However, AKF remains challenge to nonlinear systems of finding optimal solutions when faced with model parameter uncertainties
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The challenge of finding optimal solutions for nonlinear systems with model parameter uncertainties can be addressed through innovative optimization methods. One such approach involves jointly optimizing a nominal trajectory and an error feedback to handle uncertainties effectively . By reformulating uncertain nonlinear systems as linear time-varying systems, tools from system level synthesis can be applied to convexly bound uncertainties, leading to a tractable optimization problem . Additionally, incorporating uncertainties into a feedback loop via model predictive control can enhance the performance of control strategies in the presence of initial condition uncertainties . These methods offer efficient ways to handle uncertainties and optimize solutions for nonlinear systems, showcasing advancements in addressing challenges posed by model parameter uncertainties.
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| Papers (5) | Insight |
|---|---|
2 Citations | Explicit multiobjective model predictive control (MPC) addresses uncertainties in nonlinear systems, enhancing optimal solutions despite model parameter uncertainties, unlike the challenges faced by the Extended Kalman Filter (AKF). |
03 Apr 2023 | Not addressed in the paper. |
| EAKF-MGA addresses challenges of AKF in nonlinear systems by optimizing parameters effectively, reducing model bias, and improving assimilation quality through a hybrid adaptive approach. | |
| Robust optimal control addresses nonlinear systems with parametric uncertainties, offering a solution to optimal control challenges in the presence of model parameter uncertainties. | |
| The proposed optimization method in the paper addresses challenges of finding optimal solutions for nonlinear systems with model parameter uncertainties, offering a robust optimal controller solution. |
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