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Active components, such as actuators, constitute a fundamental aspect ofengineering systems, affording the freedom to shape system behavior as desired.However, this capability necessitates energy consumption, primarily in the formof electricity. Thus, a trade-off emerges between energy usage and desiredoutcomes. While open-loop optimal control methods strive for efficiency,practical implementation is hampered by disturbances and model discrepancies,underscoring the need for closed-loop controllers. The Proportional-Integral-Derivative (PID) controller is widely favored in industry due to itssimplicity, despite sub-optimal responses in many cases. To bridge this gap,Model Predictive Control (MPC) offers a solution, yet its complexity limits itsbroad applicability. This paper introduces user-friendly Python-based MPCsoftware, enabling easy access to MPC. The effectiveness of this software isdemonstrated through multiple examples, including those with a known analyticalsolution.

LGR-MPC: A user-friendly software based on Legendre-Gauss-Radau pseudo spectral method for solving Model Predictive Control problems