  Active components, such as actuators, constitute a fundamental aspect of
engineering systems, affording the freedom to shape system behavior as desired.
However, this capability necessitates energy consumption, primarily in the form
of electricity. Thus, a trade-off emerges between energy usage and desired
outcomes. 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 its
simplicity, despite sub-optimal responses in many cases. To bridge this gap,
Model Predictive Control (MPC) offers a solution, yet its complexity limits its
broad applicability. This paper introduces user-friendly Python-based MPC
software, enabling easy access to MPC. The effectiveness of this software is
demonstrated through multiple examples, including those with a known analytical
solution.
