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The ability of a model-based real-time optimization scheme to converge to the plant optimum relies on the ability of the underlying process model to predict the plant's necessary conditions of optimality (NCO). These include the values and gradients of the ...
State-feedback model predictive control (MPC) of discrete-time linear periodic systems with possibly time-dependent state and control input dimension is considered. States and inputs are subject to hard, mixed, polytopic constraints. It is described how di ...
Many model predictive control (MPC) schemes suffer from high computational complexity. Especially robust MPC schemes, which explicitly account for the effects of disturbances, can result in computationally intractable problems. So-called move-blocking is a ...
Real time control of heating systems is essential to maximize plasma performance and avoid or neutralize instabilities under changing plasma conditions. Several feedback control algorithms have been developed on the Tokamak a Configuration Variable (TCV) t ...
This paper describes the development of a 2-DOF electrostatic haptic joystick designed for neuroscience studies in an MRI/fMRI. The joystick is fabricated using non-magnetic materials and actuated by two high-power electrostatic motor units which produce v ...
The problem of designing network-wide traffic signal control strategies for large-scale congested urban road networks is considered. One known and two novel methodologies, all based on the store-and-forward modeling paradigm, are presented and compared. Th ...
The problem of swinging up an inverted pendulum on a cart and controlling it around the upright position has traditionally been treated as two separate problems. This paper proposes a control strategy that is globally asymptotically stable under actuator s ...
Model Predictive Control has been implemented on a large drainage canal system in the Netherlands. This water system can be represented as a reservoir with uncertain inflow due to rainfall runoff and a water level that has to be kept within a certain range ...
The goal of thermal management is to meet maximum operating temperature constraints, while at the same time tracking timevarying performance requirements. Current approaches avoid thermal violations by forcing abrupt operating points changes (e.g. processo ...
This paper introduces and demonstrates a novel brain-machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping. RL allows the BMI control algorithm to learn to complete tasks from interactions with ...