State estimation, system identification and adaptive control for networked systems

A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies. For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation. Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework. We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well

BattX: An equivalent circuit model for lithium-ion batteries over broad current ranges

Advanced battery management is as important for lithium-ion battery systems as the brain is for the human body. Its performance is based on the use of fast and accurate battery models. However, the mainstream equivalent circuit models and electrochemical models have yet to meet this need well, due to their struggle with either predictive accuracy or computational complexity. This problem has acquired urgency as some emerging battery applications running across broad current ranges, e.g., electric vertical take-off and landing aircraft, can hardly find usable models from the literature. Motivated to address this problem, we develop an innovative model in this study. Called BattX, the model is an equivalent circuit model that draws comparisons to a single particle model with electrolyte and thermal dynamics, thus combining their respective merits to be computationally efficient, accurate, and physically interpretable. The model design pivots on leveraging multiple circuits to approximate major electrochemical and physical processes in charging/discharging. Given the model, we develop a multipronged approach to design experiments and identify its parameters in groups from experimental data. Experimental validation proves that the BattX model is capable of accurate voltage prediction for charging/discharging across low to high C-rates

High-Order Leader-Follower Tracking Control under Limited Information Availability

Limited information availability represents a fundamental challenge for control of multi-agent systems, since an agent often lacks sensing capabilities to measure certain states of its own and can exchange data only with its neighbors. The challenge becomes even greater when agents are governed by high-order dynamics. The present work is motivated to conduct control design for linear and nonlinear high-order leader-follower multi-agent systems in a context where only the first state of an agent is measured. To address this open challenge, we develop novel distributed observers to enable followers to reconstruct unmeasured or unknown quantities about themselves and the leader and on such a basis, build observer-based tracking control approaches. We analyze the convergence properties of the proposed approaches and validate their performance through simulation