Real-Time Model Predictive Control via Multi-Parametric Programming
Theory and Tools
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Model Predictive Control (MPC) is an attractive control methodology widely adopted by the process industry, since optimal operation of the plant can be achieved while taking constraints into account. Success of a practical implementation of MPC in real time then depends on how fast the optimal control input can be obtained. The computational burden can be significantly reduced by pre-computing the optimal solution for all possible operating scenarios using multi-parametric programming. This allows to reduce the task of MPC implementation to a simple set-membership test, which can be performed ...
Model Predictive Control (MPC) is an attractive control methodology widely adopted by the process industry, since optimal operation of the plant can be achieved while taking constraints into account. Success of a practical implementation of MPC in real time then depends on how fast the optimal control input can be obtained. The computational burden can be significantly reduced by pre-computing the optimal solution for all possible operating scenarios using multi-parametric programming. This allows to reduce the task of MPC implementation to a simple set-membership test, which can be performed efficiently on low-cost hardware. For researchers and process engineers, this book provides a compact overview of the field of multi-parametric programming and its use for design of MPC strategies operating under hard real-time constraints. Special emphasis is put on analysis of complexity of multi-parametric solutions and novel algorithms aimed at reducing the induced computational load arepresented. Last but not least, the book introduces a novel software tool - the Multi-Parametric Toolbox - which allows for rapid design, analysis, and deployment of MPC schemes.
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