921 research outputs found
Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement
In this paper, we present a novel solution for real-time, Non-Linear Model
Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The
proposed controller formulates the Optimal Control Problem (OCP) in terms of
flat outputs over an adaptive lattice. In common approximated OCP solutions,
the number of discretization points composing the lattice represents a critical
upper bound for real-time applications. The proposed NMPC-based technique
refines the initially uniform time horizon by adding time steps with a sampling
criterion that aims to reduce the discretization error. This enables a higher
accuracy in the initial part of the receding horizon, which is more relevant to
NMPC, while keeping bounded the number of discretization points. By combining
this feature with an efficient Least Square formulation, our solver is also
extremely time-efficient, generating trajectories of multiple seconds within
only a few milliseconds. The performance of the proposed approach has been
validated in a high fidelity simulation environment, by using an UAV platform.
We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and
Programming for Autonomous Robots (SIMPAR 2018
Model Predictive Control for Autonomous Vehicle Tracking
This study develops model predictive control (MPC) schemes for controlling autonomous vehicles tracking on feasible trajectories generated from flatness or polynomial equations. All of the vehicle online moving parameters including coordinate positions, body orientation angle, and steering angle are included into the MPC optimizer for calculating the real-time optimal inputs for the vehicle linear velocity and its steering velocity to minimize the errors between the desired and the actual course of travel. The use of MPC can simplify and eliminate the complexity of controller design since MPC can work itself as a system modelling controller. MPC can also handle on-line the constraints of any variables exceeding their limits. However the high computational demands are the main challenge for this method applying for the real applications
Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness
Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e.,
high-speed and high-acceleration) maneuvers have attracted significant
attention in the past few years. This paper focuses on accurate tracking of
aggressive quadcopter trajectories. We propose a novel control law for tracking
of position and yaw angle and their derivatives of up to fourth order,
specifically, velocity, acceleration, jerk, and snap along with yaw rate and
yaw acceleration. Jerk and snap are tracked using feedforward inputs for
angular rate and angular acceleration based on the differential flatness of the
quadcopter dynamics. Snap tracking requires direct control of body torque,
which we achieve using closed-loop motor speed control based on measurements
from optical encoders attached to the motors. The controller utilizes
incremental nonlinear dynamic inversion (INDI) for robust tracking of linear
and angular accelerations despite external disturbances, such as aerodynamic
drag forces. Hence, prior modeling of aerodynamic effects is not required. We
rigorously analyze the proposed control law through response analysis, and we
demonstrate it in experiments. The controller enables a quadcopter UAV to track
complex 3D trajectories, reaching speeds up to 12.9 m/s and accelerations up to
2.1g, while keeping the root-mean-square tracking error down to 6.6 cm, in a
flight volume that is roughly 18 m by 7 m and 3 m tall. We also demonstrate the
robustness of the controller by attaching a drag plate to the UAV in flight
tests and by pulling on the UAV with a rope during hover.Comment: To be published in IEEE Transactions on Control Systems Technology.
Revision: new set of experiments at increased speed (up to 12.9 m/s), updated
controller design using quaternion representation, new video available at
https://youtu.be/K15lNBAKDC
Online optimisation-based backstepping control design with application to quadrotor
In backstepping implementation, the derivatives of virtual control signals are required at each step. This study provides a novel way to solve this problem by combining
online optimisation with backstepping design in an outer and inner loop manner. The
properties of differential flatness and the B-spline polynomial function are exploited
to transform the optimal control problem into a computationally efficient form. The
optimisation process generates not only the optimised states but also their finite order
derivatives which can be used to analytically calculate the derivatives of virtual control signal required in backstepping design. In addition, the online optimisation repeatedly performed in a receding horizon fashion can also realise local motion planning for obstacle avoidance. The stability of the receding horizon control scheme is analysed via
Lyapunov method which is guaranteed by adding a parametrised terminal condition in the online optimisation. Numerical simulations and flight experiments of a quadrotor unmanned air vehicle are given to demonstrate the effectiveness of the proposed composite control method
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
FlightGoggles is a photorealistic sensor simulator for perception-driven
robotic vehicles. The key contributions of FlightGoggles are twofold. First,
FlightGoggles provides photorealistic exteroceptive sensor simulation using
graphics assets generated with photogrammetry. Second, it provides the ability
to combine (i) synthetic exteroceptive measurements generated in silico in real
time and (ii) vehicle dynamics and proprioceptive measurements generated in
motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of
simulating a virtual-reality environment around autonomous vehicle(s). While a
vehicle is in flight in the FlightGoggles virtual reality environment,
exteroceptive sensors are rendered synthetically in real time while all complex
extrinsic dynamics are generated organically through the natural interactions
of the vehicle. The FlightGoggles framework allows for researchers to
accelerate development by circumventing the need to estimate complex and
hard-to-model interactions such as aerodynamics, motor mechanics, battery
electrochemistry, and behavior of other agents. The ability to perform
vehicle-in-the-loop experiments with photorealistic exteroceptive sensor
simulation facilitates novel research directions involving, e.g., fast and
agile autonomous flight in obstacle-rich environments, safe human interaction,
and flexible sensor selection. FlightGoggles has been utilized as the main test
for selecting nine teams that will advance in the AlphaPilot autonomous drone
racing challenge. We survey approaches and results from the top AlphaPilot
teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be
found at https://flightgoggles.mit.edu. Revision includes description of new
FlightGoggles features, such as a photogrammetric model of the MIT Stata
Center, new rendering settings, and a Python AP
A survey of differential flatness-based control applied to renewable energy sources
Conference ProceedingsThis paper presents an overview of various methods used
to minimize the fluctuating impacts of power generated from
renewable energy sources. Several sources are considered in the
study (biomass, wind, solar, hydro and geothermal). Different
control methods applied to their control are cited, alongside some
previous applications. Hence, it further elaborates on the adoptive
control principles, of which includes; Load ballast control, dummy
load control, proportional integral and derivative (PID) control,
proportional integral (PI) control, pulse-width modulation (PWM)
control, buck converter control, boost converter control, pitch
angle control, valve control, the rate of river flow at turbine,
bidirectional diffuser-augmented control and differential flatnessbased
controller. These control operations in renewable energy
power generation are mainly based on a steady-state linear control
approach. However, the flatness based control principle has the
ability to resolve the complex control problem of renewable energy
systems while exploiting their linear properties. Using their
flatness properties, feedback control is easily achieved which
allows for optimal/steady output of the system components. This
review paper highlights the benefits that range from better control
techniques for renewable energy systems to established robust grid
(or standalone generations) connections that can bring immense
benefits to their operation and maintenance costs
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