Motivated by the fact that many nonlinear plants can be represented through Linear
Parameter Varying (LPV) embedding, and being this framework very popular for control …

In this paper, we discuss the model predictive control algorithms that are tailored for
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …

We present a framework for online generation of robust motion plans for robotic systems with
nonlinear dynamics subject to bounded disturbances, control constraints, and online state …

Because of their complex behavior, wastewater treatment processes (WWTPs) are very
difficult to control. In this paper, the design and implementation of a nonlinear model …

This paper is concerned with tube-based model predictive control (MPC) for both linear and
nonlinear, input-affine continuous-time dynamic systems that are affected by time-varying …

Generally, vision-based controls use various camera sensors and require camera
calibration, while the control performance would degrade due to inaccuracy calibration …

The integration of design and control, control and scheduling and design, control and
scheduling, all have been core PSE challenges. While significant progress has been …

In this article, we develop a tracking model predictive control (MPC) scheme for nonlinear
systems using the linearized dynamics at the current state as a prediction model. Under …

We present a framework for online generation of robust motion plans for robotic systems with
nonlinear dynamics subject to bounded disturbances, control constraints, and online state …

In this paper, we present a framework for risk-sensitive model predictive control (MPC) of
linear systems affected by stochastic multiplicative uncertainty. Our key innovation is to …