摘要：This paper considers the adaptive tracking problem for a class of first-order systems with binary-valued observations generated via fixed thresholds. A recursive projection algorithm is proposed for parameter estimation based on the statistical properties of the system noise. Then, an adaptive control law is designed via the certainty equivalence principle. By use of the conditional expectations of the innovation and output prediction with respect to the estimates, the closed-loop system is shown to be stable and asymptotically optimal. Meanwhile, the parameter estimate is proved to be both almost surely and mean square convergent, and the convergence rate of the estimation error is also obtained. A numerical example is given to demonstrate the efficiency of the adaptive control law.

摘要：研究集值系统的辨识与适应控制,这是在随着网络化和信息化发展的过程中遇到的新兴热点问题.集值系统有着广泛的实际应用背景,但其研究才刚刚起步,主要原因是其辨识和控制的可用信息非常少,只是系统输出是否属于某个集合,从而使得已有方法无法适用.总结了近年来关于集值系统的研究工作:在辨识方面,从不同的系统结构、不同的噪声情况和不同的集值情形等方面进行了深入的研究.针对性地提出了参数解耦、比例满秩输入设计、联合可辨识、经验分布函数、在线递推等有效的辨识和控制方法,得到了一系列重要结果;在适应控制方面,实现了一类集值增益系统的适应跟踪控制.所得成果从正面回答了集值系统研究的核心科学问题:即使利用极为粗糙的集值信息,仍然可以精确地估计和控制系统,而且可以构造渐近最优的算法.