40 research outputs found

    Suspended Load Path Tracking Control Using a Tilt-rotor UAV Based on Zonotopic State Estimation

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    This work addresses the problem of path tracking control of a suspended load using a tilt-rotor UAV. The main challenge in controlling this kind of system arises from the dynamic behavior imposed by the load, which is usually coupled to the UAV by means of a rope, adding unactuated degrees of freedom to the whole system. Furthermore, to perform the load transportation it is often needed the knowledge of the load position to accomplish the task. Since available sensors are commonly embedded in the mobile platform, information on the load position may not be directly available. To solve this problem in this work, initially, the kinematics of the multi-body mechanical system are formulated from the load's perspective, from which a detailed dynamic model is derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear state-space representation of the system, affine in the inputs, with the load's position and orientation directly represented by state variables. A zonotopic state estimator is proposed to solve the problem of estimating the load position and orientation, which is formulated based on sensors located at the aircraft, with different sampling times, and unknown-but-bounded measurement noise. To solve the path tracking problem, a discrete-time mixed H2/Hโˆž\mathcal{H}_2/\mathcal{H}_\infty controller with pole-placement constraints is designed with guaranteed time-response properties and robust to unmodeled dynamics, parametric uncertainties, and external disturbances. Results from numerical experiments, performed in a platform based on the Gazebo simulator and on a Computer Aided Design (CAD) model of the system, are presented to corroborate the performance of the zonotopic state estimator along with the designed controller

    A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems

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    Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008โ€“2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests

    Multi-rotor with suspended load: System Dynamics and Control Toolbox

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    There is an increasing demand for Unmanned Aerial Systems (UAS) to carry suspended loads as this can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. The constant variation in operating point induced by the slung load also causes conventional controllers to demand increased control effort. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present a novel controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions. The paper describes a System Dynamics and Control Simulation Toolbox for use with MATLAB/SIMULINK which includes a detailed simulation of the multi-rotor and slung load as well as a predictive controller to manage the nonlinear dynamics whilst accounting for system constraints. It is demonstrated that the controller simultaneously tracks specified waypoints and actively damps large slung load oscillations. A linear-quadratic regulator (LQR) is derived and control performance is compared. Results show the improved performance of the predictive controller for a larger flight envelope, including aggressive manoeuvres and large slung load displacements. The computational cost remains relatively small, amenable to practical implementations

