Correct-By-Construction Control Synthesis for Systems with Disturbance and Uncertainty

This dissertation focuses on correct-by-construction control synthesis for Cyber-Physical Systems (CPS) under model uncertainty and disturbance. CPSs are systems that interact with the physical world and perform complicated dynamic tasks where safety is often the overriding factor. Correct-by-construction control synthesis is a concept that provides formal performance guarantees to closed-loop systems by rigorous mathematic reasoning. Since CPSs interact with the environment, disturbance and modeling uncertainty are critical to the success of the control synthesis. Disturbance and uncertainty may come from a variety of sources, such as exogenous disturbance, the disturbance caused by co-existing controllers and modeling uncertainty. To better accommodate the different types of disturbance and uncertainty, the verification and control synthesis methods must be chosen accordingly. Four approaches are included in this dissertation. First, to deal with exogenous disturbance, a polar algorithm is developed to compute an avoidable set for obstacle avoidance. Second, a supervised learning based method is proposed to design a good student controller that has safety built-in and rarely triggers the intervention of the supervisory controller, thus targeting the design of the student controller. Third, to deal with the disturbance caused by co-existing controllers, a Lyapunov verification method is proposed to formally verify the safety of coexisting controllers while respecting the confidentiality requirement. Finally, a data-driven approach is proposed to deal with model uncertainty. A minimal robust control invariant set is computed for an uncertain dynamic system without a given model by first identifying the set of admissible models and then simultaneously computing the invariant set while selecting the optimal model. The proposed methods are applicable to many real-world applications and reflect the notion of using the structure of the system to achieve performance guarantees without being overly conservative.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145933/1/chenyx_1.pd

Dynamics under Uncertainty: Modeling Simulation and Complexity

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc

Data-driven methods for tracking improvement

The tracking precision required by modern industrial applications is continuously increasing. Feedback control alone is often no longer capable of giving the necessary tracking accuracy and so the use of two-degree-of-freedom controllers, which include a feedforward term, has become commonplace. Traditionally the feedforward term is a filter based on the inverse of an identified model of the system. It is, however, not possible to obtain very high precision tracking with this approach because the identified model will always suffer from model uncertainty. In this thesis, data-driven methods are investigated. These methods derive the feedforward control directly from measured data and thus avoid the system identification step, which is where the model uncertainty is introduced. They are, therefore, capable of producing higher precision tracking than the traditional methods. For the general tracking problem, a precompensator controller is considered as the feedforward term. This controller filters the desired output signal before it is applied as an input to the system. The precompensator's parameters are tuned directly using measured data. These data are affected by stochastic disturbances, such as measurement noise. The effect of these disturbances on the calculated parameters is studied and the correlation approach is used to reduce it. For the specific problem where the tracking task is repetitive, a situation frequently encountered in industrial applications, Iterative Learning Control is proposed. Iterative Learning Control uses measurements from previous repetitions to adjust the system's input for the current repetition in a manner that improves the tracking. As measurements are used, the calculated input is sensitive to the stochastic disturbances. The effect of these disturbances on the learning procedure is examined and algorithms, which are less sensitive to their presence, are developed. Extensions of the methods are also made for linear parameter varying systems in which the system's dynamics change as a function of a scheduling parameter. The developed methods are successfully applied to an industrial linear motor positioning system

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences