Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems.

Area of Science:

• Control Theory
• Nonlinear Systems
• Artificial Intelligence

Background:

• Adaptive control is crucial for nonlinear systems but often requires frequent data sampling.
• Event-triggered control strategies aim to reduce communication load by updating systems only when specific conditions are met.
• Neural networks (NNs) offer powerful function approximation capabilities for complex systems.

Purpose of the Study:

• To develop an adaptive event-triggered control strategy for nonlinear continuous-time systems in strict-feedback form.
• To reduce the number of state transmissions and controller updates compared to traditional fixed-sampling methods.
• To ensure closed-loop stability under adaptive event sampling.

Main Methods:

• Utilized backstepping design combined with event-sampled neural networks (NNs) for approximating unknown nonlinear functions.
• Designed an adaptive model and an event-triggered controller where feedback signals and NN weights update aperiodically upon event condition violation.
• Ensured a positive lower bound on the minimum intersample time to prevent Zeno phenomena.

Main Results:

• The proposed event-triggered control method significantly reduces data transmissions by updating only when necessary.
• Rigorous Lyapunov analysis proved the closed-loop stability of the nonlinear impulsive dynamical system under adaptive event sampling.
• Simulation examples demonstrated the effectiveness of the adaptive event-triggered control approach.

Conclusions:

• The adaptive event-triggered control strategy is effective for nonlinear systems in strict-feedback form.
• This approach offers a more efficient alternative to traditional fixed-sampling adaptive backstepping control.
• The method successfully balances control performance with reduced communication burden.