Presidential Young Investigators Award: Robustness and Feedback Systems

他未来的研究计划分为三大领域，并概述了 下面 I. 并行算法。 他的主要目标是发展 高效的多处理器算法的解决方案，大，稀疏 李雅普诺夫和黎卡提方程。 这样的方程通常出现在 大柔性系统的控制、稳定性分析及模型降阶 高阶、带状、稀疏有限元模型 往往是可用的。 有效的李雅普诺夫和 Riccati解算器将有助于获得低阶近似， 这些复杂的模型。 这些简化的模型将发挥关键作用， 在柔性的仿真和反馈控制的背景下， 结构. 二. 自适应鲁棒控制 物理植物模型本质上是 不准确且包含参数和动态建模 不确定性，往往涉及大量的参数。 在许多 在某些情况下，可能有可能将可能的植物集合分割成碎片， 模型转换为更小、更易于管理的“操作点”， 局部鲁棒LTI控制器可以很容易地设计，使用， 例如，H -最优控制方法。 所有的问题 然后，控制对象简化为自适应的“增益调度”， 在这些强大的控制器之间。 在这方面， 一个适应方案，均匀的输入输出稳定 不确定的植物 然而，还有许多工作要做，特别是在 获得可接受的瞬态响应和干扰的背景 排斥反应 三. 非线性反馈 这项研究的动机是 基本问题，如：什么可以和不可以完成 使用（任意）非线性反馈？ 当非线性 控制器明显优于线性控制器？ 回答这些 问题是至关重要的，因为它们可以控制 系统工程师选择或消除广泛的设计类别 具体设计问题的方法。 他的主要研究 目标是开发定量技术，提供指导方针， 致动器-传感器重新配置和选择适当的 反馈综合法

His future research plans fall into three broad areas and are outlined below. I. Parallel Algorithms. His principal objective here is to develop efficient multiprocessor algorithms for the solution of large, sparse Lyapunov and Riccati equations. Such equations typically arise in the control, stability analysis and model reduction of large flexible structures for which high order, banded, sparse finite element models are often available. The development of efficient Lyapunov and Riccati solvers would facilitate obtaining low order approximations to these complex models. These simplified models would play a key role in the context of simulation and feedback control of flexible structures. II. Adaptive Robust Control. Physical plant models are inherently inaccurate and incorporate both parametric and dynamic modelling uncertainty, often involving a large number of parameters. In many circumstances it may be possible to fragment the set of possible plant models into smaller, more manageable "operating points" for which locally robust LTI controllers may be readily designed using, for instance, H -optimal control methods. The entire problems of controlling the plant then reduces to adaptively "gain-scheduling" between these robust controllers. In this connection he has provided an adaptation scheme that uniformly input-output stabilizes the uncertain plant. Much work, however, remains, especially in the context of obtaining acceptable transient responses and disturbance rejection. III. Nonlinear Feedback. The research here is motivated by fundamental questions such as: What can and cannot be accomplished by the use of (arbitrary) nonlinear feedback? When are nonlinear controllers significantly better than linear ones? Answers to these questions are vitally important because they would allow control system engineers to select or eliminate broad classes of design methodologies for specific design problems. His principal research goals are to develop quantitative techniques that offer guidelines on actuator-sensor reconfiguration and the choice of an appropriate feedback synthesis methodology.