15 research outputs found
Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors
Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this letter, we propose L1 -NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline
A Zero-Shot Adaptive Quadcopter Controller
This paper proposes a universal adaptive controller for quadcopters, which
can be deployed zero-shot to quadcopters of very different mass, arm lengths
and motor constants, and also shows rapid adaptation to unknown disturbances
during runtime. The core algorithmic idea is to learn a single policy that can
adapt online at test time not only to the disturbances applied to the drone,
but also to the robot dynamics and hardware in the same framework. We achieve
this by training a neural network to estimate a latent representation of the
robot and environment parameters, which is used to condition the behaviour of
the controller, also represented as a neural network. We train both networks
exclusively in simulation with the goal of flying the quadcopters to goal
positions and avoiding crashes to the ground. We directly deploy the same
controller trained in the simulation without any modifications on two
quadcopters with differences in mass, inertia, and maximum motor speed of up to
4 times. In addition, we show rapid adaptation to sudden and large disturbances
(up to 35.7%) in the mass and inertia of the quadcopters. We perform an
extensive evaluation in both simulation and the physical world, where we
outperform a state-of-the-art learning-based adaptive controller and a
traditional PID controller specifically tuned to each platform individually.
Video results can be found at
https://dz298.github.io/universal-drone-controller/.Comment: Video results can be found on the project webpage
https://dz298.github.io/universal-drone-controller
Recent Advances in Path Integral Control for Trajectory Optimization: An Overview in Theoretical and Algorithmic Perspectives
This paper presents a tutorial overview of path integral (PI) control
approaches for stochastic optimal control and trajectory optimization. We
concisely summarize the theoretical development of path integral control to
compute a solution for stochastic optimal control and provide algorithmic
descriptions of the cross-entropy (CE) method, an open-loop controller using
the receding horizon scheme known as the model predictive path integral (MPPI),
and a parameterized state feedback controller based on the path integral
control theory. We discuss policy search methods based on path integral
control, efficient and stable sampling strategies, extensions to multi-agent
decision-making, and MPPI for the trajectory optimization on manifolds. For
tutorial demonstrations, some PI-based controllers are implemented in MATLAB
and ROS2/Gazebo simulations for trajectory optimization. The simulation
frameworks and source codes are publicly available at
https://github.com/INHA-Autonomous-Systems-Laboratory-ASL/An-Overview-on-Recent-Advances-in-Path-Integral-Control.Comment: 16 pages, 9 figure
Towards Efficient MPPI Trajectory Generation with Unscented Guidance: U-MPPI Control Strategy
The classical Model Predictive Path Integral (MPPI) control framework lacks
reliable safety guarantees since it relies on a risk-neutral trajectory
evaluation technique, which can present challenges for safety-critical
applications such as autonomous driving. Additionally, if the majority of MPPI
sampled trajectories concentrate in high-cost regions, it may generate an
infeasible control sequence. To address this challenge, we propose the U-MPPI
control strategy, a novel methodology that can effectively manage system
uncertainties while integrating a more efficient trajectory sampling strategy.
The core concept is to leverage the Unscented Transform (UT) to propagate not
only the mean but also the covariance of the system dynamics, going beyond the
traditional MPPI method. As a result, it introduces a novel and more efficient
trajectory sampling strategy, significantly enhancing state-space exploration
and ultimately reducing the risk of being trapped in local minima. Furthermore,
by leveraging the uncertainty information provided by UT, we incorporate a
risk-sensitive cost function that explicitly accounts for risk or uncertainty
throughout the trajectory evaluation process, resulting in a more resilient
control system capable of handling uncertain conditions. By conducting
extensive simulations of 2D aggressive autonomous navigation in both known and
unknown cluttered environments, we verify the efficiency and robustness of our
proposed U-MPPI control strategy compared to the baseline MPPI. We further
validate the practicality of U-MPPI through real-world demonstrations in
unknown cluttered environments, showcasing its superior ability to incorporate
both the UT and local costmap into the optimization problem without introducing
additional complexity.Comment: This paper has 15 pages, 10 figures, 4 table
L1 adaptive control flight testing and extension to nonlinear reference systems with unmatched uncertainty
Building upon prior research efforts deploying L1 adaptive control in remotely piloted aerospace applications, this dissertation presents the progression of in-flight evaluation of L1 adaptive control to manned flight testing on Calspan’s variable stability Learjet and to an augmentation of an autonomous trajectory planner on a multirotor aircraft. These efforts ultimately led to the development of a new L1 adaptive controller for a class of control-affine nonlinear reference systems subject to time-varying, state-dependent matched and unmatched uncertainties.
The L1 adaptive controller for the Learjet flight tests was designed as stability augmentation system, modifying the pilot's stick-to-surface commands, and was evaluated in a series of flying and handling qualities tests. The results of the Learjet flight tests demonstrated the ability of the L1 adaptive controller to recover desired flying qualities and safe, consistent handling qualities in the presence of off-nominal dynamics, some of which had severe flying qualities deficiencies and aggressive tendencies toward adverse pilot-aircraft interaction, and simulated aircraft failures. A modification of the Learjet control law was implemented, with a nonlinear reference system and estimation of both matched and unmatched uncertainties, for a multirotor aircraft as an augmentation of a geometric trajectory-tracking baseline controller, tracking a reference trajectory generated by a model predictive path integral trajectory planner. Simulation results demonstrated that, with the L1 augmentation, the vehicle was able to navigate a complex environment in the presence of uncertainty and external disturbances.
The new L1 adaptive controller provides a theoretical foundation for the L1 augmentation in the multirotor application, and may be applicable to tilt-rotor, tilt-wing, and split-propulsion vertical takeoff and landing aircraft proliferating in the urban air mobility sector. The theory is based on incremental stability for robust trajectory tracking and uses a piecewise-constant adaptive law. It proposes a feedforward compensator (in the form of an embedded linear parameter-varying system), synthesized for the variational dynamics of the system using linear matrix inequality-based robust control methods to minimize the peak-to-peak gain from unmatched uncertainty to the system state. A realization of the feedforward compensator in the ambient space can be directly applied to the nonlinear system. Analysis of the closed-loop system provides an incremental stability guarantee and bounds the transient and steady-state trajectory-tracking error
DiffTune: Auto-Tuning through Auto-Differentiation
The performance of robots in high-level tasks depends on the quality of their
lower-level controller, which requires fine-tuning. However, the intrinsically
nonlinear dynamics and controllers make tuning a challenging task when it is
done by hand. In this paper, we present DiffTune, a novel, gradient-based
automatic tuning framework. We formulate the controller tuning as a parameter
optimization problem. Our method unrolls the dynamical system and controller as
a computational graph and updates the controller parameters through
gradient-based optimization. The gradient is obtained using sensitivity
propagation, which is the only method for gradient computation when tuning for
a physical system instead of its simulated counterpart. Furthermore, we use
adaptive control to compensate for the uncertainties (that
unavoidably exist in a physical system) such that the gradient is not biased by
the unmodelled uncertainties. We validate the DiffTune on a Dubin's car and a
quadrotor in challenging simulation environments. In comparison with
state-of-the-art auto-tuning methods, DiffTune achieves the best performance in
a more efficient manner owing to its effective usage of the first-order
information of the system. Experiments on tuning a nonlinear controller for
quadrotor show promising results, where DiffTune achieves 3.5x tracking error
reduction on an aggressive trajectory in only 10 trials over a 12-dimensional
controller parameter space.Comment: Minkyung Kim and Lin Song contributed equally to this wor