    ๋น„์„ ํ˜• ์ตœ์ ํ™”๋ฅผ ์ด์šฉํ•œ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ํ˜„์ˆ˜ ์šด์†ก์˜ ๊ฒฝ๋กœ ๊ณ„ํš ๋ฐ ์ œ์–ด ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2021.8. ๊น€ํ˜„์ง„.๊ฒฝ๋กœ ๊ณ„ํš๊ณผ ์ œ์–ด๋Š” ์•ˆ์ „ํ•˜๊ณ  ์•ˆ์ •์ ์œผ๋กœ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๋ฅผ ์šด์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ•„์ˆ˜์ ์ธ ์š”์†Œ์ด๋‹ค. ์ถฉ๋Œ์„ ํšŒํ”ผํ•˜๋ฉฐ ํšจ์œจ์ ์ธ ๊ฒฝ๋กœ๋ฅผ ์ƒ์„ฑํ•˜๊ณ  ์ด๋ฅผ ์‹ค์ œ๋กœ ์ถ”์ข…ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋™์—ญํ•™ ๋ชจ๋ธ์ด ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ์ผ๋ฐ˜ ๋ฉ€ํ‹ฐ๋กœํ„ฐ์˜ ๋™์—ญํ•™ ๋ชจ๋ธ์€ ๋†’์€ ์ฐจ์›์„ ๊ฐ€์ง„ ๋น„์„ ํ˜•์‹์œผ๋กœ ํ‘œํ˜„๋˜๋Š”๋ฐ, ํ˜„์ˆ˜ ์šด์†ก ๋ฌผ์ฒด๋ฅผ ์ถ”๊ฐ€ํ•  ๊ฒฝ์šฐ ๊ณ„์‚ฐ์ด ๋”์šฑ ๋ณต์žกํ•ด์ง„๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๋ฅผ ์ด์šฉํ•œ ํ˜„์ˆ˜ ์šด์†ก์— ์žˆ์–ด ๊ฒฝ๋กœ ๊ณ„ํš๊ณผ ์ œ์–ด์— ๋Œ€ํ•œ ํšจ์œจ์ ์ธ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ ๋‹จ์ผ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๋ฅผ ์ด์šฉํ•œ ํ˜„์ˆ˜ ์šด์†ก์„ ๋‹ค๋ฃฌ๋‹ค. ๋ฌผ์ฒด๊ฐ€ ๋ณ„๋„์˜ ์—‘์ธ„์—์ดํ„ฐ ์—†์ด ์šด์†ก๋  ๊ฒฝ์šฐ ๋ฌผ์ฒด๋Š” ๊ธฐ์ฒด์˜ ์›€์ง์ž„์— ์˜ํ•ด์„œ๋งŒ ์ œ์–ด๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค. ํ•˜์ง€๋งŒ, ๋™์—ญํ•™์‹์˜ ๋†’์€ ๋น„์„ ํ˜•์„ฑ์œผ๋กœ ์šด์šฉ์— ์–ด๋ ค์›€์ด ์กด์žฌํ•œ๋‹ค. ์ด๋ฅผ ๊ฒฝ๊ฐ์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ ํšŒ์ „ ๋™์—ญํ•™์‹์˜ ๋น„์„ ํ˜•์„ฑ์„ ์ค„์ด๊ณ  ์ž์„ธ ์ œ์–ด์— ์กด์žฌํ•˜๋Š” ์‹œ๊ฐ„ ์ง€์—ฐ์„ ๊ณ ๋ คํ•˜์—ฌ ๋™์—ญํ•™์‹์„ ๊ฐ„์†Œํ™”ํ•œ๋‹ค. ๊ฒฝ๋กœ ๊ณ„ํš์— ์žˆ์–ด์„œ๋Š” ์ถฉ๋Œ ํšŒํ”ผ๋ฅผ ์œ„ํ•ด ๊ธฐ์ฒด, ์ผ€์ด๋ธ”, ๊ทธ๋ฆฌ๊ณ  ์šด์†ก ๋ฌผ์ฒด๋ฅผ ๋‹ค๋ฅธ ํฌ๊ธฐ์™€ ๋ชจ์–‘์„ ๊ฐ€์ง„ ํƒ€์›์ฒด๋“ค๋กœ ๊ฐ์‹ธ๋ฉฐ, ํšจ๊ณผ์ ์ด๋ฉด์„œ๋„ ๋œ ๋ณด์ˆ˜์ ์ธ ๋ฐฉ์‹์œผ๋กœ ์ถฉ๋Œ ํšŒํ”ผ ๊ตฌ์†์กฐ๊ฑด์„ ๋ถ€๊ณผํ•œ๋‹ค. Augmented Lagrangian ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋น„์„ ํ˜• ๊ตฌ์†์กฐ๊ฑด์ด ๋ถ€๊ณผ๋œ ๋น„์„ ํ˜• ๋ฌธ์ œ๋ฅผ ์‹ค์‹œ๊ฐ„ ์ตœ์ ํ™”ํ•˜์—ฌ ๊ฒฝ๋กœ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ์ƒ์„ฑ๋œ ๊ฒฝ๋กœ๋ฅผ ์ถ”์ข…ํ•˜๊ธฐ ์œ„ํ•ด์„œ Sequential linear quadratic ์†”๋ฒ„๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ธ ์˜ˆ์ธก ์ œ์–ด๊ธฐ๋กœ ์ตœ์  ์ œ์–ด ์ž…๋ ฅ์„ ๊ณ„์‚ฐํ•œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ ์—ฌ๋Ÿฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ๋‹ค์ค‘ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๋ฅผ ์ด์šฉํ•œ ํ˜‘์—… ํ˜„์ˆ˜ ์šด์†ก ์‹œ์Šคํ…œ์„ ๋‹ค๋ฃฌ๋‹ค. ํ•ด๋‹น ์‹œ์Šคํ…œ์˜ ์ƒํƒœ ๋ณ€์ˆ˜๋‚˜ ๋™์—ญํ•™์‹์—์„œ ์—ฐ๊ฒฐ๋œ(coupled) ํ•ญ์˜ ๊ฐœ์ˆ˜๋Š” ๊ธฐ์ฒด์˜ ์ˆ˜์— ๋น„๋ก€ํ•˜์—ฌ ์ฆ๊ฐ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ํšจ๊ณผ์ ์ธ ๊ธฐ๋ฒ• ์—†์ด๋Š” ์ตœ์ ํ™”์— ๋งŽ์€ ์‹œ๊ฐ„์ด ์†Œ์š”๋œ๋‹ค. ๋†’์€ ๋น„์„ ํ˜•์„ฑ์„ ๊ฐ€์ง„ ๋™์—ญํ•™์‹์˜ ๋ณต์žก์„ฑ์„ ๋‚ฎ์ถ”๊ธฐ ์œ„ํ•˜์—ฌ ๋ฏธ๋ถ„ ํ‰ํƒ„์„ฑ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๊ฒฝ๋กœ ๋˜ํ•œ piece-wise Bernstein ๋‹คํ•ญ์‹์„ ์ด์šฉํ•˜์—ฌ ๋งค๊ฐœ๋ณ€์ˆ˜ํ™”ํ•˜์—ฌ ์ตœ์ ํ™” ๋ณ€์ˆ˜์˜ ๊ฐœ์ˆ˜๋ฅผ ์ค„์ธ๋‹ค. ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ๋ถ„ํ•ดํ•˜๊ณ  ์ถฉ๋Œ ํšŒํ”ผ ๊ตฌ์†์กฐ๊ฑด๋“ค์— ๋Œ€ํ•ด ๋ณผ๋กํ™”(convexification)๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ์šด์†ก ๋ฌผ์ฒด์˜ ๊ฒฝ๋กœ์™€ ์žฅ๋ ฅ์˜ ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ๋ณผ๋กํ•œ(convex) ํ•˜์œ„๋ฌธ์ œ๋“ค์ด ๋งŒ๋“ค์–ด์ง„๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•˜์œ„๋ฌธ์ œ์ธ ๋ฌผ์ฒด ๊ฒฝ๋กœ ์ƒ์„ฑ์—์„œ๋Š”, ์žฅ์• ๋ฌผ ํšŒํ”ผ์™€ ๋ฉ€ํ‹ฐ๋กœํ„ฐ์˜ ๊ณต๊ฐ„์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์•ˆ์ „ ๋น„ํ–‰ ํ†ต๋กœ(safe flight corridor, SFC)์™€ ์—ฌ์œ  ๊ฐ„๊ฒฉ ๊ตฌ์†์กฐ๊ฑด์„ ๊ณ ๋ คํ•˜์—ฌ ์ตœ์ ํ™”ํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์žฅ๋ ฅ ๋ฒกํ„ฐ๋“ค์˜ ๊ฒฝ๋กœ๋Š” ์žฅ์• ๋ฌผ ํšŒํ”ผ์™€ ์ƒํ˜ธ ์ถฉ๋Œ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์•ˆ์ „ ๋น„ํ–‰ ์„นํ„ฐ(safe flight sector, SFS)์™€ ์ƒ๋Œ€ ์•ˆ์ „ ๋น„ํ–‰ ์„นํ„ฐ(relative safe flight sector, RSFS) ๊ตฌ์†์กฐ๊ฑด์„ ๋ถ€๊ณผํ•˜์—ฌ ์ตœ์ ํ™”ํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹คํ—˜์œผ๋กœ ๋ณต์žกํ•œ ํ™˜๊ฒฝ์—์„œ ํšจ์œจ์ ์ธ ๊ฒฝ๋กœ ๊ณ„ํš ๊ธฐ๋ฒ•์„ ์‹œ์—ฐํ•˜๋ฉฐ ๊ฒ€์ฆํ•œ๋‹ค.Trajectory generation and control are fundamental requirements for safe and stable operation of multi-rotors. The dynamic model should be considered to generate efficient and collision-free trajectories with feasibility. While the dynamic model of a bare multi-rotor is expressed non-linearly with high dimensions which results in computational loads, the suspended load increases the complexity further. This dissertation presents efficient algorithms for trajectory generation and control of multi-rotors with a suspended load. A single multi-rotor with a suspended load is addressed first. Since the load is suspended through a cable without any actuator, movement of the load must be controlled via maneuvers of the multi-rotor. However, the highly non-linear dynamics of the system results in difficulties. To relive them, the rotational dynamics is simplified to reduce the non-linearity and consider the delay in attitude control. For trajectory generation, the vehicle, cable, and load are considered as ellipsoids with different sizes and shapes, and collision-free constraints are expressed in an efficient and less-conservative way. The augmented Lagrangian method is applied to solve a nonlinear optimization problem with nonlinear constraints in real-time. Model predictive control with the sequential linear quadratic solver is used to track the generated trajectories. The proposed algorithm is validated with several simulations and experiment. A system with multiple multi-rotors for cooperative transportation of a suspended load is addressed next. As the system has more state variables and coupling terms in the dynamic equation than the system with a single multi-rotor, optimization takes a long time without an efficient method. The differential flatness of the system is used to reduce the complexity of the highly non-linear dynamic equation. The trajectories are also parameterized using piece-wise Bernstein polynomials to decrease the number of optimization variables. By decomposing an optimization problem and performing convexification, convex sub-problems are formulated for the load and the tension trajectories optimization, respectively. In each sub-problem, a light-weight sampling method is used to find a feasible and low-cost trajectory as initialization. In the first sub-problem, the load trajectory is optimized with safe flight corridor (SFC) and clearance constraints for collision avoidance and security of space for the multi-rotors. Then, the tension histories are optimized with safe flight sector (SFS) and relative safe flight sector (RSFS) constraints for obstacle and inter-agent collision avoidance. Simulations and experiments are conducted to demonstrate efficient trajectory generation in a cluttered environment and validate the proposed algorithms.Chapter 1 Introduction 1 1.1 Literature Survey 5 1.2 Contributions 9 1.3 Outline 10 Chapter 2 Single Multi-rotor with a Suspended Load 11 2.1 Dynamics 11 2.2 Trajectory Generation 23 2.3 Optimal Control 31 Chapter 3 Multiple Multi-rotors with a Suspended Load 36 3.1 Problem Setting 36 3.2 Load Trajectory Generation 45 3.3 Tension History Generation 54 Chapter 4 Experimental Validation 68 4.1 Single Multi-rotor with a Suspended Load 68 4.2 Multiple Multi-rotors with a Suspended Load 79 Chapter 5 Conclusion 100 Appendix A Detailed Derivation of Dierential Flatness 102 B Preliminaries of Bernstein Polynomials 108 B.1 Denition of a Bernstein Polynomial 108 B.2 Convex hull property of a Bernstein Polynomial 110 B.3 Representation of a General Polynomial with Bernstein Basis Polynomials 111 B.4 Representation of the Derivative of a Bernstein Polynomial with Bernstein Basis Polynomials 112 References 113 Abstract (in Korean) 119๋ฐ•

