Decentralized control of uncertain nonlinear stochastic systems based on DSC

In this paper, the decentralized stabilization control approach based on the dynamic surface control (DSC) is proposed for a class of large-scale interconnected stochastic nonlinear systems. The proposed approach combined the existing dynamic surface control (DSC) with back-stepping technique. This approach can overcome the problem of “explosion of complexity” inherent in the back-stepping method. Thus, the proposed control approach is simpler than the traditional back-stepping control method for the large-scale interconnected stochastic nonlinear systems. The stability analysis shows that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Finally, an example is provided to illustrate the effectiveness of the proposed control system.     

System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty

Trajectory tracking of a mobile manipulator is a challenging research because of its complex nonlinearity and dynamics. This paper presents an adaptive control strategy for trajectory tracking of a mobile manipulator system that consists of a wheeled platform and a modular manipulator. When a robot system moves in the presence of sliding, it is difficult to accurately track its trajectory by applying the backstepping approach, even if we employ a non-ideal kinematic model. To address this problem, we propose using a combination of adaptive fuzzy control and backstepping approach based on a dynamic model. The proposed control scheme considers the dynamic interaction between the platform and manipulator. To accurately track the trajectory, we propose a fuzzy compensator in order to compensate for modeling uncertainties such as friction and external disturbances. Moreover, to reduce approximation errors and ensure system stability, we include a robust term to the adaptive control law. Simulation results obtained by comparing several cases reveal the presence of the dynamic interaction and confirm the robustness of the designed controller. Finally, we demonstrate the effectiveness and merits of the proposed control strategy to counteract the modeling uncertainties and accurately track the trajectory.     

Adaptive dynamic surface control for cooperative path following of marine surface vehicles with input saturation

This paper considers the cooperative path following problem of multiple marine surface vehicles subject to input saturation, unknown dynamical uncertainty and unstructured ocean disturbances, and partial knowledge of the reference velocity. The control design is categorized into two envelopes. Path following for each vehicle amounts to reducing an appropriately defined geometric error. Vehicles coordination is achieved by exchanging the path variables, as determined by the communications topology adopted. The control design is developed with the aid of the neural network-based dynamic surface control (DSC) technique, an auxiliary design, and a distributed estimator. The key features of the developed controllers are as follows. First, the neural network-based adaptive DSC technique allows for handling the unknown dynamical uncertainty and ocean disturbances without the need for explicit knowledge of the model, and at the same time simplify the cooperative path following controllers by introducing the first-order filters. Second, input saturations are incorporated into the cooperative path following design, and the stability of the modified control solution is verified. Third, the amount of communications is reduced effectively due to the distributed speed estimator, which means the global knowledge of the reference speed is relaxed. Under the proposed controllers, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results validate the performance and robustness improvement of the proposed strategy.     

Output feedback stabilization based on dynamic surface control for a class of uncertain stochastic nonlinear systems

In this paper, the dynamic surface control (DSC) algorithm is proposed for a class of stochastic nonlinear systems with nonminimum phase and the standard output-feedback form. The proposed algorithm is a stochastic vision by combining the traditional back-stepping together with the DSC technique, which can overcome the problem of ‘explosion of complexity’ in the back-stepping designing procedure for the stochastic nonlinear systems. Thus, it can reduce the computation complexity and is easy to be used in the actual implementation. It is shown that all the signals of the resulting closed-loop system are uniformly ultimately bounded.     

