algorithms in reinforcement learning studies have been developed, providing a strong potential to efficiently break the barriers and make it possible to deal with complex and practical decision problems with decent performance. We propose a formulation of a velocity varying one-on-one quadrotor robot game problem in the three-dimensional space and an approximate dynamic programming approach using a projected policy iteration method for learning the utilities of game states and improving motion policies. In addition, a simulation-based iterative scheme is employed to overcome the curse of dimensionality. Simulation results demonstrate that the proposed decision strategy can generate effective and efficient motion policies that can contend with the opponent quadrotor and gather advantaged status during the game. Flight experiments, which are conducted in the Networked Autonomous Vehicles (NAV) Lab at the Concordia University, have further validated the performance of the proposed decision strategy in the real-time environment.