Adaptive-Critic Design for Decentralized Event-Triggered Control of Constrained Nonlinear Interconnected Systems...

No prior work had resolved the challenge of maintaining stability in constrained nonlinear interconnected systems using decentralized, aperiodic control updates. Traditional methods often rely on complex actor-critic architectures that impose strict requirements on system dynamics. That uncertainty drove the need for a more flexible framework capable of handling interconnected subsystems without excessive computational overhead. It was already known that Hamilton-Jacobi-Bellman equations provide a pathway for optimal control, yet their application in decentralized settings remains difficult. Prior research has shown that persistence of excitation conditions often limit the practical implementation of adaptive control strategies. This gap motivated the development of an identifier-critic structure to bypass conventional limitations. Researchers have long sought to reduce the reliance on actor networks while preserving optimal performance. The current study addresses these limitations by proposing a novel decentralized control scheme designed for constrained environments.

The aim of this study is to develop a decentralized event-triggered control strategy for constrained nonlinear interconnected systems. The researchers seek to address the limitations inherent in traditional adaptive critic designs. A specific problem involves the complexity of managing interconnected subsystems while adhering to operational constraints. The authors intend to transform the control problem into a series of optimal policies updated aperiodically. This motivation stems from the need to reduce computational demands in decentralized control environments. The study explores how to relax restrictions on system dynamics by modifying the network architecture. By removing the actor network, the researchers aim to simplify the control law approximation process. This work seeks to provide a stable and efficient solution for complex systems that require robust, decentralized management.

The review approach involves transforming the original control problem into a series of optimal policies for constrained auxiliary subsystems. Researchers employ an identifier-critic network architecture to approximate the required control laws. This design strategy avoids the use of actor networks, which are common in standard control literature. The team utilizes a gradient descent approach to tune the weights within the critic network. Historical data is incorporated into the learning process to enhance the stability of the system. The methodology focuses on solving event-triggered Hamilton-Jacobi-Bellman equations to ensure optimal performance. A simulation example serves as the primary tool to verify the validity of the proposed control scheme. This approach ensures that the system remains stable under aperiodic update conditions.

The strongest finding indicates that the proposed identifier-critic architecture successfully maintains system stability in the sense of uniformly ultimate boundedness. The researchers demonstrate that their control laws function effectively in an aperiodic manner. By assigning specific cost functions to each auxiliary subsystem, they successfully transform the complex control problem. The study confirms that the persistence of excitation condition is no longer required for weight convergence. The critic network weights are updated using gradient descent and historical data. This result contrasts with traditional methods that rely on actor networks for policy approximation. The simulation example provides evidence that the decentralized controller performs as intended under constraints. The findings show that the system dynamics are managed without the strict requirements typically imposed by conventional adaptive critic designs.

The authors propose that their identifier-critic framework successfully achieves decentralized control for constrained nonlinear interconnected systems. Synthesis and implications suggest that circumventing the actor network simplifies the overall control architecture significantly. The researchers demonstrate that their approach maintains system stability in the sense of uniformly ultimate boundedness. This study implies that updating control policies aperiodically reduces communication requirements within complex interconnected networks. The findings suggest that the gradient descent approach for weight tuning effectively eliminates the necessity for persistence of excitation conditions. The authors conclude that their method provides a robust alternative to traditional adaptive critic designs. The results indicate that the proposed scheme is valid for systems with specific operational constraints. This work provides a foundation for future applications in decentralized control scenarios where computational efficiency is paramount.