A Virtual Rider for Motorcycles: Maneuver Regulation of a Multi-Body Vehicle Model

Saccon, Alessandro; Hauser, John; Beghi, Alessandro

doi:10.1109/tcst.2011.2181170

Cited by 26 publications

(14 citation statements)

References 32 publications

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“…with statex w = [w TvTΦ T ] T , inputū = [ω TF ] T and suitablef . Remark 1: The general theory regarding the transverse coordinates is introduced in [29] and used to design a maneuver regulation controller for a bi-dimensional case in [30]. Differently from [30], we use the transverse coordinates in a more general three-dimensional case and in order to develop a trajectory optimization strategy rather than a controller.…”

Section: B Transverse Dynamicsmentioning

confidence: 99%

Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments

Spedicato

Notarstefano

2018

IEEE Trans. Contr. Syst. Technol.

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Abstract-In this paper, we present a novel strategy to compute minimum-time trajectories for quadrotors in constrained environments. In particular, we consider the motion in a given flying region with obstacles and take into account the physical limitations of the vehicle. Instead of approaching the optimization problem in its standard time-parameterized formulation, the proposed strategy is based on an appealing re-formulation. Transverse coordinates, expressing the distance from a frame path, are used to parameterize the vehicle position and a spatial parameter is used as independent variable. This re-formulation allows us to (i) obtain a fixed horizon problem and (ii) easily formulate (fairly complex) position constraints. The effectiveness of the proposed strategy is proven by numerical computations on two different illustrative scenarios. Moreover, the optimal trajectory generated in the second scenario is experimentally executed with a real nano-quadrotor in order to show its feasibility.

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Section: B Transverse Dynamicsmentioning

confidence: 99%

Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments

Spedicato

Notarstefano

2018

IEEE Trans. Contr. Syst. Technol.

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“…Path following, on the other hand, separates these concerns by allowing the user to specify a reference (feasible) path parameterized with a scalar, θ (instead of time), x r (θ) yields a state for each θ and dxr dθ = r(θ). As proposed by Hauser et al, one could define Π as a function that maps a state x to the closest state on the reference trajectory x r (·), using an auxiliary map π [1], [31]: where ||x|| 2 P : x t P x is a Lyapunov function for the linearized dynamics around the reference trajectory. In order to stabilize the system to the reference path, Hauser et al propose to decrease the value of ||x − Π(x)|| 2 P .…”

Section: B Trajectory Tracking Vs Path Followingmentioning

confidence: 99%

Path-Following through Control Funnel Functions

Ravanbakhsh

Aghli

Heckman

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We present an approach to path following using so-called control funnel functions. Synthesizing controllers to "robustly" follow a reference trajectory is a fundamental problem for autonomous vehicles. Robustness, in this context, requires our controllers to handle a specified amount of deviation from the desired trajectory. Our approach considers a timing law that describes how fast to move along a given reference trajectory and a control feedback law for reducing deviations from the reference. We synthesize both feedback laws using "control funnel functions" that jointly encode the control law as well as its correctness argument over a mathematical model of the vehicle dynamics. We adapt a previously described demonstration-based learning algorithm to synthesize a control funnel function as well as the associated feedback law. We implement this law on top of a 1/8th scale autonomous vehicle called the Parkour car. We compare the performance of our path following approach against a trajectory tracking approach by specifying trajectories of varying lengths and curvatures. Our experiments demonstrate the improved robustness obtained from the use of control funnel functions.

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“…The equations (30) are equivalent to the equations (6-8) but they are only functions of the longitudinal coordinate s, the transverse coordinates w 1 , w 2 and the state trajectoryx x x ξ (·). Furthermore, the equations (9)(10)(11)(12)(13)(14)(15)(16)(17) can be expressed as function of s, w w w andx x x ξ (·), using the coordinate transformation from x x x to (s, w w w) and equations (25). Taking the time derivative of x i = w i−1 +x iξ , the equationsẋ i = f i (x x x, u u u) can be written aṡ…”

Section: Transverse Linearization Based Maneuver Regulation Controllermentioning

confidence: 99%

“…We design a feedback matrixK(·) that asymptotically stabilizes the transverse linearization by solving a linear quadratic regulator problem. If the transverse linearization is exponentially stabilized by an s-varing linear state feedback,ū u u w = −K(s)w w w, then the nonlinear feedback u u u =ū u u ξ (s) −K(s)w w w (33) exponentially stabilizes the maneuver [x x x ξ ] for (6-17) [14]. The controller in (33) can be rewritten by exploiting the dependence of s from the system output (s = φ 1 (y y y)) as u u u =ū u u ξ (φ 1 (y y y)) −K(φ 1 (y y y))w w w.…”

Section: Transverse Linearization Based Maneuver Regulation Controllermentioning

confidence: 99%

See 1 more Smart Citation

Aggressive Maneuver Regulation of a Quadrotor UAV

Spedicato

Notarstefano

Bülthoff

et al. 2016

Springer Tracts in Advanced Robotics

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In this paper we design a nonlinear controller for aggressive maneuvering of a quadrotor. We take a maneuver regulation perspective. Differently from the classical trajectory tracking approach, maneuver regulation does not require following a timed reference state, but a geometric "path" with a velocity (and possibly orientation) profile assigned on it. The proposed controller relies on three main ideas. Given a desired maneuver, i.e., a set of state trajectories equivalent under time translations, the system dynamics is decomposed into dynamics longitudinal and transverse to the maneuver. A space-dependent version of the transverse dynamics is derived, by using the longitudinal state, i.e., the arc-length of the path, as an independent variable. Then the controller is obtained as a function of the arc-length consisting of two terms: a feedforward term, being the nominal input to apply when on the path at the current arc-length, and a feedback term exponentially stabilizing the state-dependent transverse dynamics. Numerical computations are presented to prove the effectiveness of the proposed strategy. The controller performances are tested in presence of uncertainty of the model parameters and input noise and saturations. The controller is also tested in a realistic simulation environment validated against an experimental test-bed.

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A Virtual Rider for Motorcycles: Maneuver Regulation of a Multi-Body Vehicle Model

Cited by 26 publications

References 32 publications

Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments

Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments

Path-Following through Control Funnel Functions

Aggressive Maneuver Regulation of a Quadrotor UAV

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