基于反演高阶滑模的飞行器最优末制导律

针对传统最优末制导律鲁棒性能较弱,且对参数摄动及外扰敏感的不足,而滑模控制对扰动具有较强鲁棒性的优点,提出一种新的基于反演准连续高阶滑模的最优末制导律,其中反演控制能够有效保证系统全局稳定性,而准连续高阶滑模控制则用于消除扰动影响。为了去除抖振效果,引入自适应超螺旋算法在线更新控制参数以消除符号函数导致的高频抖振影响。仿真结果表明:飞行器在该末制导律导引下,弹目视线角速率快速收敛,从而保证飞行器有很高的命中精度;鲁棒性较强;能够较好的满足约束条件要求。     

可重复使用运载器大姿态机动自抗扰控制

针对可重复使用运载器大俯仰角或偏航角转弯机动而产生的姿态角奇异的控制问题,提出了基于四元数的自抗扰控制方法。通过两级跟踪微分器从期望四元数中逐步得到三通道解耦的角加速度信号,然后利用扩张状态观测器观测模型中的不确定项,最终采用动态逆得到解耦的三通道发动机等效摆角或RCS(Reaction Control System)等控制信号,并设计了数字滤波器对弹性振动与液体晃动信号进行滤波处理。考虑到系统模型具有非线性、不确定性、11阶弹性振动、一阶液体晃动、风干扰和气动偏差等多种外部扰动条件,对可重复使用运载器从主动段到再入飞行段进行了非线性六自由度仿真分析。仿真结果表明,基于四元数的自抗扰姿态控制器具有快速、平稳、超调量小、抗干扰能力强、无系统抖振且控制参数较少的特点。     

Adaptive fuzzy formation control for a swarm of nonholonomic differentially driven vehicles

In this paper, an adaptive fuzzy robust H ^∞ controller is proposed for formation control of a swarm of differential driven vehicles with nonholonomic dynamic models. Artificial potential functions are used to design the formation control input for kinematic model of the robots and matrix manipulations are used to transform the nonholonomic model of each differentially driven vehicle into equivalent holonomic one. The main advantage of the proposed controller is the robustness to input nonlinearity, external disturbances, model uncertainties, and measurement noises, in a formation control of a nonholonomic robotic swarm. Moreover, robust stability proof is given using Lyapunov functions. Finally, simulation results are demonstrated for a swarm formation problem of a group of six unicycles, illustrating the effective attenuation of approximation error and external disturbances, even in the case of robot failure.     

Adaptive robust maneuvering control for nonlinear systems via dynamic surface technique

In this paper, the adaptive robust controller based on dynamic surface technique is investigated for the maneuvering problem of uncertain nonlinear systems with external disturbances. As preliminary, the definition of semi-globally uniformly practically asymptotically stable and its Lyapunov criterion are presented. The static part of controller with smooth robust compensator and adaptive law is designed to achieve the geometric task of maneuverability, and the dynamic control is proposed to reach the speed task by filtered-gradient update law. Moreover, utilizing first-order filter, the problem of “dimensional explosion” is avoided in controller design. Simulation is conducted for three-mecanum-wheeled mobile robot actuated by DC motors to illustrate the effectiveness of the control strategy.

     

Component Mode Synthesis Using Nonlinear Normal Modes

This paper describes a methodology for developing reduced-order dynamic models of structural systems that are composed of an assembly of nonlinear component structures. The approach is a nonlinear extension of the fixed-interface component mode synthesis (CMS) technique developed for linear structures by Hurty and modified by Craig and Bampton. Specifically, the case of nonlinear substructures is handled by using fixed-interface nonlinear normal modes (NNMs). These normal modes are constructed for the various substructures using an invariant manifold approach, and are then coupled through the traditional linear constraint modes (i.e., the static deformation shapes produced by unit interface displacements). A class of systems is used to demonstrate the concept and show the effectiveness of the proposed procedure. Simulation results show that the reduced-order model (ROM) obtained from the proposed procedure outperforms the ROM obtained from the classical fixed-interface linear CMS approach as applied to a nonlinear structure. The proposed method is readily applicable to large-scale nonlinear structural systems that are based on finite-element models.     

