Nonlinear control of three‐dimensional underactuated vehicles

Fetzer, K.; Nersesov, Sergey G.; Ashrafiuon, Hashem

doi:10.1002/rnc.4833

Cited by 15 publications

(14 citation statements)

References 23 publications

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“…38,39 The most recent work that is applicable to more generic three-dimensional (3D) underactuated vehicle models addresses the path following problem rather than trajectory tracking. 40 This work builds on our previous works on 2D vehicles, 1 3D vehicles with one degree of underactuation, 2 and early research presented in Reference 41. Our previous work in Reference 2 had limited application since most available underactuated 3D vehicles have two degrees of underactuation.…”

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confidence: 87%

Trajectory tracking control of spatial underactuated vehicles

Fetzer

Nersesov

Ashrafiuon

2021

Intl J Robust & Nonlinear

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A common approach to control of underactuated vehicles is a prelude to cooperative control of heterogeneous networks. This article presents a novel nonlinear trajectory tracking control framework for general three-dimensional models of underactuated vehicles with twelve states and four control inputs, including underwater, air, and space vehicles. Given a desired reference trajectory for four of the six degrees of freedom, feasible state trajectories are generated using the second-order nonholonomic constraints in the vehicle equations of motion. A novel transformation is then introduced to formulate the error dynamics in a simplified form. The error dynamics is stabilized using a traditional nonlinear control approach. The control law is shown to uniformly asymptotically stabilize all six pose states assuming bounded unknown uncertainties and disturbances. Adaptation of the method to underwater vehicles is presented along with simulation under highly nonlinear conditions. The approach is also applied to quadrotors and both simulation and experimental results are presented. K E Y W O R D Sbackstepping control, trajectory tracking control, underactuated vehicles INTRODUCTIONAutonomous vehicles have become more practical and thus more prevalent as a consequence of sensors, computers, and power electronics becoming better, cheaper, and smaller. However, controlling the motion of such vehicles is complex, as they tend to be underactuated, that is, fewer actuators than degrees of freedom. These include cars, surface vessels, underwater vessels, and air vehicles. In one of our previous works 1 we considered developing a control algorithm that can be applied to any planar underactuated vehicle. We then extended that work to Spatial vehicle models but with only one degree of underactuation. 2 In this work, we attempt to develop a control algorithm applicable to a variety of spatial underactuated vehicles with two degrees of underactuation including underwater vessels and air vehicles.Considering underwater vehicles, a submarine's propeller produces a surge force along the axis of motion; this is the translational actuator. There are also rotational actuators. A rudder induces yaw motion and diving planes induce pitching motion. 3 Roll motion, however, is a bit more complicated. Many manned submarines are designed to be passively self-righting and therefore do not have active roll control, with some exceptions where roll motion is desired, such as the DeepFlight Super Falcon. 4 There are several works that ignore the roll motion or at least its coupling with the other degrees of freedom and use five degree-of-freedom (DOF) models. [5][6][7] These methods concentrate on uncertainty and disturbance rejection as well as other important topics such as actuation limits and velocity estimation. 8

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confidence: 87%

Trajectory tracking control of spatial underactuated vehicles

Fetzer

Nersesov

Ashrafiuon

2021

Intl J Robust & Nonlinear

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“…In the past decades, a host of remarkable control methods have been developed for trajectory tracking control of AUVs, such as backstepping control (BC), [7] adaptive control, [8][9][10] sliding mode control (SMC), [11][12][13] model predictive control (MPC), [14][15][16] fuzzy control, [17] neural networks control, [18] etc. Shen et al investigated the nonlinear model predictive control of AUVs, where a distributed implementation strategy was proposed to alleviate the computational burden by decomposing the original optimization problems into smaller size subproblems.…”

Section: Doi: 101002/adts202100445mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization

Wang

et al. 2022

Advcd Theory and Sims

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This paper proposes an adaptive backstepping sliding mode control (ABSMC) scheme for autonomous underwater vehicles (AUVs) subject to the dynamic uncertainty, external disturbance and quantization error. The control input signals including control forces and moment are quantized by a hybrid quantizer which is the combinination of a logarithmic quantizer and a uniform quantizer. The kinematic controller is designed by the backstepping control technique and the dynamic controller is developed using the sliding mode control method. In order to further improve the robustness of the closed-loop system, an adaptive law is employed to estimate the upper bound of the total uncertainties in real time. The stability of the closed-loop system is proved based on the Lyapunov theory and indicates that the proposed control method can force the AUV to track the desired trajectory. Simulation results demonstrate the effectiveness of the proposed control strategy.

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“…Underactuated mechanical systems exist due to different reasons, [1][2][3] such as the failure of the actuator, the inherent dynamics of the system or just by the design; but a common challenge for control of the systems is the complicated coupling resulting from the underactuation. To deal with the underactuation problem, effective technique like the partial feedback linearization (PFL), 4 the backstepping, 5,6 the energy-based control methods such as the interconnection and damping assignment passivity-based control (IDA-PBC), 7,8 the controlled Lagrangian method (CL) 9,10 are proposed.…”

Section: Introductionmentioning

confidence: 99%

Energy shaping control for systems with underactuation degrees two by controlled Lagrangian method

Liu

2022

Intl J Robust & Nonlinear

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This article investigates underactuated systems' energy shaping control by controlled Lagrangian (CL) method. The existence of CL based control depends on a set of matching conditions, which are usually in terms of partial differential equations (PDEs). In this work, the matching conditions are derived and simplified for underactuation‐degrees‐two systems. Moreover, the solvability conditions are provided for a class of systems satisfying Assumptions 1 and 2, based on which the controlled energy could be explicitly solved. By the proposed matching solution method, the desired equilibrium can be effectively assigned. The proposed matching controller is implemented for a strongly coupled single rigid body system with six degrees of freedom for the first time. Smooth state feedback control laws guaranteeing asymptotic stability of the system are presented. The range of the control parameters could be obtained based on the positive definiteness of reshaped closed‐loop energy. Simulations for comparison with relate work show the effectiveness of the theoretical results.

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Nonlinear control of three‐dimensional underactuated vehicles

Cited by 15 publications

References 23 publications

Trajectory tracking control of spatial underactuated vehicles

Trajectory tracking control of spatial underactuated vehicles

Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization

Energy shaping control for systems with underactuation degrees two by controlled Lagrangian method

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