Lithium-ion batteries are widely recognised as the leading technology for electrochemical
energy storage. Their applications in the automotive industry and integration with renewable …

We present a robust adaptive model predictive control (MPC) framework for nonlinear
continuous-time systems with bounded parametric uncertainty and additive disturbance. We …

To control a dynamical system it is essential to obtain an accurate estimate of the current
system state based on uncertain sensor measurements and existing system knowledge. An …

We consider the design of functional estimators, ie, approaches to compute an estimate of a
nonlinear function of the state of a general nonlinear dynamical system subject to process …

In this paper, we study state estimation for general nonlinear systems with unknown
parameters and persistent process and measurement noise. In particular, we are interested …

In this article, we propose a suboptimal moving horizon estimator for a general class of
nonlinear systems. For the stability analysis, we transfer the “feasibility-implies …

Bayesian filtering serves as the mainstream framework of state estimation in dynamic
systems. Its standard version utilizes total probability rule and Bayes' law alternatively …

High-dimensional data is characterized by its sparsity and noise, which can increase the
likelihood of overfitting and compromise the model's generalizability performance. In this …

We propose a moving horizon estimation scheme for joint state and parameter estimation for
nonlinear uncertain discrete-time systems. We establish robust exponential convergence of …

Robust stability of moving-horizon estimators is investigated for nonlinear discrete-time
systems that are detectable in the sense of incremental input/output-to-state stability and are …