Titlebook: Lasso-MPC – Predictive Control with ?1-Regularised Least Squares; Marco Gallieri Book 2016 Springer International Publishing Switzerland 2

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Principles of LASSO MPC,Least Absolute Selection and Shrinking Operator (LASSO) is a powerful regression technique, which aims to induce sparseness in the solution (Tibshirani .; Osborne and Presnell .; Kim et?al. .; Ohlsson .; Schmidt .; Schuet .).

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Robust Tracking with Soft Constraints,It is becoming standard among researchers to use a terminal constraint in MPC. This constraint generally depends on the particular set-point we want to regulate the plant to. For changing set-points this means changing the terminal constraint, which could make the problem infeasible.

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Concluding Remarks,This is suitable, for instance, for control applications where the control signals are required to be sparse in their magnitudes or in their time-increments.

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https://doi.org/10.1007/978-3-319-27963-3Asychronous actuator interventions; LASSO Model Predictive Control; LASSO cost function; Least Absolute

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Introduction,ance automatic systems necessarily features the design of smart control algorithms, for scenarios such as power generation or energy saving. In general, an automatic system has to satisfy a particular performance specification, which is tightly related to its particular application field.

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Ship Roll Reduction with Rudder and Fins,ddressed in the control literature as surveyed in Perez, Ship motion control: course keeping and roll stabilization using rudder and fins (.). The considered set-up is shown in Fig.?.. The system, controlled by rudder and fins, is ..