Modern autonomous systems, such as robotic manipulators in industrial settings and autonomous vehicles navigating urban environments, exemplify the increasing complexity of contemporary control systems. These systems operate in highly dynamic and unpredictable contexts, requiring advanced strategies to ensure reliability and safety. For instance, a robotic arm assembling delicate components must manage precision and variability in its tasks, while an autonomous car must respond to dynamic traffic conditions, changing weather, and human unpredictability. Designing control strategies for such scenarios is a multifaceted challenge, involving not only technical innovation but also adherence to strict safety and performance standards.
At the core of these challenges lies the interaction between complex system dynamics and an uncertain external world. The behavior of modern autonomous systems is governed by nonlinear, time-varying dynamics, often coupled with high-dimensional state spaces. Additionally, their external environments introduce further uncertainties, including unmodeled disturbances, sensor noise, and the need to adapt to unforeseen scenarios. Addressing these issues requires a robust and systematic approach to control design.
Our work focuses on leveraging classical and intelligent control methodologies to address these challenges. Classical control theory, with its proven stability guarantees and predictability, provides a solid foundation for managing well-defined system behaviors. For example, proportional-integral-derivative (PID) controllers and linear quadratic regulators (LQR) are widely used for their robustness in conventional settings. However, the limitations of these methods become evident in high-complexity systems where dynamics are highly nonlinear or where adaptability to rapid environmental changes is critical.
To address these limitations, we integrate intelligent control techniques into the design process. Machine learning-based methods, such as reinforcement learning and neural network-based controllers, enable systems to learn optimal control strategies from interaction with their environments. This is particularly advantageous for autonomous systems operating in environments where uncertainties are difficult to model explicitly. For instance, reinforcement learning can optimize a vehicle’s path planning and control in a dynamically changing urban environment, while neural networks can model and control the intricate behaviors of soft robotic systems.
Our approach is grounded in systematic design principles and validated through rigorous simulation and experimental testing. By coupling theoretical insights with empirical evaluation, we ensure that our methods not only meet theoretical performance benchmarks but also align with industrial standards for robustness and safety. This dual validation process allows us to identify potential failure modes early and refine control strategies to address them.
The combination of classical and intelligent control techniques allows us to optimize the trade-offs between robustness, adaptability, and compliance. For example, robust control methods ensure stability under uncertainty, but they can sometimes lead to overly conservative system behavior. Intelligent methods, when appropriately integrated, enhance flexibility and efficiency, enabling systems to perform optimally without compromising safety.
By addressing the challenges posed by the increasing complexity of modern autonomous systems, our research contributes to the development of reliable and intelligent control solutions. These solutions empower systems like robots and autonomous vehicles to operate effectively in complex, unpredictable environments, ensuring both innovation and safety.