Signal processing, identification and control in switched non-linear systems

本研究涉及的分析和识别的控制系统，经历突然的参数变化，使选择替代控制方案。这种系统存在于许多生理环境中，例如：即大脑用来控制运动的神经元。由于通常的恒定特性假设（平稳性）被违反，因此需要特殊的算法来允许在突然参数变化和可变初始条件下对系统动力学进行一致的估计。因此，本研究的第一部分解决了在MATLAB模块中开发易于分发的软件工具的问题，这将有两个目的。首先，使用简化模型将系统的响应模式快速分类为潜在配置。其次，利用考虑短响应段和可变初始条件的辨识方法，将系统模型拟合到每个潜在模态。这些研究结果在CIHR资助的临床诊断和双目控制建模研究中得到了直接应用。第二个主要组成部分检查从生理运动控制建模推断出的策略（CIHR支持的工作），并将推断出的新策略移植到机器人定向系统和感觉融合问题上。例如，我们已经成功地在双目系统中使用动态模式切换实现了快速目标追踪，并且可以在没有额外控制器的情况下协调支撑“颈部”的运动。尽管控制器结构简单，但生物诱发方法提供了更快、更准确的控制。复杂的行为依赖于内部切换，这当然会引入非线性，必须在项目的设计和分析方面加以解决。这项技术正在被扩展，以允许机器人头部和手臂的感觉融合和“反射”适应环境的创新方法。

This research concerns the analysis and identification of control systems undergoing sudden parametric changes, to enable selection of alternate control schemes. Such systems exist in many physiological contexts,e.g., those used by the brain to control movement. Since usual assumptions of constant characteristics (stationarity) are violated, special algorithms are required to allow consistent estimation of system dynamics in the presence of sudden parametric changes and variable initial conditions.Hence, the first part of this research addresses the issue of developing software tools in MATLAB modules for easy distribution, which will have 2 purposes. First, fast classification of a system's response modes into potential configurations, using reduced models. Second, fitting system models to each of the potential modes using identification methods which take into account short response segments and variable initial conditions. Results here are finding direct application in CIHR funded research on clinical diagnosis and modelling of binocular control.The second major component examines the strategies deduced from the modelling of physiological motor control (CIHR supported work) and is porting so-deduced novel strategies to robot orientation systems and sensory fusion issues. For example, we have successfully implemented fast target pursuit in a binocular system using dynamic mode switching, and can coordinate motion of a supporting "neck" without additional controllers. The biologially-evoked approach offers the promise of faster and more accurate control, despite structurally simple controllers. Complex behaviour relies on internal switching which of course introduces non-linearities which must be addressed in the design and analysis aspects of the projects. This is being extended to allow innovative methods of sensory fusion and 'reflex' adaptation with context in both robotic heads and arms.