Next Level Robust And Adaptive Model Predictive Control For Nonlinear Systems That Handles Parametric Uncertainty In Real World Control Robotics And Sensor Applications

Robust Adaptive MPC for Uncertain Nonlinear Systems
✔️ Offers online parameter updating with set-based estimation for NMPC

This illustrated book introduces an adaptive robust nonlinear model predictive control (NMPC) framework designed for systems with disturbances and parametric uncertainty. It explains how online parameter estimation and set-based uncertainty bounds can be leveraged to guarantee robust performance in real-world control, robotics, and sensor applications.

✅ Topic coverage: adaptive robust NMPC, set-based parameter estimation, online updates
✅ Addresses parametric uncertainty and disturbances in nonlinear control
✅ 272 pages, illustrated edition for clearer concepts
✅ English language release, 2015 edition for reference in robotics and sensor systems

💡 What is adaptive robust model predictive control used for in nonlinear systems with parametric uncertainty? It provides a practical approach to keep systems stable and performing well when model parameters are not precisely known, or change over time. - Online parameter estimation and bound tightening help manage uncertainty in real-time - Robust optimization ensures constraint satisfaction under disturbances - Suitable for application in robotics, automation, and sensor-driven control - Supports performance improvement through adaptive tuning and reduced conservatism

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