Accelerated Energy-Saving Learning Control for Stochastic Point-to-Point Tracking Systems.

Area of Science:

• Control Systems Engineering
• Robotics
• Signal Processing

Background:

• Point-to-point (P2P) tracking systems often face challenges with stochastic noise.
• Reducing input energy is crucial for efficiency in tracking systems.

Purpose of the Study:

• To propose an accelerated learning control framework for P2P tracking systems.
• To reduce input energy consumption while managing stochastic noise.
• To enhance the performance and convergence of tracking iterations.

Main Methods:

• A novel stochastic accelerated method with a fixed penalty factor.
• A two-loop structure involving an inner loop with a historical term and an outer loop with Lagrange multiplier updates.
• Finite iteration termination for practical implementation and noise handling.

Main Results:

• Substantial performance advancements in the iteration process.
• Effective reduction of input energy by converging the input sequence to a limit closest to the initial input.
• Validation of theoretical results through numerical simulations.

Conclusions:

• The proposed framework effectively addresses P2P tracking under stochastic noise.
• The method achieves significant performance gains and energy reduction.
• The approach is practical for real-world implementation.