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Imran IH, Can A, Stolkin R, Montazeri A. Real-time nonlinear parameter estimation and tracking control of unmanned aerial vehicles in closed-loop. Sci Rep 2023; 13:3125. [PMID: 36813826 PMCID: PMC9947013 DOI: 10.1038/s41598-023-29544-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 02/06/2023] [Indexed: 02/24/2023] Open
Abstract
The real-time unknown parameter estimation and adaptive tracking control problems are investigated in this paper for a six degrees of freedom (6-DOF) of under-actuated quadrotor unmanned aerial vehicle (UAV). A virtual proportional derivative (PD) controller is designed to maintain the translational dynamics. Two adaptive schemes are developed to handle the attitude dynamics of the UAV with several unknown parameters. In the beginning, a classical adaptive scheme (CAS) using the certainty equivalence principle is proposed and designed. The idea is to design a controller for an ideal situation by assuming the unknown parameters were known. Then the unknown parameters are replaced by their estimation. A theoretical analysis is provided to ensure the trajectory tracking of the adaptive controller. However, an inherent drawback of this scheme is that there is no guarantee for the estimated parameters to converge to the actual values. To address this issue, a new adaptive scheme (NAS) is developed as the next step by adding a continuously differentiable function to the control structure. The proposed technique guarantees handling of the parametric uncertainties with an appropriate design manifold. A rigorous analytical proof, numerical simulation analyses, and experimental validation are presented to show the effectiveness of the proposed control design.
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Affiliation(s)
- Imil Hamda Imran
- grid.9835.70000 0000 8190 6402Department of Engineering, Lancaster University, Bailrigg, Lancaster, LA1 4YW UK ,grid.412135.00000 0001 1091 0356Applied Research Center for Metrology, Standards and Testing, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia
| | - Aydin Can
- grid.9835.70000 0000 8190 6402Department of Engineering, Lancaster University, Bailrigg, Lancaster, LA1 4YW UK
| | - Rustam Stolkin
- grid.6572.60000 0004 1936 7486Extreme Robotics Lab (ERL), School of Metallurgy and Materials, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Allahyar Montazeri
- Department of Engineering, Lancaster University, Bailrigg, Lancaster, LA1 4YW, UK.
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Han H, Cheng J, Xi Z, Yao B. Cascade Flight Control of Quadrotors Based on Deep Reinforcement Learning. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3196455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Haoran Han
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Cheng
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhilong Xi
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bingcai Yao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
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Neural Network Based Adaptive Event-Triggered Control for Quadrotor Unmanned Aircraft Robotics. MACHINES 2022. [DOI: 10.3390/machines10080617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
With the aim of addressing the problem of the trajectory tracking control of quadrotor unmanned aircraft robots (UARs), in this study, we developed a neural network and event-triggering mechanism-based adaptive control scheme for a quadrotor UAR control system. The main technologies included this scheme are as follows. (1) Under the condition that only the quadrotor’s position information can be obtained, a modified high-gain state observer-based adaptive dynamic surface control (DSC) method was applied and the tracking control of quadrotor UARs was acquired. (2) An event-triggered mechanism for UARs was designed, in which the energy consumption was greatly reduced and the communication efficiency between the system and the control terminal was improved. (3) By selecting appropriate parameters, appropriate initial conditions for the adaptive laws, and establishing a high-gain state observer, a tracking performance of L∞ could be achieved. Finally, simulation results of the hardware-in-loop strategy are presented. The control method we propose here outperformed the traditional backstepping sliding mode control (BSMC) scheme.
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Hu M, Lee K, Ahn H, Choi A, Kim H, You K. Stabilization and Tracking of a Quadrotor Using Modified Sigmoid Sliding Mode Control. SENSORS 2022; 22:s22103618. [PMID: 35632026 PMCID: PMC9147314 DOI: 10.3390/s22103618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/29/2022] [Accepted: 05/09/2022] [Indexed: 01/14/2023]
Abstract
A modified sigmoid sliding mode control (MS-SMC) approach is proposed for stabilizing and tracking a quadrotor system with a nonlinear sliding surface, where the dynamics model is underactuated, highly coupled, and nonlinear. The constructed nonlinear sliding surface is based on the traditional sliding mode surface with a modified sigmoid function, allowing the initial value to quickly reach equilibrium. A new type of nonlinear SMC is applied for performance improvement of the quadrotor using the proposed modified sigmoid sliding surface. To control the quadrotor effectively, a double-loop control method is used to design the control rate, in which the position subsystem is the outer loop, and the attitude subsystem is the inner loop.With the Lyapunov function, the stability of the overall closed-loop system is ensured by stabilizing each subsystem step by step. Moreover, from a practical point of view, the system performance under the model uncertainties and external disturbances are also considered. The simulation results show that the proposed MS-SMC performs better than the conventional sliding mode control (CSMC) and the back-stepping sliding mode control (BS-SMC) in terms of stabilization and tracking against external disturbances.
