12 research outputs found

    Straight-line path following for asymmetric unmanned platform with disturbance estimation

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    The problem of straight-line path following for asymmetric unmanned platform exposed to unknown disturbances was addressed in this paper. The mathematical model of asymmetric unmanned platform was established and the inputs in sway and yaw directions were decoupled, which facilitated the establishment of control strategy of path following. The guidance law and the cross-track error were derived from the classical line-of-sight (LOS) guidance principle. And the equilibrium point of the cross-track error was proven to be uniformly semiglobally exponentially stable (USGES), which guaranteed the exponential convergence to zero. A new disturbance estimation law was developed by adding a linear item of the estimation error into the classical one, which improved the principleโ€™s precision and sensitivity dramatically. The control strategy was developed based on the integrator backstepping technique and the new disturbance estimation law, which made the equilibrium system to be uniformly globally asymptotically stable (UGAS). Computer simulations were conducted to verify the effectiveness of the estimation and control laws during straight-line path following for asymmetric unmanned platform in the presence of unknown disturbances

    Autonomous ROV inspections of aquaculture net pens using DVL

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    This article presents a method for guiding a remotely operated vehicle (ROV) to autonomously traverse an aquaculture net pen. The method is based on measurements from a Doppler velocity log (DVL) and uses the measured length of the DVL beam vectors to approximate the geometry of a local region of the net pen in front of the ROV. The ROV position and orientation relative to this net pen approximation are used as inputs to a nonlinear guidance law. The guidance law is based upon the line-of-sight (LOS) guidance law. By utilizing that an ROV is fully actuated in the horizontal plane, the crosstrack error is minimized independently of the ROV heading. A Lyapunov analysis of the closed-loop system with this guidance law shows that the ROV is able to follow a continuous path in the presence of a constant irrotational ocean current. Finally, results from simulations and experiments demonstrating the performance of the net pen approximation and control system are presented.acceptedVersio

    Motion Control for Autonomous Navigation in Blue and Narrow Waters Using Switched Controllers

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    Autonomous ships represent one of the new frontiers of technological innovation in marine engineering, which demand the development of innovative control systems to guarantee efficient and safe navigation of vessels. A convenient control system should be able to command the several actuators installed on board in different conditions\u2014for instance, during oceanic navigation, harbor approach, narrow channels, and crowed areas. Such tasks are accomplished by different switching controllers for high and low speed motion, which have to be orchestrated to ensure an effective maneuvering. An approach to the design of hierarchies of controllers for maneuvering and navigation of ships equipped with a standard propulsion configuration in both blue and narrow water is proposed. Different levels of control, from global to local, are defined and integrated to steer the vessel in such a way to increase the maneuvering capability in various scenarios

    Fuzzy-Based Optimal Adaptive Line-of-Sight Path Following for Underactuated Unmanned Surface Vehicle with Uncertainties and Time-Varying Disturbances

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    This paper investigates the path following control problem for an underactuated unmanned surface vehicle (USV) in the presence of dynamical uncertainties and time-varying external disturbances. Based on fuzzy optimization algorithm, an improved adaptive line-of-sight (ALOS) guidance law is proposed, which is suitable for straight-line and curve paths. On the basis of guidance information provided by LOS, a three-degree-of-freedom (DOF) dynamic model of an underactuated USV has been used to design a practical path following controller. The controller is designed by combining backstepping method, neural shunting model, neural network minimum parameter learning method, and Nussbaum function. Neural shunting model is used to solve the problem of โ€œexplosion of complexity,โ€ which is an inherent illness of backstepping algorithm. Meanwhile, a simpler neural network minimum parameter learning method than multilayer neural network is employed to identify the uncertainties and time-varying external disturbances. In particular, Nussbaum function is introduced into the controller design to solve the problem of unknown control gain coefficient. And much effort is made to obtain the stability for the closed-loop control system, using the Lyapunov stability theory. Simulation experiments demonstrate the effectiveness and reliability of the improved LOS guidance algorithm and the path following controller

    Finite-Time Observer Based Guidance and Control of Underactuated Surface Vehicles with Unknown Sideslip Angles and Disturbances

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    Suffering from complex sideslip angles, path following control of an under actuated surface vehicle (USV) becomes significantly challenging and remains unresolved. In this paper, a finite-time observer based guidance and control (FOGC) scheme for path following of an USV with time-varying and large sideslip angles and unknown external disturbances is proposed. The salient features of the proposed FOGC scheme are as follows: 1) time-varying large sideslip angle is exactly estimated by a finite-time sideslip observer, and thereby contributing to the sideslip-tangent line-of-sight guidance law which significantly enhances the robustness of the guidance system to unknown sideslip angles which are significantly large and time-varying; 2) a finite-time disturbance observer (FDO) is devised to exactly observe unknown external disturbances, and thereby implementing FDO-based surge and heading robust tracking controllers, which possess remarkable tracking accuracy and precise disturbance rejection, simultaneously; and 3) by virtue of cascade analysis and Lyapunov approach, global asymptotic stability of the integrated guidance-control system is rigorously ensured. Simulation studies and comparisons are conducted to demonstrate the effectiveness and superiority of the proposed FOGC scheme

    A guiding vector field algorithm for path following control of nonholonomic mobile robots

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    In this paper we propose an algorithm for path following control of the nonholonomic mobile robot based on the idea of the guiding vector field (GVF). The desired path may be an arbitrary smooth curve in its implicit form, that is, a level set of a predefined smooth function. Using this function and the robotโ€™s kinematic model, we design a GVF, whose integral curves converge to the trajectory. A nonlinear motion controller is then proposed which steers the robot along such an integral curve, bringing it to the desired path. We establish global convergence conditions for our algorithm and demonstrate its applicability and performance by experiments with wheeled robots

    ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ์กฐ๋ฅ˜ ์™ธ๋ž€์„ ๊ณ ๋ คํ•œ ๊ฒฝ๋กœ ์ถ”์ข… ์ œ์–ด๊ธฐ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ์กฐ์„ ํ•ด์–‘๊ณตํ•™๊ณผ, 2017. 8. ๊น€์šฉํ™˜.์ž‘๋™๊ธฐ์ˆ˜๊ฐ€ ๋ถ€์กฑํ•œ ๋Œ€ํ‘œ์ ์ธ ์šด๋™์ฒด์ธ ์–ด๋ขฐํ˜• ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ๊ฒฝ๋กœ ์ถ”์ข… ๋ฌธ์ œ๋Š” ๋น„์„ ํ˜•์„ฑ์ด ํฐ ์‹œ์Šคํ…œ์˜ ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ๋น„์„ ํ˜• ์ œ์–ด ๋ถ„์•ผ์—์„œ ๋‹ค์–‘ํ•˜๊ฒŒ ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์šดํ•ญ์†๋„๊ฐ€ ๋Š๋ฆฌ๊ณ  ์ œ์–ดํŒ์˜ ํฌ๊ธฐ๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ์ž‘์•„ ์™ธ๋ž€์˜ ์˜ํ–ฅ์ด ํฐ ๊ตฌ์กฐ์  ํŠน์ง•์œผ๋กœ ์ธํ•ด ์กฐ๋ฅ˜์™€ ๊ฐ™์€ ๋ฏธ์ง€์˜ ํ™˜๊ฒฝ ์™ธ๋ž€๊ณผ ๋ชจ๋ธ๋ง ๋ถˆํ™•์‹ค์„ฑ์€ ์–ด๋ขฐํ˜• ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ๊ฒฝ๋กœ ์ถ”์ข… ๋ฌธ์ œ์— ์žˆ์–ด์„œ ๊ทน๋ณตํ•ด์•ผํ•˜๋Š” ๊ณผ์ œ๋กœ ๋– ์˜ฌ๋ž๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋ณตํ•ฉ ์‹œ์„ ๊ฐ ์œ ๋„(Augmented LOS guidance) ๊ธฐ๋ฒ• ๋“ฑ ์™ธ๋ž€์˜ ์˜ํ–ฅ์„ ๋ณด์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์œ ๋„ ๋ฐฉ์‹์„ ์ฑ„ํƒํ•˜๊ฑฐ๋‚˜ ๋ฆฌ์•„ํ”„๋…ธํ”„ ๊ธฐ๋ฒ•(Lyapunov method)์„ ํ™œ์šฉํ•œ ์ œ์–ด ๊ธฐ๋ฒ•, ์ ์‘ ์ œ์–ด ๊ธฐ๋ฒ•(Adaptive control) ๋“ฑ์„ ํ™œ์šฉํ•œ ๋‹ค์–‘ํ•œ ๊ฒฝ๋กœ ์ถ”์ข… ์ œ์–ด๊ธฐ๊ฐ€ ์ œ์•ˆ๋˜์–ด์™”๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๊ฒฝ๋กœ์— ์ ‘ํ•˜๊ณ  ์žˆ๋Š” ๊ฒฝ๋กœ ์ขŒํ‘œ๊ณ„์ƒ์—์„œ์˜ ๊ฒฝ๋กœ ์ดํƒˆ ์˜ค์ฐจ ๊ฐ’๊ณผ ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ์„ ์ฒด๊ณ ์ •์ขŒํ‘œ๊ณ„์—์„œ ์ธก์ •๋˜๋Š” ์†๋„ ์„ฑ๋ถ„๋“ค์„ ์ด์šฉํ•˜์—ฌ ๋ชฉํ‘œ ์„ ์ˆ˜๊ฐ(Desired angle)์„ ๊ณ„์‚ฐํ•˜๋Š” ์ƒˆ๋กœ์šด ํ˜•ํƒœ์˜ ๋น„์„ ํ˜• ์œ ๋„ ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์‹œ์Šคํ…œ์˜ ์•ˆ์ •์„ฑ์„ ์œ„ํ•ด ์œ ๋„ ๋ฒ•์น™ ๊ณ„์ˆ˜๊ฐ€ ์ด์šฉ๋˜์—ˆ์œผ๋ฉฐ, ๋ฆฌ์•„ํ”„๋…ธํ”„ ์•ˆ์ •์„ฑ ์ด๋ก ์„ ์ด์šฉํ•˜์—ฌ ์ œ์•ˆ๋œ ์œ ๋„ ๋ฐฉ์‹์˜ ๊ฒฝ๋กœ ์ˆ˜๋ ด์„ฑ์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์œ ๋„ ๋ฐฉ์‹์€ ๋ฏธ๋„๋Ÿฌ์ง ์†๋„(Side-slip velocity)๊ฐ€ ์กด์žฌํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ ๊ฒฝ๋กœ ์ถ”์ข…์— ๊ฐ€์žฅ ๋งŽ์ด ํ™œ์šฉ๋˜๋Š” ๋ฐฉ์‹์ธ ์‹œ์„ ๊ฐ ์œ ๋„ ๋ฒ•์น™(LOS guidance)๊ณผ ๊ฐ™์€ ํ˜•ํƒœ๋ฅผ ๋ ๊ฒŒ ๋œ๋‹ค. 2์ฐจ์› ์ง์„  ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ๊ฒฝ๋กœ ์ถ”์ข… ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์‹œ์„ ๊ฐ ์œ ๋„(LOS guidance), ๊ทธ๋ฆฌ๊ณ  ๊ฐ•ํ™” ์‹œ์„ ๊ฐ ์œ ๋„ ๊ธฐ๋ฒ•์˜ ํ•˜๋‚˜์ธ ์ ๋ถ„-์‹œ์„ ๊ฐ ์œ ๋„(Integral-LOS guidance) ๋ฐฉ์‹๊ณผ ์„ฑ๋Šฅ์„ ๋น„๊ต๋ถ„์„ ํ•˜์˜€๋‹ค. ํผ์„ผํŠธ ์˜ค๋ฒ„์ŠˆํŠธ(% Overshoot)์™€ ๊ฒฝ๋กœ ์ดํƒˆ ์˜ค์ฐจ์— ๋Œ€ํ•œ ๋น„์šฉ ํ•จ์ˆ˜, ์ •์ƒ์ƒํƒœ ์˜ค์ฐจ(Steady-state error), ๋ฐฉํ–ฅํƒ€ ์†๋„(Rudder rate)์— ๋Œ€ํ•œ ๋น„์šฉ ํ•จ์ˆ˜ ๋“ฑ์„ ๋น„๊ต ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ œ์•ˆ๋œ ์œ ๋„ ๋ฐฉ์‹์€ ์กฐ๋ฅ˜์˜ ์˜ํ–ฅ์ด ์—†๋Š” ํ™˜๊ฒฝ์—์„œ๋Š” ๋‹ค๋ฅธ ์œ ๋„ ๋ฐฉ์‹๊ณผ ๋น„์Šทํ•œ ์„ฑ๋Šฅ์„ ๋ณด์˜€์ง€๋งŒ ์กฐ๋ฅ˜๊ฐ€ ์žˆ๋Š” ํ™˜๊ฒฝ์—์„œ๋Š” ์˜ค๋ฒ„์ŠˆํŠธ์™€ ๊ฒฝ๋กœ ์ดํƒˆ ์˜ค์ฐจ์˜ ์ธก๋ฉด์—์„œ ์„ฑ๋Šฅ์ด ๋›ฐ์–ด๋‚จ์„ ๋ณด์˜€๋‹ค. ๋ชจ๋ธ๋ง ๋ถˆํ™•์‹ค์„ฑ๊ณผ ๋ฏธ์ง€์˜ ์™ธ๋ž€์„ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•ด ์ œ์•ˆ๋œ ์œ ๋„ ๋ฐฉ์‹์„ ์‹ ๊ฒฝํšŒ๋กœ๋ง ๊ธฐ๋ฐ˜ ์ ์‘ ์ œ์–ด ๊ธฐ๋ฒ•์— ์ ์šฉํ•˜์—ฌ ์ตœ์ข…์ ์ธ ๊ฒฝ๋กœ ์ถ”์ข… ์ œ์–ด๊ธฐ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ž‘๋™๊ธฐ์˜ ์œ„์น˜์™€ ์†๋„ ํฌํ™”๋„(saturation)์— ๋”ฐ๋ฅธ ์‹œ์Šคํ…œ์˜ ๋น„์„ ํ˜•์„ฑ์— ์‹ ๊ฒฝํšŒ๋กœ๋ง์ด ์ ์‘ํ•˜์—ฌ ์„ฑ๋Šฅ์ด ์ €ํ•˜๋˜๋Š” ๊ฒƒ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•ด PCH ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ์ผ๋ฐ˜์ ์ธ ๊ตฌ์กฐ์  ํŠน์ง•์— ๋”ฐ๋ผ ํšก๋™์š” ์šด๋™์ด ์ƒ๋žต๋œ 5์ž์œ ๋„ ์šด๋™์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ๋˜ํ•œ ํฌ๊ธฐ๊ฐ€ ์ผ์ •ํ•˜๊ณ  ๋น„ํšŒ์ „์„ฑ์ธ ์กฐ๋ฅ˜ ์กฐ๋ฅ˜์˜ ์†๋ ฅ์ด ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ์†๋ ฅ๋ณด๋‹ค ์ž‘๊ณ  ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ์ถ”๋ ฅ์€ ์ผ์ •ํ•˜๋‹ค๋Š” ๊ฐ€์ • ํ•˜์— ์กฐ๋ฅ˜์— ๋Œ€ํ•œ ์™ธ๋ ฅ์„ ์กฐ๋ฅ˜์— ๋Œ€ํ•œ ์ƒ๋Œ€์†๋„๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‹จ์ˆœํ™”ํ•˜์˜€๋‹ค. ์กฐ๋ฅ˜์— ๋Œ€ํ•œ ์ƒ๋Œ€์†๋„์™€ ๊ด€๋ จ๋œ ์œ ์ฒด๋ ฅ ๋ฏธ๊ณ„์ˆ˜์™€ ์กฐ๋ฅ˜ ์†๋„๋ฅผ ๋ฏธ์ง€์˜ ๊ฐ’์œผ๋กœ ์„ค์ •ํ•œ ํ›„, ์šด๋™๋ฐฉ์ •์‹์„ ๋ชจ๋ธ๋ง์ด ๋œ ํ•ญ๊ณผ ๋ชจ๋ธ๋ง๋˜์ง€ ์•Š์€ ํ•ญ์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. 3์ฐจ์› ๊ณก์„  ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ๊ฒฝ๋กœ ์ถ”์ข… ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ, ์ œ์•ˆ๋œ ๊ฒฝ๋กœ ์ถ”์ข… ์ œ์–ด๊ธฐ์˜ ์ ์‘ ์‹ ํ˜ธ(Adaptive signal)๊ฐ€ ์šด๋™๋ฐฉ์ •์‹์˜ ๋ชจ๋ธ๋ง ๋˜์ง€ ์•Š์€ ํ•ญ์— ์ˆ˜๋ ดํ•˜๋Š” ๊ฒƒ์„ ๋ณด์˜€์œผ๋ฉฐ, ์ˆ˜ํ‰๋ฐฉํ–ฅ ๊ฒฝ๋กœ ์ดํƒˆ ์˜ค์ฐจ 0.1m ์ด๋‚ด, ์ค‘๋ ฅ์˜ ์˜ํ–ฅ์ด ์ž‘์šฉํ•˜๋Š” ์ˆ˜์ง๋ฐฉํ–ฅ์˜ ๊ฒฝ๋กœ ์ดํƒˆ ์˜ค์ฐจ๋Š” 2m ์ด๋‚ด๋กœ ์ถฉ๋ถ„ํžˆ ์šฐ์ˆ˜ํ•œ ๊ฒฝ๋กœ ์ถ”์ข… ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 2 ์žฅ ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ์šด๋™๋ฐฉ์ •์‹ 6 ์ œ 1 ์ ˆ ์ขŒํ‘œ๊ณ„ ์„ค์ • 6 ์ œ 2 ์ ˆ ์šด๋™ํ•™(Kinematics) 9 ์ œ 3 ์ ˆ ์šด๋™๋ฐฉ์ •์‹(Equations of Motion) 10 ์ œ 3 ์žฅ ์œ ๋„ ๋ฒ•์น™(Guidance Law) 14 ์ œ 1 ์ ˆ ์œ ๋„-ํ•ญ๋ฒ•-์ œ์–ด (GNC) ์‹œ์Šคํ…œ 14 ์ œ 2 ์ ˆ ์ œ์–ด ๋ชฉํ‘œ(Control Object) 18 ์ œ 3 ์ ˆ ์‹œ์„ ๊ฐ ์œ ๋„ ๋ฒ•์น™ 19 ์ œ 4 ์ ˆ ์ ๋ถ„-์‹œ์„ ๊ฐ ์œ ๋„ ๋ฒ•์น™ 21 ์ œ 5 ์ ˆ ๋น„์„ ํ˜• ์‹œ์„ ๊ฐ ์œ ๋„ ๋ฐฉ์‹ 22 ์ œ 1 ํ•ญ ์œ ๋„ ๋ฒ•์น™ 22 ์ œ 2 ํ•ญ ๊ฒฝ๋กœ ์ˆ˜๋ ด์„ฑ ์ฆ๋ช… 23 ์ œ 3 ํ•ญ ๋น„์„ ํ˜• ์‹œ์„ ๊ฐ ์œ ๋„ ๋ฐฉ์‹ ํŠน์„ฑ ๋ถ„์„ 26 ์ œ 4 ์žฅ ์ œ์–ด๊ธฐ ์„ค๊ณ„ 29 ์ œ 1 ์ ˆ ์˜์‚ฌ์ œ์–ด(Pseudo Control) ๊ธฐ๋ฒ• 29 ์ œ 2 ์ ˆ ์‹ ๊ฒฝํšŒ๋กœ๋ง ๊ธฐ๋ฐ˜ ์ ์‘ ์ œ์–ด 32 ์ œ 3 ์ ˆ Pseudo Control Hedging (PCH) ๊ธฐ๋ฒ• 35 ์ œ 4 ์ ˆ ์ œ์–ด๊ธฐ ๊ตฌ์„ฑ 37 ์ œ 5 ์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 39 ์ œ 1 ์ ˆ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์กฐ๊ฑด 39 ์ œ 2 ์ ˆ ์˜คํ”ˆ๋ฃจํ”„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 41 ์ œ 3 ์ ˆ ์œ ๋„ ๋ฐฉ์‹์— ๋”ฐ๋ฅธ ๋น„๊ต 45 ์ œ 4 ์ ˆ 3์ฐจ์› ๊ณก์„  ๊ฒฝ๋กœ ์ถ”์ข… 49 ๊ฒฐ ๋ก  54 ์ฐธ๊ณ ๋ฌธํ—Œ 56 Abstract 60Maste

