Full Description
The book explores the field of model predictive control (MPC). It reports on the latest developments in MPC, current applications, and presents various subfields of MPC. The book features topics such as uncertain and stochastic MPC variants, learning and neural network approaches, easy-to-use numerical implementations as well as multi-agent systems and scheduling and coordination tasks. While MPC is rooted in engineering science, this book illustrates the potential of using MPC theory and methods in non-engineering sciences and applications such as economics, finance, and environmental sciences.
Contents
Chapter 1. Multi-horizon MPC and Its Application to theIntegrated Power and Thermal Management ofElectrified Vehicles (Qiuhao Hu).- Chapter 2. Data/Moment-Driven Approaches for FastPredictive Control of Collective Dynamics (Giacomo Albi).- Chapter 3. Finite-Dimensional Receding Horizon Control ofLinear Time-Varying Parabolic PDEs: StabilityAnalysis and Model-Order Reduction (Behzad Azmi).- Chapter 4. Solving Hybrid Model Predictive ControlProblems via a Mixed-Integer Approach (Iman Nodozi).- Chapter 5. nMPyC - A Python Package for Solving OptimalControl Problems via Model Predictive Control (Jonas Schießl).- Chapter 6. Controllability of Continuous Networks and aKernel-Based Learning Approximation (Michael Herty).- Chapter 7. Economic Model Predictive Control as aSolution to Markov Decision Processes (Dirk Reinhardt).- Chapter 8. Reinforcement Learning with Guarantees (Mario Zanon).