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Affiliation(s)
- Mingyuan Hu
- Department of Smart Fab. Technology, Sungkyunkwan University, Suwon 16419, Korea;
| | - Kyunghyun Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (K.L.); (H.A.); (A.C.); (H.K.)
| | - Hyeongki Ahn
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (K.L.); (H.A.); (A.C.); (H.K.)
| | - Ahyeong Choi
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (K.L.); (H.A.); (A.C.); (H.K.)
| | - Hyunchang Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (K.L.); (H.A.); (A.C.); (H.K.)
| | - Kwanho You
- Department of Smart Fab. Technology, Sungkyunkwan University, Suwon 16419, Korea;
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea; (K.L.); (H.A.); (A.C.); (H.K.)
- Correspondence: ; Tel.: +82-31-290-7148
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Yogi SC, Tripathi VK, Behera L. Adaptive Integral Sliding Mode Control Using Fully Connected Recurrent Neural Network for Position and Attitude Control of Quadrotor. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:5595-5609. [PMID: 33881998 DOI: 10.1109/tnnls.2021.3071020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article proposes an adaptive integral sliding mode control (ISMC) strategy for quadrotor control that ensures faster and finite-time convergence along with chattering attenuation. Quadrotor dynamics are assumed to be unknown because of the high degree of parametric uncertainties, including external disturbances. The equivalent control law obtained by ISMC consists of quadrotor dynamics and, thus, cannot be applied to the quadrotor. A new fully connected recurrent neural network (FCRNN) controller has been proposed to mimic the equivalent control instead of estimating the Quadrotor dynamics separately. The proposed FCRNN architecture consists of output feedback to the input layer and the hidden layer, which enhances the approximation capability of FCRNN. All hidden layer neurons receive self-feedback and feedback from other hidden layer neurons, which further strengthens FCRNN's potential to capture complex dynamic characteristics. As learning should happen in finite time, the finite-time stability of the overall system has been guaranteed using the Lyapunov stability theory, and the update laws for FCRNN weights in real time are derived using the same. To show the effectiveness of the proposed approach, a comprehensive analysis has been done against existing SMC strategy and against well-known function approximation techniques, e.g., the radial basis function network (RBFN) and RNN.
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Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.06.079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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A Review on Comparative Remarks, Performance Evaluation and Improvement Strategies of Quadrotor Controllers. TECHNOLOGIES 2021. [DOI: 10.3390/technologies9020037] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The quadrotor is an ideal platform for testing control strategies because of its non-linearity and under-actuated configuration, allowing researchers to evaluate and verify control strategies. Several control strategies are used, including Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), Backstepping, Feedback Linearization Control (FLC), Sliding Mode Control (SMC), and Model Predictive Control (MPC), Neural Network, H-infinity, Fuzzy Logic, and Adaptive Control. However, due to several drawbacks, such as high computation, a large amount of training data, approximation error, and the existence of uncertainty, the commercialization of those control technologies in various industrial applications is currently limited. This paper conducts a thorough analysis of the current literature on the effects of multiple controllers on quadrotors, focusing on two separate approaches: (i) controller hybridization and (ii) controller development. Besides, the limitations of the previous works are discussed, challenges and opportunities to work in this field are assessed, and potential research directions are suggested.
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Shao X, Tian B, Yang W. Fixed-time trajectory following for quadrotors via output feedback. ISA TRANSACTIONS 2021; 110:213-224. [PMID: 33092865 DOI: 10.1016/j.isatra.2020.10.039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 10/09/2020] [Accepted: 10/10/2020] [Indexed: 06/11/2023]
Abstract
A fixed-time trajectory following problem for quadrotors via output feedback is concerned. Based on the inner-outer separation design philosophy, the under-actuated quadrotor is formulated as a hierarchical structure composed by position and attitude dynamics. With an emphasis on removing the demand on unmeasured velocity and eliminating the negative effect of disturbances, fixed-time extended state observers utilizing two kinds of polynomial feedback terms are proposed to simultaneously identify unavailable velocity states and unknown uncertainties with a fixed-time estimation capability. With these observation results, a velocity free fixed-time control protocol is synthesized to enable a satisfied trajectory regulation with a uniform convergence time independent of initial positions, such that a prescribed fixed-time stability and enhanced robustness can be obtained with chattering-free inputs. By virtue of bi-limit homogeneity properties, all error variables of the resultant quadrotor system are demonstrated to be fixed-time convergent. Eventually, the benefits of developed algorithm are illustrated via simulations.