Numerical prediction of droplet dynamics in turbulent flow,using the level set method

In the present article, the droplet dynamics in turbulent flow is numerically predicted. The modelling is based on an interfacial marker-level set (IMLS) method, coupled with the Reynolds-averaged Navier–Stokes (RANS) equations to predict the dynamics of turbulent two-phase flow. The governing equations for time-dependent, two-dimensional and incompressible two-phase flow are described in both phases and solved separately using a control volume approach on structured cell-centred collocated grids. The topological changes of the interface are predicted by applying the level set approach. The kinematic and dynamic conditions on the interface separating the two phases are satisfied. The numerical method proposed is validated against a well-known computational fluid dynamics problem. Further, the deformation and breakup of a single droplet either suddenly moved in air or exposed to turbulent stream are numerically investigated. In general, the developed numerical method demonstrates remarkable capability in predicting the characteristics of complex turbulent two-phase flows.     

Neural network-based adaptive output feedback formation control for multi-agent systems

This paper investigates the problem of output feedback formation tracking control for second-order multi-agent systems under an undirected connected graph and in the presence of dynamic uncertainties and bounded external disturbances. Two state tracking error measures (i.e., absolute and relative state tracking errors) are considered for each individual agent in the formation, and linear reduced-order observers are constructed based on the lumped state tracking errors which include absolute and relative state tracking errors. Chebyshev neural networks are used to approximate unknown nonlinear function in the agent dynamics on-line, and the implementation of the basis functions of Chebyshev neural networks depends only on the desired signals. The smooth projection algorithm is applied to guarantee that the estimated parameters remain in some known bounded sets. Numerical simulations are presented to illustrate the performance of the proposed controller.     

Dynamic analysis of wind turbine tower structures in complex ocean environment

Studying and analyzing the dynamic behavior of offshore wind turbines are of great importance to ensure the safety and improve the efficiency of such expensive equipments. In this work, a tapered beam model is proposed to investigate the dynamic response of an offshore wind turbine tower on the monopile foundation assembled with rotating blades in the complex ocean environment. Several environment factors like wind,wave, current, and soil resistance are taken into account. The proposed model is ...     

Trajectory stabilization control for aerial recovery of cable-drogue-UAV assembly

Unmanned aerial vehicles (UAVs) aerial recovery denotes the technology that UAVs are recovered in the air by the transport aircraft for reuse. During the recovery process, the multiple wind perturbations and fast-changing UAV’s engine shutdown will induce oscillations in the cable-drogue-UAV assembly (CDUA) with strong nonlinearities and tight coupling, which affects the safety and speed of the UAV aerial recovery. Aiming at this problem, this paper proposes a non-constraining force direction (NCFD)-based CDUA anti-disturbance trajectory control method for the first time. First, by transforming the CDUA trajectory control to the NCFD control, the coupling and nonlinear effects in the CDUA can be reduced, and the fast-changing disturbances caused by the engine shutdown can be compensated. Then, feed forward control is designed based on the relationship between the NCFD and cable shape, which is established based on the cable dynamics, to improve the response speed. Furthermore, a fixed-time anti-disturbance controller (FTADC) is designed for the flow angle of drogue-UAV assembly (DUA) given by the NCFD controller and compensates for the effects of wind and parameter perturbations. Finally, the stability of the proposed method is analyzed, and the effectiveness is demonstrated by abundant simulations.

     

Leaderless and leader-follower cooperative control of multiple marine surface vehicles with unknown dynamics

Unlike the tracking control of a single marine vehicle, this paper considers the leaderless and leader-follower cooperative control of multiple marine surface vehicles subject to unknown nonlinear dynamics and ocean disturbances, all seeking to maintain a relative formation. For both cases, a cooperative control design approach is proposed by integrating neural networks, a backstepping technique, and graph theory. It is shown that with the developed cooperative controllers, formation behavior among vehicles can be achieved for any undirected connected communication graphs without requiring the accurate model of each vehicle. Based on Lyapunov stability analysis, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded, and cooperative tracking errors converge to a small neighborhood of the origin. Simulation results are given to show the efficacy of the proposed methods.     