    State relativity and speed-allocated line-of-sight course control for path-following of underwater vehicles

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    Path-following is a primary task for most marine, air or space crafts, especially during autonomous operations. Research on autonomous underwater vehicles (AUV) has received large interests in the last few decades with research incentives emerging from the safe, cost-effective and practical solutions provided by their applications such as search and rescue, inspection and monitoring of pipe-lines ans sub-sea structures. This thesis presents a novel guidance system based on the popular line-of-sight (LOS) guidance law for path-following (PF) of underwater vehicles (UVs) subject to environmental disturbances. Mathematical modeling and dynamics of (UVs) is presented first. This is followed by a comprehensive literature review on guidance-based path-following control of marine vehicles, which includes revised definitions of the track-errors and more detailed illustrations of the general PF problem. A number of advances on relative equations of motion are made, which include an improved understanding of the fluid FLOW frame and expression of its motion states, an analytic method of modeling the signs of forces and moments and the proofs of passivity and boundedness of relative UV systems in 3-D. The revision in the relative equations of motion include the concept of state relativity, which is an improved understanding of relativity of motion states expressed in reference frames and is also useful in incorporating environmental disturbances. In addition, the concept of drift rate is introduced along with a revision on the angles of motion in 3-D. A switching mechanism was developed to overcome a drawback of a LOS guidance law, and the linear and nonlinear stability results of the LOS guidance laws have been provided, where distinctions are made between straight and curved PF cases. The guidance system employs the unique formulation and solution of the speed allocation problem of allocating a desired speed vector into x and y components, and the course control that employs the slip angle for desired heading for disturbance rejection. The guidance system and particularly the general course control problem has been extended to 3-D with the new definition of vertical-slip angle. The overall guidance system employing the revised relative system model, course control and speed allocation has performed well during path-following under strong ocean current and/or wave disturbances and measurement noises in both 2-D and 3-D scenarios. In 2-D and 3-D 4 degrees-of-freedom models (DOF), the common sway-underactuated and fully actuated cases are considered, and in 3-D 5-DOF model, sway and heave underactuated and fully actuated cases are considered. Stability results of the LOS guidance laws include the semi-global exponential stability (SGES) of the switching LOS guidance and enclosure-based LOS guidance for straight and curved paths, and SGES of the loolahead-based LOS guidance laws for curved paths. Feedback sliding mode and PID controllers are applied during PF providing a comparison between them, and simulations are carried out in MatLab

