Control and Optimization of Autonomous Engineering Systems

Autonomous engineering systems operate in environments where uncertainty, disturbance, and incomplete information are unavoidable. What distinguishes a system that works from one that can be trusted is control.

The answer is control.

Autonomy succeeds, not because it is complex, but because it is stable, predictable, and adaptive under real-world uncertainty. The goal of my control and optimization research is to ensure that autonomy is reliable, rather than merely spectacular.

The book covers topics in AGVs, aerial platforms, and intelligent engineering systems. It particularly focuses on control schemes that work under disturbances, model uncertainty, and changing environments, which are typical characteristics of real-world applications.

One of the key contributions of this research work is the combination of classical control and intelligent optimization methods. Techniques such as PID parameter tuning, adaptive control, and particle swarm optimization have been employed not independently, but together. This coordinated use enables performance improvement without sacrificing stability guarantees.

In AGV systems, this allows for smoother movements, superior disturbance rejection, and greater energy efficiency. Rather than being overly reactive with respect to disturbances, these systems are proactive, which translates into safer, more predictable, and more human-friendly behavior. Quantitative results show measurable improvements in energy efficiency and disturbance attenuation.

The effort also brings out the need for planning and control to be properly coordinated. Self-behavior becomes possible when there is support for higher-level tasks in the strategies for control.

Such considerations are easily extended into the area of aerial and multi-platform systems, where stability and responsiveness are direct factors in mission accomplishment. The work presented here is clearly a system-level perspective of autonomy, where intelligence is distributed throughout the control stack, as opposed to being solely within perception and planning.

This project reiterates a major tenet: autonomy is an engineering field and not a software capability. The novelty and impact of this work lie in demonstrating that trustworthy autonomy emerges from disciplined control and optimization, not from complexity alone. Autonomy has nothing to do with speed, because true autonomy means acting wisely, consistently, and safely in the real world.