Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

作     者：Nhan T.Nguyen Sivasubramanya N.Balakrishnan 

作者机构：the NASA Ames Research CenterMoffett Field the Missouri University of Science and Technology 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2014年第1卷第4期

页      面：423-434页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 08[工学] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Optimal control Adaptive control Uncertainty Adaptation models Robustness Cost function NASA Learning (artificial intelligence) 

摘      要：This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and the predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and the predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.