    Reliable and Safe Motion Control of Unmanned Vehicles

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    Unmanned vehicles (UVs) are playing an increasingly significant role in modern daily life. In the past decades, numerous commercial, scientific, and military communities across the world are developing fully autonomous UVs for a variety of applications, such as environmental monitoring and surveillance, post-disaster search and rescue, border patrol, natural resources exploration, and experimental platforms for new technologies verification. The excessive opportunities and threats that come along with these diverse applications have created a niche demand for UVs to extend their capabilities to perform more sophisticated and hazardous missions with greater autonomy, lower costs of development and operation, improved personnel safety and security, extended operational range (reliability) and precision, as well as increased flexibility in sophisticated environments including so-called dirty, dull, harsh, and dangerous missions. In order to successfully and effectively execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, greater autonomy together with more advanced reliable and safe motion control systems are required to offer the critical technologies for ensuring intelligent, safe, reliable, and efficient control of UVs in the presence of disturbances, actuator saturation, and even actuator faults, especially for practical applications. This thesis concentrates on the development of different reliable and safe motion control algorithms/strategies applicable to UVs, in particular, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). A number of contributions pertaining to the fault detection and diagnosis (FDD), fault-tolerant control (FTC), disturbance estimation and compensation, and actuator saturation avoidance have been made in this thesis. In addition to the control problems, this thesis also presents several guidance-related contributions, including adaptive observer-based line-of-sight (LOS) guidance law, time-varying lookahead distance scheme, piecewise path switching criterion for guiding a single UV, as well as a proportional-integral (PI) type of leader-follower formation guidance strategy for a group of UVs
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