Current Trends in Model Predictive Control and State Estimation
Recent advancements in Model Predictive Control (MPC) and state estimation are significantly enhancing the efficiency and applicability of these techniques across various large-scale systems. A notable trend is the development of constraint-adaptive MPC frameworks, which dynamically select subsets of constraints to reduce computational complexity while preserving closed-loop performance and ensuring recursive feasibility. These methods are particularly promising for systems with numerous state constraints, such as those arising from spatially discretized partial differential equations, as seen in applications like hyperthermia cancer treatments and metal additive manufacturing.
In parallel, there is a growing focus on real-time feasible state estimation techniques for large-scale systems, leveraging spatial correlations to reduce computational complexity without compromising accuracy. These approaches are crucial for enabling feedback control in complex systems where traditional estimation methods are computationally infeasible.
The integration of advanced optimization techniques, such as augmented Lagrangian differential dynamic programming, with GPU acceleration, is also advancing the ability to control spatial microstructures in metal additive manufacturing, demonstrating the potential for precise and efficient control of material properties.
Noteworthy Developments
- Constraint-adaptive MPC for large-scale systems: Novel schemes dynamically select constraints, significantly reducing computational complexity with minimal impact on performance.
- Approximate Kalman filtering for large-scale systems: A real-time feasible state estimation scheme that leverages spatial correlations, showing significant computational improvements with good accuracy.
- Trajectory optimization for microstructure control in metal AM: An innovative approach using augmented Lagrangian differential dynamic programming, achieving precise control of material properties in metal additive manufacturing.
