Simultaneous model prediction and data-driven control with relaxed assumption on the model.

This paper aims to design a Model Predictive Control (MPC) law based on the time series data gathered from the input and output of a system. An Auto-Regressive Integrated Moving Average (ARIMA) model with unknown parameters and an unknown sequence of controller signal are considered for the system. Based on a window of data, an optimization problem is formulated which can find the optimal unknown model parameters and controller sequence, simultaneously.

Observer-Based Fault-Tolerant Finite-Time Control of Nonlinear Multiagent Systems.

In this article, an adaptive neural containment control for a class of nonlinear multiagent systems considering actuator faults is introduced. By using the general approximation property of neural networks, a neuro-adaptive observer is designed to estimate unmeasured states. In addition, in order to reduce the computational burden, a novel event-triggered control law is designed.

Optimal robust model predictive reset control design for performance improvement of uncertain linear system.

This study aims to design a robust reset dynamic output feedback control (DOFC) for a class of uncertain linear systems. This procedure is performed as following. First, the elements of the robust DOFC are designed via the linear matrix inequality (LMI) technique such that closed-loop exponential stability is achieved.

Model predictive-based reset gain-scheduling dynamic control law for polytopic LPV systems.

This paper proposes a novel systematic approach for designing a reset gain-scheduling dynamic controller based on a model predictive method for a class of nonlinear systems represented by polytopic linear parameter varying models. The proposed design procedure involves offline and online steps. In the offline step, sufficient conditions of the gain-scheduling dynamic controller design in terms of linear matrix inequalities are derived through a novel D-stability region.

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation.