In a groundbreaking development poised to revolutionize fault-tolerant control systems, researchers have introduced a novel approach that promises to enhance the safety and efficiency of critical infrastructure, particularly in the energy sector. The study, led by Pengxin Yang from the School of Intelligence Science and Technology at the University of Science and Technology Beijing, addresses a longstanding challenge in control systems: balancing optimal performance with explicit time adjustability.
The research, published in the Journal of Engineering Science, focuses on affine nonlinear systems, which are prevalent in modern intelligent systems such as power grids, high-speed railways, and deep-space and deep-sea exploration. Traditional methods like fuzzy control, adaptive control, and sliding mode control ensure system stability but often fall short in achieving optimal control performance. Moreover, these methods typically rely on the initial state or controller parameters to determine convergence time, limiting their flexibility and adaptability.
Yang and his team have developed a prescribed-time fault-tolerant control strategy based on zero-sum differential games. This innovative approach transforms the fault-tolerant control problem into an adversarial optimization problem, leveraging an auxiliary function with time and space constraint characteristics. “By introducing an auxiliary function that constrains the system state within a prescribed time and desired accuracy, we can ensure that the system achieves both stability and optimal control performance,” explains Yang.
The researchers employed neural-network technology to mitigate the “curse of dimensionality,” a common challenge in solving complex optimization problems. This adaptive dynamic programming framework significantly reduces algorithm complexity and enhances the system’s ability to adapt to multiplicative faults. “Our method not only explicitly defines the convergence time but also synchronously optimizes control performance,” Yang adds.
The practical implications of this research are profound, particularly for the energy sector. Power grids, for instance, require robust control systems to maintain stability and efficiency, especially in the face of actuator faults. The ability to predesign the convergence time ensures that the system can quickly recover from faults, minimizing downtime and enhancing overall performance. This is crucial for preventing cascading failures that could lead to widespread blackouts and significant economic losses.
In a simulation involving a two-link robotic manipulator, the researchers demonstrated that their method could effectively handle actuator faults and bias faults, ensuring that the system converged within the desired accuracy and prescribed time. The results showed stable tracking performance across different initial states, controller parameters, and prescribed times, highlighting the method’s robustness and adaptability.
The contributions of this study are threefold. First, it employs a zero-sum differential game framework to design an optimal fault-tolerant control strategy, ensuring both stability and optimal control performance in finite time. Second, it accounts for the coexistence of actuator partial faults and bias faults, addressing a gap in existing algorithms. Third, it enables users to predesign the convergence time, making the system’s performance independent of initial states or controller parameters.
As the energy sector continues to evolve, the demand for advanced control systems that can handle complex and dynamic environments will only grow. This research paves the way for more resilient and efficient control systems, ultimately enhancing the reliability and safety of critical infrastructure. “Our work represents a significant step forward in the field of fault-tolerant control,” Yang concludes. “It opens up new possibilities for improving the performance and robustness of intelligent systems across various industries.”
The study, published in the Journal of Engineering Science, underscores the importance of interdisciplinary research in addressing real-world challenges. By integrating principles from differential games, neural networks, and control theory, Yang and his team have developed a solution that could have far-reaching implications for the energy sector and beyond. As the world becomes increasingly interconnected and reliant on complex systems, the need for advanced fault-tolerant control strategies will be paramount, shaping the future of technology and infrastructure.