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This thesis addresses the design of optimization-based control laws for the case where convergence to a desired set-point, minimization of an arbitrary performance index, or a combination of the two, is the desired objective. The results are developed with ...
In this technical note, a new model predictive controller for tracking arbitrary periodic references is presented. The proposed controller is based on a single layer that unites dynamic trajectory planning and control. A design procedure to guarantee that ...
Institute of Electrical and Electronics Engineers2016
This thesis focuses on the development of robust control solutions for linear time-invariant interconnected systems affected by polytopic-type uncertainty. The main issues involved in the control of such systems, e.g. sensor and actuator placement, control ...
This paper deals with stochastic model predictive control of constrained discrete-time periodic linear systems. Control inputs are subject to periodically time-varying polytopic constraints with possibly time-dependent state and input dimensions. A stochas ...
We consider the tracking of geometric paths in output spaces of nonlinear systems subject to input and state constraints without pre-specified timing requirements. Such problems are commonly referred to as constrained output path-following problems. Specif ...
Institute of Electrical and Electronics Engineers2016
The steady advance of computational methods makes model-based optimization an increasingly attractive method for process improvement. Unfortunately, the available models are often inaccurate. The traditional remedy is to update the model parameters, but th ...
Optimal operation of chemical processes is key for meeting productivity, quality, safety and environmental objectives. Both model-based and data-driven schemes are used to compute optimal operating conditions [1]: - The model-based techniques are intu ...
The integration of weather forecasts and demand prediction into the energy management system of buildings is usually achieved using a model-based predictive control. The performance of such control techniques strongly depends on the accuracy of the thermal ...
A short technical subsection for Gaussian Process learning and Uncertainty propagation is presented as required in applications like Model Predictive Control, machine learning and optimization. ...
In this paper we present a certified reduced basis (RB) framework for the efficient solution of PDE-constrained parametric optimization problems. We consider optimization problems (such as optimal control and optimal design) governed by elliptic PDEs and i ...