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Affiliation(s)
- Xingling Shao
- Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China.
| | - Biao Tian
- Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Wei Yang
- Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
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9
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Li P, Lin Z, Shen H, Zhang Z, Mei X. Optimized neural network based sliding mode control for quadrotors with disturbances. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:1774-1793. [PMID: 33757210 DOI: 10.3934/mbe.2021092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In this paper, optimized radial basis function neural networks (RBFNNs) are employed to construct a sliding mode control (SMC) strategy for quadrotors with unknown disturbances. At first, the dynamics model of the controlled quadrotor is built, where some unknown external disturbances are considered explicitly. Then SMC is carried out for the position and the attitude control of the quadrotor. However, there are unknown disturbances in the obtained controllers, so RBFNNs are employed to approximate the unknown parts of the controllers. Furtherly, Particle Swarm optimization algorithm (PSO) based on minimizing the absolute approximation errors is used to improve the performance of the controllers. Besides, the convergence of the state tracking errors of the quadrotor is proved. In order to exposit the superiority of the proposed control strategy, some comparisons are made between the RBFNN based SMC with and without PSO. The results show that the strategy with PSO achieves quicker and smoother trajectory tracking, which verifies the effectiveness of the proposed control strategy.
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Affiliation(s)
- Ping Li
- College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
| | - Zhe Lin
- College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
| | - Hong Shen
- College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
| | - Zhaoqi Zhang
- College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
| | - Xiaohua Mei
- College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
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10
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Lin X, Liu J, Yu Y, Sun C. Event-triggered reinforcement learning control for the quadrotor UAV with actuator saturation. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.042] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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11
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Meng R, Chen S, Hua C, Qian J, Sun J. Disturbance observer-based output feedback control for uncertain QUAVs with input saturation. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.096] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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12
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Labbadi M, Boukal Y, Cherkaoui M. Path Following Control of Quadrotor UAV With Continuous Fractional-Order Super Twisting Sliding Mode. J INTELL ROBOT SYST 2020. [DOI: 10.1007/s10846-020-01256-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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Gu W, Valavanis KP, Rutherford MJ, Rizzo A. UAV Model-based Flight Control with Artificial Neural Networks: A Survey. J INTELL ROBOT SYST 2020. [DOI: 10.1007/s10846-020-01227-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Xu Q, Wang Z, Zhen Z. Information fusion estimation-based path following control of quadrotor UAVs subjected to Gaussian random disturbance. ISA TRANSACTIONS 2020; 99:84-94. [PMID: 31629487 DOI: 10.1016/j.isatra.2019.10.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 09/23/2019] [Accepted: 10/04/2019] [Indexed: 06/10/2023]
Abstract
Random disturbance has a detrimental effect on the reliability and safety of quadrotor unmanned aerial vehicles (UAVs). This paper proposes an anti-Gaussian random disturbance control method for the path following of a quadrotor UAV. The quadrotor system is linearized and divided into two subsystems, i.e., a translational subsystem and a rotational subsystem, and hierarchical strategy is used to design the overall control architecture. In order to suppress the negative effects of Gaussian random disturbances and simplify the design process of linear-quadratic optimal output tracking control problem, a new information fusion estimation based robust control named Gaussian information fusion control (GIFC) scheme is proposed. The convergence of the output tracking errors of GIFC system is proved via Lyapunov theory. The proposed GIFC control scheme is employed for position and attitude controller designs to enhance the robustness of the quadrotor system to Gaussian random perturbations. Finally numerical simulation experiments illustrate the effectiveness and robustness of the proposed control strategy.
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Affiliation(s)
- Qingzheng Xu
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Jiangjun Ave, Nanjing 211106, China.
| | - Zhisheng Wang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Jiangjun Ave, Nanjing 211106, China.
| | - Ziyang Zhen
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No.29, Jiangjun Ave, Nanjing 211106, China.
