Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding surfaces, and backstepping are typically employed to advise adaptive control strategies. The resulting solutions are often challenged by the complexity of the reference model and those of the derived control strategies. Additionally, the explicit dependence of the control strategies on the process dynamics and reference dynamical models may contribute in degrading their efficiency in the face of uncertain or unknown dynamics. A model-reference adaptive solution is developed here for autonomous systems where it solves the Hamilton-Jacobi-Bellman equation of an error-based structure. The proposed approach describes the process with an integral temporal difference equation and solves it using an integral reinforcement learning mechanism. This is done in real-time without knowing or employing the dynamics of either the process or reference model in the control strategies. A class of aircraft is adopted to validate the proposed technique.

翻译：模型参考自适应系统是一类指导被控对象跟踪期望参考轨迹的技术集合。通常采用基于李雅普诺夫理论、滑模面和反步法等方法设计自适应控制策略，但这些方案的实现常受限于参考模型复杂度及推导的控制策略复杂性。此外，控制策略对过程动力学和参考动力学模型的显式依赖，可能降低其在不确定性或未知动力学条件下的效能。本文针对自主系统提出了一种模型参考自适应方案，该方案通过求解基于误差结构的Hamilton-Jacobi-Bellman方程实现。所提方法采用积分时间差分方程描述过程，并利用积分强化学习机制进行实时求解——无需在控制策略中获知或利用过程动力学及参考模型动力学信息。通过一类飞行器实例验证了该技术的有效性。