Full-State-Constrained Adaptive Control for a Class of UAVs Suffering from Coupled Uncertainties Using the HOBLF

Nowadays, the practical tasks of UAVs are becoming more and more complicated and diversified. In the practical flight process, the large-scale changes of the flight environment, the modeling errors, and the external disturbances may induce the instability of the UAV flight system. Meanwhile, the constraints of the UAV attitudes also have to be guaranteed during the flight process. However, most existing control methods still have limitations in handling the constraints and the multisource disturbances simultaneously. To address this problem, in this paper, we focus on the actual output tracking control for the UAV systems with full-state constraints and multisource disturbances. Firstly, a high-order tan-type barrier Lyapunov function (HOBLF) has been constructed for the UAV to maintain the full-state constraints. Secondly, by combining the adaptive backstepping technique and the fuzzy logic systems, the modeling errors and the unknown nonlinearities of the UAV attitude control system can be handled. Moreover, by properly constructing several adaptive laws, the time-varying disturbances existing in the UAV attitude control system can be suppressed. Finally, the full-state-constrained antidisturbance controller is formed, ensuring that the tracking error approaches arbitrarily to small neighborhood and does not violate the given constraints. The simulation results illustrate the feasibility and the advantages of the proposed method.

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Finite-time tracking control for nonstrict-feedback nonlinear systems with unknown time delays and input saturation

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211058302 ◽

2021 ◽

pp. 014233122110583

Author(s):

Xiaojing Qi ◽

Wenhui Liu

Keyword(s):

Finite Time ◽

State Constraints ◽

Tracking Error ◽

Input Saturation ◽

Small Neighborhood ◽

Delay Systems ◽

Fuzzy Logic Systems ◽

Time Control ◽

Full State ◽

Full State Constraints

In this article, the problem of adaptive finite-time control is studied for a category of nonstrict-feedback nonlinear time-delay systems with input saturation and full state constraints. The fuzzy logic systems are applied to model the unknown nonlinear terms in the systems. Then, a novel tan-type barrier Lyapunov function is adopted to overcome the problem of full state constraints. By utilizing the finite-time control theory and the backstepping technique, a finite-time fuzzy adaptive controller is designed. The controller can guarantee that the tracking error is adjusted around zero with a small neighborhood in a finite time and all the signals in the closed-loop system are bounded. Finally, two simulation examples are included to verify the validity and feasibility of the control scheme.

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Prescribed Performance Control of Marine Surface Vessel Trajectory Tracking in Finite-Time with Full-State Constraints and Input Saturation

Journal of Marine Science and Engineering ◽

10.3390/jmse9080866 ◽

2021 ◽

Vol 9 (8) ◽

pp. 866

Author(s):

Xiyun Jiang ◽

Yuanhui Wang

Keyword(s):

Finite Time ◽

State Constraints ◽

Tracking Error ◽

Input Saturation ◽

Prescribed Performance ◽

Full State ◽

Marine Surface ◽

Surface Vessel ◽

Full State Constraints ◽

Function Matrix

This manuscript mainly solves a fully actuated marine surface vessel prescribed performance trajectory tracking control problem with full-state constraints and input saturation. The entire control design process is based on a backstepping technique. The prescribed performance control is introduced to embody the analytical relationship between the transient performance and steady-state performance of the system and the parameters. Meanwhile, a new finite time performance function is introduced to ensure that the performance of the system tracking error is constrained within the preset constraints in finite time, and the full-state constraints problem of the system can be solved simultaneously in the entire control design, at the same time without introducing additional theory and parameters. To solve the non-smooth input saturation function matrix is not differentiable, the smooth function matrix is introduced to replace the non-smooth characteristics. Combining the Moore-Penrose generalized inverse matrix to design the virtual control law, the dynamic surface control is introduced to avoid the complicated virtual control derivation process, and finally the actual control law is designed using the properties of Nussbaum function. In addition, in view of the uncertainties in the system, a fractional disturbance observer is designed to estimate it. With the proposed control, the full-state will never be violated constraints, and the system tracking error satisfies transient and steady-state performance. Compared with other methods, the simulation results show the effectiveness and advantages of the proposed method.

