Primal Dual iLQR

Primal-dual iterative Linear Quadratic Regulators (iLQR) are advanced optimization algorithms designed to efficiently solve complex nonlinear optimal control problems, particularly those arising in robotics and autonomous systems. Current research focuses on improving the scalability and robustness of iLQR methods, including exploring distributed and decentralized architectures for multi-agent systems and incorporating techniques like augmented Lagrangians and consensus ADMM to handle large-scale problems. These improvements enable real-time trajectory optimization for applications such as legged robot locomotion and cooperative autonomous vehicle control, offering significant advancements in the control of complex dynamic systems.