VLSI Design for Real-Time Digital Control Systems

Abstract

Real-time digital control systems are fundamental to modern embedded applications such as robotics, autonomous vehicles, and industrial automation. The integration of control algorithms into Very Large Scale Integration (VLSI) circuits ensures deterministic performance, energy efficiency, and compact hardware footprints. This paper explores the design considerations for VLSI-based digital control systems, including architectural choices, hardware-software co-design strategies, and low-latency signal pathways. We analyze pipelined datapaths, dedicated arithmetic logic blocks, and fault-tolerant architectures tailored to real-time operations. Emphasis is placed on achieving low power, low delay, and high reliability in harsh environments. A comparative performance graph of ASIC vs. FPGA implementations under control workloads is presented.Furthermore, this study discusses the role of fixed-point arithmetic units optimized for high-speed control loops, minimizing computational overhead without sacrificing accuracy. We also address the use of clock-gated logic and dynamic voltage scaling to reduce switching power in continuous operation scenarios. The impact of interconnect topology on signal propagation delay is evaluated, with a focus on NoC (Network-on-Chip) based controllers for scalable design. Real-time constraints are analyzed through worst-case execution time (WCET) metrics, providing insights into timing closure strategies across various control domains.The integration of programmable logic with hard processor systems (HPS) is also explored, enabling adaptive control schemes and system reconfiguration in field environments. Case studies, including FPGA-based PID motor controllers and ASIC-based drone stabilization circuits, illustrate the practical implications of the discussed architectures. In conclusion, the paper highlights trends in AI-assisted control synthesis, where VLSI accelerators for neural network inference are embedded alongside traditional control logic to enhance system autonomy and learning capability.