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Kasey A. Ackerman

Publications and source records attributed to Kasey A. Ackerman.

3 recordsLinked to original sources

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

Quadrotors that can operate predictably in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present L1Quad, a control architecture that ensures uniformly bounded transient response of the quadrotor's uncertain dynamics on the special Euclidean group SE(3). By leveraging the geometric controller and the L1 adaptive controller, the L1Quad architecture provides a theoretically justified framework for the design and analysis of quadrotor's tracking controller in the presence of nonlinear (time- and state-dependent) uncertainties on both the translational and rotational dynamics. In addition, we validate the performance of the L1Quad architecture through extensive experiments for eleven types of uncertainties across various trajectories. The results demonstrate that the L1Quad can achieve consistently small tracking errors despite the uncertainties and disturbances and significantly outperforms existing state-of-the-art controllers.

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$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors

This paper introduces an $\mathcal{L}_1$ adaptive control augmentation for geometric tracking control of quadrotors. In the proposed design, the $\mathcal{L}_1$ augmentation handles nonlinear (time- and state-dependent) uncertainties in the quadrotor dynamics without assuming or enforcing parametric structures, while the baseline geometric controller achieves stabilization of the known nonlinear model of the system dynamics. The $\mathcal{L}_1$ augmentation applies to both the rotational and the translational dynamics. Experimental results demonstrate that the augmented geometric controller shows consistent and (on average five times) smaller trajectory tracking errors compared with the geometric controller alone when tested for different trajectories and under various types of uncertainties/disturbances.

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L1-Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and L1 adaptive control are combined to achieve both fast model predictive trajectory planning and robust trajectory tracking. MPPI provides a framework to solve nonlinear MPC with complex cost functions in real-time. However, it often lacks robustness, especially when the simulated dynamics are different from the true dynamics. We show that the L1 adaptive controller robustifies the architecture, allowing the overall system to behave similar to the nominal system simulated with MPPI. The architecture is validated in a simulated multirotor racing environment.

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