Nonlinear Model Predictive Control with Constraints

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Resource Overview

This code implements Model Predictive Control (MPC) algorithms for nonlinear plants with constraints, featuring prediction model optimization and constraint handling.

Detailed Documentation

This implementation demonstrates Model Predictive Control (MPC) algorithms applied to nonlinear plants with constraints. MPC is a model-based control methodology that utilizes prediction models to compute optimal control inputs over a finite horizon. The code features a nonlinear plant model implementation, which more accurately captures real-world system dynamics compared to linear approximations. The implementation incorporates comprehensive constraint handling for system states, inputs, and outputs, ensuring controller stability and reliability through proper constraint enforcement mechanisms. Key algorithmic components include horizon-based optimization, quadratic programming solvers for control sequence calculation, and real-time constraint violation checks. This implementation provides fundamental insights into MPC applications for nonlinear systems and serves as a foundational framework for future research developments in advanced control systems.

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