Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15396 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916706242265088 |
|---|---|
| author | Ladnik, Igor |
| author_facet | Ladnik, Igor |
| contents | This article presents a unified approach to quadratic optimal control for both linear and nonlinear discrete-time systems, with a focus on trajectory tracking. The control strategy is based on minimizing a quadratic cost function that penalizes deviations of system states and control inputs from their desired trajectories.
For linear systems, the classical Linear Quadratic Regulator (LQR) solution is derived using dynamic programming, resulting in recursive equations for feedback and feedforward terms. For nonlinear dynamics, the Iterative Linear Quadratic Regulator (iLQR) method is employed, which iteratively linearizes the system and solves a sequence of LQR problems to converge to an optimal policy.
To implement this approach, a software service was developed and tested on several canonical models, including: Rayleigh oscillator, inverted pendulum on a moving cart, two-link manipulator, and quadcopter. The results confirm that iLQR enables efficient and accurate trajectory tracking in the presence of nonlinearities.
To further enhance performance, it can be seamlessly integrated with Model Predictive Control (MPC), enabling online adaptation and improved robustness to constraints and system uncertainties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Quadratic Control Framework for Dynamic Systems Ladnik, Igor Systems and Control This article presents a unified approach to quadratic optimal control for both linear and nonlinear discrete-time systems, with a focus on trajectory tracking. The control strategy is based on minimizing a quadratic cost function that penalizes deviations of system states and control inputs from their desired trajectories. For linear systems, the classical Linear Quadratic Regulator (LQR) solution is derived using dynamic programming, resulting in recursive equations for feedback and feedforward terms. For nonlinear dynamics, the Iterative Linear Quadratic Regulator (iLQR) method is employed, which iteratively linearizes the system and solves a sequence of LQR problems to converge to an optimal policy. To implement this approach, a software service was developed and tested on several canonical models, including: Rayleigh oscillator, inverted pendulum on a moving cart, two-link manipulator, and quadcopter. The results confirm that iLQR enables efficient and accurate trajectory tracking in the presence of nonlinearities. To further enhance performance, it can be seamlessly integrated with Model Predictive Control (MPC), enabling online adaptation and improved robustness to constraints and system uncertainties. |
| title | A Quadratic Control Framework for Dynamic Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.15396 |