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Robust Backstepping Trajectory Tracking Control of a Quadrotor with Input Saturation via Extended State Observer. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9235184] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Quadrotor unmanned aerial vehicles have become increasingly popular in several applications, and the improvement of their control performance has been documented in several studies. Nevertheless, the design of a high-performance tracking controller for aerial vehicles that reliably functions in the simultaneous presence of model uncertainties, external disturbances, and control input saturation still remains a challenge. In this paper, we present a robust backstepping trajectory tracking control of a quadrotor with input saturation. The controller design accounts for both parameterized uncertainties and external disturbances, whereas a new auxiliary system is proposed to cope with control input saturation. Taking into account that only the position and attitude of the quadrotor are measurable, we devise an extended state observer to supply the estimations of unmeasured states, model uncertainties, and external disturbances. We strictly prove the stability of the closed-loop system by using the Lyapunov theory and demonstrate the effectiveness of the proposed algorithm through numerical simulations.
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16
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Quadcopter Adaptive Trajectory Tracking Control: A New Approach via Backstepping Technique. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9183873] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Nowadays, quadcopter unmanned aerial vehicles play important roles in several real-world applications and the improvement of their control performance has become an increasingly attractive topic of a great number of studies. In this paper, we present a new approach for the design and stability analysis of a quadcopter adaptive trajectory tracking control. Based on the quadcopter nonlinear dynamics model which is obtained by using the Euler–Lagrange approach, the tracking controller is devised via the backstepping control technique. Besides, an adaptive law is proposed to deal with the system parameterized uncertainties and to guarantee that the control input is finite. In addition, the vehicle’s vertical descending acceleration is ensured to not exceed the gravitational acceleration by making use of a barrier Lyapunov function. It is shown that the suitable parameter estimator is stable and the tracking errors are guaranteed to be asymptotically stable simultaneously. By prescribing certain flight conditions, we use numerical simulations to compare the control performance of our method to that of existing approaches. The simulation results demonstrate the effectiveness of the proposed algorithm.
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Robust Dynamic Sliding Mode Control-Based PID–Super Twisting Algorithm and Disturbance Observer for Second-Order Nonlinear Systems: Application to UAVs. ELECTRONICS 2019. [DOI: 10.3390/electronics8070760] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper introduces a robust dynamic sliding mode control algorithm using a nonlinear disturbance observer for system dynamics. The proposed method is applied to provide a rapid adaptation and strictly robust performance for the attitude and altitude control of unmanned aerial vehicles (UAVs). The procedure of the proposed method consists of two stages. First, a nonlinear disturbance observer is applied to estimate the exogenous perturbation. Second, a robust dynamic sliding mode controller integrated with the estimated values of disturbances is presented by a combination of a proportional–integral–derivative (PID) sliding surface and super twisting technique to compensate for the effect of these perturbations on the system. In addition, the stability of a control system is established by Lyapunov theory. A numerical simulation was performed and compared to recently alternative methods. An excellent tracking performance and superior stability of the attitude and altitude control of UAVs, exhibiting a fast response, good adaptation, and no chattering effect in the simulation results proved the robustness and effectiveness of the proposed method.
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Zhou Z, Wang H, Hu Z, Wang Y, Wang H. A Multi-Time-Scale Finite Time Controller for the Quadrotor UAVs with Uncertainties. J INTELL ROBOT SYST 2019. [DOI: 10.1007/s10846-018-0837-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Zhang P, Lai X, Wang Y, Wu M. Motion planning and adaptive neural sliding mode tracking control for positioning of uncertain planar underactuated manipulator. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.01.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Adaptive Trajectory Tracking Control for Underactuated Unmanned Surface Vehicle Subject to Unknown Dynamics and Time-Varing Disturbances. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8040547] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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22
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Mofid O, Mobayen S. Adaptive sliding mode control for finite-time stability of quad-rotor UAVs with parametric uncertainties. ISA TRANSACTIONS 2018; 72:1-14. [PMID: 29224853 DOI: 10.1016/j.isatra.2017.11.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 11/04/2017] [Accepted: 11/24/2017] [Indexed: 06/07/2023]
Abstract
Adaptive control methods are developed for stability and tracking control of flight systems in the presence of parametric uncertainties. This paper offers a design technique of adaptive sliding mode control (ASMC) for finite-time stabilization of unmanned aerial vehicle (UAV) systems with parametric uncertainties. Applying the Lyapunov stability concept and finite-time convergence idea, the recommended control method guarantees that the states of the quad-rotor UAV are converged to the origin with a finite-time convergence rate. Furthermore, an adaptive-tuning scheme is advised to guesstimate the unknown parameters of the quad-rotor UAV at any moment. Finally, simulation results are presented to exhibit the helpfulness of the offered technique compared to the previous methods.
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Affiliation(s)
- Omid Mofid
- Electrical Engineering Department, University of Zanjan, Zanjan, Iran
| | - Saleh Mobayen
- Electrical Engineering Department, University of Zanjan, Zanjan, Iran.
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