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In cyber-physical systems, safety and availability are of utmost importance. To satisfy requirements on safety and availability, suitable supervisory controllers need to be employed. Supervisory control theory provides a foundation on which a model-based engineering method has been developed, providing guarantees on the correctness of resulting supervisory controllers with respect to the defined requirements. In this lecture, an overview will be given of the recent research projects at Eindhoven University of Technology aiming at the development of extensions to this method, and of supporting tools, giving rise to an integrated approach to the design of supervisory controllers for complex real-life systems. This includes a mathematically underpinned, straightforward and error-free path to implementation of the designed controllers. The research projects are related to the partnership with Rijkswaterstaat which is a part of the Dutch Ministry of Infrastructure and Water Management.
This talk presents recent results in nonlinear observer design and their applications in motion estimation problems ranging from wearable sensors to bicycles. First, a new observer design technique that integrates the classical high-gain observer with a novel LPV/LMI observer to provide significant advantages compared to both methods is presented. Second, the challenges in designing observers for nonlinear systems which are non-monotonic are discussed. Non-monotonic systems are commonly encountered, but popular observer design methods fail to yield feasible solutions for such systems. Hybrid observers with switched gains enable existing observer design methods to be utilized for these systems. Following the analytical observer results, some of their applications in motion estimation are presented, including a wearable device for Parkinson’s disease patients, a smart bicycle that automatically tracks the trajectories of nearby vehicles on the road to protect itself, and smart agricultural/construction vehicles that utilize inexpensive sensors for end-effector position estimation. Each application is accompanied by a video of a prototype experimental demonstration. One of these applications has been successfully commercialized through a start-up company which expects to sell over 5,000 sensor boards this year.
Mechanical motion generation and vibration suppression is fundamental to modern machines and emerging innovations. Abilities to learn and compensate for complex mechanical system and disturbance dynamics are key to synthesizing adequate control actions to achieve precision motions. Using application case studies to motivate challenges and demonstrate implementation results, I will present control methods for addressing narrowband (repetitive control, iterative learning control) and broadband (adaptive control) motions and disturbances. I will attempt to convey a common theme, controller syntheses stemming from ideas of system dynamic inversions and utilizing solutions of optimal model matching problems.
Genetic circuits control every aspect of life and thus the ability to engineer them de-novo opens exciting possibilities, from revolutionary drugs and green energy to bugs that recognize and kill cancer cells. Just like in mechanical, electrical, and hydraulic systems, the problem of loading, or back-action, is encountered when engineering genetic circuits. These molecular loads can be severe to the point of completely destroying the intended function of a circuit. In this talk, I will review a systems theoretic modeling formalism, grounded on the concept of retroactivity, that captures molecular loads in a way that makes the loading problem amenable of a solution. I will, in particular, focus on two types of loading: inter-module loads and loads to cellular resources that feed the modules. I will show experimentally validated models of loading effects on the emergent dynamics of a system and nonlinear control techniques that we have developed and implemented to mitigate these effects.
