Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents a systematic evaluation of control strategies (PID, LQR, MPC) for the classical control problem involving inverted pendulum on a cart under strict embedded constraints representative of software-defined vehicle ECUs. The objective is to evaluate and compare the performance of advanced control algorithms under varying control objectives when deployed on microcontrollers with constrained computational and memory resources, representative of the limitations encountered in embedded platforms used for SDVs. Furthermore, the study illustrates systematic optimization strategies that enable these algorithms to achieve real-time execution within such resource-constrained environments. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. By demonstrating 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 automotive and embedded engineers developing control systems for SDVs, where balancing performance and resource constraints is critical to achieving scalable, safe, and adaptive vehicle functionality.