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Output tracking control of strict-feedback non-linear systems under asymmetrically bilateral and time-varying full-state constraints

IET Control Theory and Applications ◽

10.1049/iet-cta.2019.0399 ◽

2020 ◽

Vol 14 (1) ◽

pp. 156-164

Author(s):

Yawen Sun ◽

Shigen Gao ◽

Lingbin Ning ◽

Hairong Dong ◽

Bin Ning

Keyword(s):

Linear Systems ◽

Tracking Control ◽

State Constraints ◽

Time Varying ◽

Output Tracking ◽

Output Tracking Control ◽

Full State ◽

Non Linear ◽

Non Linear Systems ◽

Full State Constraints

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Command filtered adaptive neural tracking control of uncertain nonlinear time-delay systems with asymmetric time-varying full state constraints and actuator saturation

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820975265 ◽

2020 ◽

pp. 095965182097526

Author(s):

Yuxiang Wu ◽

Tian Xu ◽

Haoran Fang

Keyword(s):

Time Delay ◽

Tracking Control ◽

State Constraints ◽

Actuator Saturation ◽

Small Neighborhood ◽

Delay Systems ◽

Time Varying ◽

Time Delay Systems ◽

Full State ◽

Full State Constraints

This article investigates the command filtered adaptive neural tracking control for uncertain nonlinear time-delay systems subject to asymmetric time-varying full state constraints and actuator saturation. To stabilize such a class of systems, the radial basis function neural networks and the backstepping technique are used to structure an adaptive controller. The command filter is utilized to overcome the complexity explosion problem in backstepping. By employing the Lyapunov–Krasovskii functionals, the effect of time-delay is eliminated. The asymmetric time-varying barrier Lyapunov functions are designed to ensure full state constraint satisfaction. Moreover, the hyperbolic tangent function and an instrumental variable are introduced to deal with actuator saturation. All signals in the closed-loop system are proved to be bounded and the tracking error converges to a small neighborhood of the origin. Finally, two examples are provided to illustrate the effectiveness of the proposed method.

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Dual-Motor Synchronization Control Design Based on Adaptive Neural Networks Considering Full-State Constraints and Partial Asymmetric Dead-Zone

Sensors ◽

10.3390/s21134261 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4261

Author(s):

Chunhong Jin ◽

Mingjie Cai ◽

Zhihao Xu

Keyword(s):

Neural Networks ◽

State Constraints ◽

Controller Design ◽

Dead Zone ◽

Tracking Error ◽

Servo System ◽

Zone Model ◽

Control Scheme ◽

Full State ◽

Full State Constraints

This paper proposes a command filtering backstepping (CFB) scheme with full-state constraints by leading into time-varying barrier Lyapunov functions (T-BLFs) for a dual-motor servo system with partial asymmetric dead-zone. Firstly, for the convenience of the controller design, the conventional partial asymmetric dead-zone model was replaced with a new smooth differentiable model owing to its non-smoothness. Secondly, neural networks (NNs) were utilized to approximate the nonlinearity that exists in the dead-zone model, improving the control performance. In addition, CFB was utilized to deal with the inherent computational explosion problem of the traditional backstepping method, and an error compensation mechanism was introduced to further reduce the filtering errors. Then, by applying the T-BLF to the CFB process, the states of the system never violated the prescribed constraints, and all signals in the dual-motor servo system were bounded. The tracking error and synchronization error could converge to a small desired neighborhood of the origin. In the end, the effectiveness of the proposed control scheme was verified through simulations.

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Neural network-based stochastic adaptive attitude control for generic hypersonic vehicles with full state constraints

Neurocomputing ◽

10.1016/j.neucom.2019.04.014 ◽

2019 ◽

Vol 351 ◽

pp. 228-239 ◽

Cited By ~ 1

Author(s):

Xiaofeng Zhang ◽

Kang Chen ◽

Wenxing Fu ◽

Hanqiao Huang

Keyword(s):

Neural Network ◽

Attitude Control ◽

State Constraints ◽

Hypersonic Vehicles ◽

Full State ◽

Full State Constraints

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Tracking control for strict-feedback nonlinear systems with time-varying full state constraints

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217737596 ◽

2018 ◽

Vol 40 (14) ◽

pp. 3964-3977 ◽

Cited By ~ 8

Author(s):

Chunxiao Wang ◽

Yuqiang Wu ◽

Zhongcai Zhang

Keyword(s):

Nonlinear Systems ◽

Tracking Control ◽

State Constraints ◽

Tracking Error ◽

Input Saturation ◽

Constrained Systems ◽

Time Varying ◽

Full State ◽

Full State Constraints ◽

Strict Feedback

This paper focuses on the tracking control problem for strict-feedback nonlinear systems subject to asymmetric time-varying full state constraints. Time-varying asymmetric barrier Lyapunov functions are employed to ensure time-varying constraint satisfaction. By allowing the barriers to vary with the desired trajectory in time, the initial condition requirements are relaxed. High-order coupling terms caused by backstepping are cancelled through a novel variable substitution for the first time. Besides the normal case, where the full knowledge of the system is available, we also handle scenarios of parametric uncertainties. Asymptotic tracking is achieved without violation of any constraints, and all signals in the closed-loop system are ultimately bounded. State-constrained systems with input saturation and bounded disturbances are also considered; the tracking error converges to a bounded set around zero. The performance of the asymmetric-barrier-Lyapunov-function-based control is illustrated through a numerical example.

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