Trajectory Tracking Nonlinear Model Predictive Control for an Overactuated MAV

Brunner, Maximilian; Bodie, Karen; Kamel, Mina; Pantic, Michael; Zhang, Weixuan; Nieto, Juan; Siegwart, Roland

doi:10.1109/icra40945.2020.9197005

Cited by 27 publications

(30 citation statements)

References 15 publications

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“…) b) Constraints on inputs and states: The sets of states X and inputs U are constructed to limit actuator wrench and its derivative similarly as in [11]. In this way, the MPC is aware of the system's physical constraints.…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…We employ the same EKF-based disturbance observer as in [11]. Similarly to equations (2a) to (2e), it models the AM as a rigid body with disturbance forces and torques applied on F B .…”

Section: Disturbance Observermentioning

confidence: 99%

“…However, due to underactuation of the platform, tasks remain slow, with low interaction forces during push-and-slide. Some MPC-based approaches have also been developed for fully-actuated MAVs [11], [12], but only in free-flight. In the domain of mobile robot manipulators, direct interaction force control has been implemented with MPC, achieving large forces applied to the environment [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Direct Force and Pose NMPC with Multiple Interaction Modes for Aerial Push-and-Slide Operations

Peric

Brunner

Bodie

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we present a model predictive controller for a fully actuated aerial manipulator to track a hybrid force and pose trajectory at the end-effector in an aerial interaction task. A force sensor at the end-effector is used to detect contact and to directly control the interaction force. We propose an approach for automatic transition between three operation modes which reflect the state of contact constraints, including free flight and two modes for force control based on static or dynamic friction at the end-effector. This division into three modes allows for different mode-specific controller tunings to optimize the desired performance throughout an interaction task. Results from flight experiments which combine force, position, and attitude tracking, show the performance of the controller in terms of accuracy and precision. The performance is further benchmarked against a hybrid force/impedance controller.

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Section: Model Predictive Controlmentioning

confidence: 99%

“…We employ the same EKF-based disturbance observer as in [11]. Similarly to equations (2a) to (2e), it models the AM as a rigid body with disturbance forces and torques applied on F B .…”

Section: Disturbance Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direct Force and Pose NMPC with Multiple Interaction Modes for Aerial Push-and-Slide Operations

Peric

Brunner

Bodie

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

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“…They use a sequential linear quadratic (SLQ) solver to solve, online, an OCP and apply the optimized thrust commands to the motors. A similar approach is taken in [27], where they consider the omnidirectional multicopter described in [28]. They use the derivative of the wrench on the body as the control variable in the nMPC, resorting to control allocation to retrieve the rotor speeds and tilt angles.…”

Section: Optimal Control In Aerial Manipulationmentioning

confidence: 99%

Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation

Marti-Saumell¹,

Solà²,

Santamaria-Navarro³

et al. 2021

Preprint

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Non-linear model predictive control (nMPC) is a powerful approach to control complex robots (such as humanoids, quadrupeds or unmanned aerial manipulators (UAMs)) as it brings important advantages over other existing techniques. The full-body dynamics, along with the prediction capability of the optimal control problem (OCP) solved at the core of the controller, allows to actuate the robot in line with its dynamics. This fact enhances the robot capabilities and allows, e.g., to perform intricate maneuvers at high dynamics while optimizing the amount of energy used. Despite the many similarities between humanoids or quadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to UAMs.This paper provides a thorough description of how to use such techniques in the field of aerial manipulation. We give a detailed explanation of the different parts involved in the OCP, from the UAM dynamical model to the residuals in the cost function. We develop and compare three different nMPC controllers: Weighted MPC, Rail MPC and Carrot MPC, which differ on the structure of their OCPs and on how these are updated at every time step. To validate the proposed framework, we present a wide variety of simulated case studies. First, we evaluate the trajectory generation problem, i.e., optimal control problems solved offline, involving different kinds of motions (e.g., aggressive maneuvers or contact locomotion) for different types UAMs. Then, we assess the performance of the three nMPC controllers, i.e., closed-loop controllers solved online, through a variety of realistic simulations. For the benefit of the community, we have made available the source code related to this work.

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“…Optimal control such as the model predictive controller is shown to work well for standard multicopters [11]. However, it is shown in [12] that the model predictive controller has comparable performance to a standard PD controller in case of insufficient model knowledge.…”

Section: Related Workmentioning

confidence: 99%

Learning Dynamics for Improving Control of Overactuated Flying Systems

Zhang

Brunner

Ott

et al. 2020

IEEE Robot. Autom. Lett.

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Overactuated omnidirectional flying vehicles are capable of generating force and torque in any direction, which is important for applications such as contact-based industrial inspection. This comes at the price of an increase in model complexity. These vehicles usually have non-negligible, repetitive dynamics that are hard to model, such as the aerodynamic interference between the propellers. This makes it difficult for high-performance trajectory tracking using a model-based controller. This paper presents an approach that combines a datadriven and a first-principle model for the system actuation and uses it to improve the controller. In a first step, the first-principle model errors are learned offline using a Gaussian Process (GP) regressor. At runtime, the first-principle model and the GP regressor are used jointly to obtain control commands. This is formulated as an optimization problem, which avoids ambiguous solutions present in a standard inverse model in overactuated systems, by only using forward models. The approach is validated using a tilt-arm overactuated omnidirectional flying vehicle performing attitude trajectory tracking. The results show that with our proposed method, the attitude trajectory error is reduced by 32% on average as compared to a nominal PID controller.

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Trajectory Tracking Nonlinear Model Predictive Control for an Overactuated MAV

Cited by 27 publications

References 15 publications

Direct Force and Pose NMPC with Multiple Interaction Modes for Aerial Push-and-Slide Operations

Direct Force and Pose NMPC with Multiple Interaction Modes for Aerial Push-and-Slide Operations

Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation

Learning Dynamics for Improving Control of Overactuated Flying Systems

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