    Machine learning techniques to estimate the dynamics of a slung load multirotor UAV system

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    This thesis addresses the question of designing robust and flexible controllers to enable autonomous operation of a multirotor UAV with an attached slung load for general cargo transport. This is achieved by following an experimental approach; real flight data from a slung load multirotor coupled system is used as experience, allowing for a computer software to estimate the pose of the slung in order to propose a swing-free controller that will dampen the oscillations of the slung load when the multirotor is following a desired flight trajectory. The thesis presents the reader with a methodology describing the development path from vehicle design and modelling over slung load state estimators to controller synthesis. Attaching a load via a cable to the underside of the aircraft alters the mass distribution of the combined "airborne entity" in a highly dynamic fashion. The load will be subject to inertial, gravitational and unsteady aerodynamic forces which are transmitted to the aircraft via the cable, providing another source of external force to the multirotor platform and thus altering the flight dynamic response characteristics of the vehicle. Similarly the load relies on the forces transmitted by the multirotor to alter its state, which is much more difficult to control. The principle research hypothesis of this thesis is that the dynamics of the coupled system can be identified by applying Machine Learning techniques. One of the major contributions of this thesis is the estimator that uses real flight data to train an unstructured black-box algorithm that can output the position vector of the load using the vehicle pose and pilot pseudo-controls as input. Experimental results show very accurate position estimation of the load using the machine learning estimator when comparing it with a motion tracking system (~2% offset). Another contribution lies in the avionics solution created for data collection, algorithm execution and control of multirotor UAVs, experimental results show successful autonomous flight with a range of algorithms and applications. Finally, to enable flight capabilities of a multirotor with slung load, a control system is developed that dampens the oscillations of the load; the controller uses a feedback approach to simultaneously prevent exciting swing and to actively dampen swing in the slung load. The methods and algorithms developed in this thesis are validated by flight testing

