Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles: A Case Study Using the Inverted Pendulum

Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents the implementation of multiple control strategies such as PID, LQR, and MPC for the inverted pendulum on a cart problem, a canonical example of nonlinear and unstable system control. The objective is to demonstrate how sophisticated control algorithms can be optimized for execution on microcontrollers with limited processing power and memory, reflecting the constraints typical of embedded platforms in SDVs. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics including stability, convergence time, and computational load, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. The results provide actionable insights into selecting the most suitable control approach for embedded automotive applications, especially where throughput and latency requirements are stringent. By showcasing how advanced control logic can be effectively deployed on constrained hardware, this work supports the broader goal of enabling intelligent, responsive vehicle behavior through software- centric design. The findings are particularly relevant for engineers developing control systems for SDVs, where balancing performance and resource constraints is critical to achieving scalable, safe, and adaptive vehicle functionality.