Robust motion trajectory optimization of overhead cranes based on polynomial chaos expansion

Area of Science:

• Engineering
• Control Systems
• Applied Mathematics

Background:

• Overhead crane systems are crucial in logistics and manufacturing.
• Trajectory optimization is vital for efficiency and safety.
• Uncertainties in initial states and system parameters pose significant challenges.

Purpose of the Study:

• To develop a robust trajectory optimization framework for overhead cranes under uncertainty.
• To efficiently solve complex stochastic optimization problems.
• To quantify the impact of uncertainties on crane system dynamics.

Main Methods:

• Formulation of deterministic and uncertainty-based trajectory optimization problems.
• Application of polynomial chaos expansion to handle stochastic functions.
• Development of an augmented deterministic problem for direct solution.
• Comparison with traditional sampling-based Monte Carlo simulations.

Main Results:

• A novel polynomial chaos-based framework for robust trajectory optimization was successfully developed.
• The proposed method efficiently solved the complex, uncertainty-laden optimization problem.
• Simulations demonstrated the feasibility and effectiveness of the approach compared to Monte Carlo methods.

Conclusions:

• The polynomial chaos framework provides an effective solution for overhead crane trajectory optimization under uncertainty.
• This method offers a robust and efficient alternative to traditional simulation techniques.
• The findings have implications for improving the performance and reliability of automated crane systems.