    Adaptive backstepping controller design of quadrotor biplane for payload delivery

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    Performance of the UAVs for a particular application can be enhanced by hybrid design, where take-off, hover, and landing happen like rotary-wing UAVs, and flies like fixed-wing UAVs. A backstepping controller and an adaptive backstepping controller are designed for trajectory tracking and payload delivery in a medical emergency or medical substance delivery like vaccine delivery in the presence of wind gust. Simulation results show that the backstepping controller effectively tracks the trajectory during the entire flight envelope, including take-off, hovering, the transition phase, level flight mode, and landing. A comparison between Backstepping, Integral Terminal Sliding Mode (ITSMC) and Adaptive Backstepping controllers for payload delivery show that the adaptive backstepping controller effectively tracks the altitude and attitude. ITSMC is capable of tracking the desired trajectory for a change in the mass but has sluggish response. The backstepping controller generates a steady-state error in altitude during the mass change in biplane-quadrotor.The publication of this article was funded by Qatar National Library.Scopu

    Modeling, Estimation, and Control of Helicopter Slung Load System

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    Optimal Control of Multiple Quadrotors for Transporting a Cable Suspended Payload

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    In this thesis, the main aim is to improve the flight control performance for a cable suspended payload with single and two quadrotors based on optimised control techniques. The study utilised optimal controllers, such as the Linear Quadratic Regulator LQR, the Iterative based LQR (ILQR), the Model Predictive Control MPC and the dynamic game controller to solve tracking control problems in terms of stabilisation, accuracy, constraints and collision avoidance. The LQR control was applied to the system as the first control method and compared with the classical Proportional-Derivative controller PD. It was used to achieve the load path tracking performance for single and two quadrotors with a cable slung load. The second controller was ILQR, which was developed based on the LQR control method to deal with the model nonlinearity. The MPC technique was also applied to the linearised nonlinear model LMPC of two quadrotors with a payload suspended by cables and compared with a nonlinear MPC (NMPC). Both MPC controllers LMPC and NMPC considered the constraints imposed on the system states and control inputs. The dynamic game control method was developed based on an incentive strategy for a leader-follower framework with the consideration of different optimal cost functions. It was applied to the linearised nonlinear model. Selecting these control techniques led to a number of achievements. Firstly, they improved the system performance in terms of achieving the system stability and reducing the steady-state errors. Secondly, the system parameter uncertainties were taken into consideration by utilising the ILQR controller. Thirdly, the MPC controllers guaranteed the handling of constraints and external disturbances in linear and nonlinear systems. Finally, avoiding collision between the leader and follower robots was achieved by applying the dynamic game controller. The controllers were tested in MATLAB simulation and verified for various desired predefined trajectories. In real experiments, these controllers were used as high-level controllers, which produce the optimised trajectory points. Then a low-level controller (PD controller) was used to follow the optimised trajectory points

    A framework to design interaction control of aerial slung load systems: transfer from existing flight control of under-actuated aerial vehicles

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    This paper establishes a framework within which interaction control is designed for the aerial slung load system composed of an underactuated aerial vehicle, a cable and a load. Instead of developing a new control law for the system, we propose the interaction control scheme by the controllers for under-actuated aerial systems. By selecting the deferentially flat output as the configuration, the equations of motion of the two systems are described in an identical form. The flight control task of the under-actuated aerial vehicle is thus converted into the control of the aerial slung load system. With the help of an admittance filter, the compliant trajectory is generated for the load subject to external interaction force. Moreover, the convergence of the whole system is proved by using the boundedness of the tracking error of vehicle attitude tracking as well as the estimation error of external force. Based on the developed theoretical results, an example is provided to illustrate the design algorithm of interaction controller for the aerial slung load via an existing flight controller directly. The correctness and applicability of the obtained results are demonstrated via the illustrative numerical example
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