Aeroelastic analysis of a rotating wind turbine blade using a geometrically exact formulation

In this paper, an aeroelastic analysis of a rotating wind turbine blade is performed by considering the effects of geometrical nonlinearities associated with large deflection of the blade produced during wind turbine operation. This source of nonlinearity has become more important in the dynamic analysis of flexible blades used in more recent multi-megawatt wind turbines. The structural modeling, involving the coupled edgewise, flapwise and torsional DOFs, has been performed by using a nonlinear geometrically exact beam formulation. The aerodynamic model is presented based on the strip theory, by applying the principles of quasi-steady and unsteady airfoil aerodynamics. Compared to the conventional steady aerodynamic model, the presented model offers a more realistic consideration of fluid–structure interactions. The resulting governing equation, expanded up to the third-order terms, is analyzed by using the reduced-order model (ROM). The ROM is developed by employing the coupled mode shapes of a cantilever blade under free loading condition. The specifications of the 5MW-NREL wind turbine are used in the simulation study. After verifying the ROM results by comparing them with those of the full FEM model, the model is used in additional static, modal and transient dynamics analyses. The results indicate the important effect of geometrical nonlinearity, especially for larger structural deformations. Moreover, nonlinear analyses reveal the important effects of torsion induced by lateral deformations. It is also found that the governing equation is more efficient, and sufficiently accurate, when it is developed by using the second-order kinetic terms, third-order potential terms and the second-order aerodynamic terms together with third-order damping. Finally, the effects of nonlinearities on the flutter characteristics of wind turbine blades are evaluated through frequency and dynamic analyses.     

Shakedown reduced kinematic formulation,separated collapse modes,and numerical implementation

Our shakedown reduced kinematic formulation is developed to solve some typical plane stress problems, using finite element method. Whenever the comparisons are available, our results agree with the available ones in the literature. The advantage of our approach is its simplicity, computational effectiveness, and the separation of collapse modes for possible different treatments. Second-order cone programming developed for kinematic plastic limit analysis is effectively implemented to study the incremental plasticity collapse mode. The approach is ready to be used to solve general shakedown problems, including those for elastic–plastic kinematic hardening materials and under dynamic loading.     

空间机器人捕获航天器后基于无源性理论的鲁棒镇定控制

研究了漂浮基空间机器人捕获非合作航天器过程对系统产生的冲击效应及其后联合体系统镇定运动的控制问题。为此,利用拉格朗日方法及牛顿-欧拉法分别获得了捕获前空间机器人及目标航天器的动力学模型;结合动量守恒定律、系统运动几何关系及力的传递规律,分析了捕获过程相互碰撞所产生的冲击效应,建立了捕获完成后两者联合体的系统动力学模型。在此基础上,针对同时存在不确定参数及外部扰动的联合体系统,设计了基于无源性理论的镇定运动神经网络H_∞鲁棒控制算法。本文提出的基于无源性理论设计的鲁棒控制算法具有良好的动态特性及较强的鲁棒性,可快速完成系统的镇定控制,实现轨迹的精确跟踪。系统数值模拟仿真验证了本文控制方案的正确性。     

Distributed coordinated tracking of multiple autonomous underwater vehicles

This paper considers the distributed coordinated tracking problem of multiple autonomous underwater vehicles with a time-varying reference trajectory. Each vehicle is subject to model uncertainty and time-varying ocean disturbances. A novel predictor-based neural dynamic surface control design approach is proposed to develop the node controllers, under which synchronization between vehicles can be reached on condition that the augmented graph induced by the vehicles and the reference trajectory contains a spanning tree. The prediction errors are used to update the neural adaptive laws, which enable fast identifying the vehicle dynamics without excessive knowledge of their dynamical models. Further, this result is extended to the output-feedback case where only position-yaw information can be measured. A local predictor, based on its own position-yaw information, is constructed, not only to recover the unmeasured velocity information, but also to identify the unknown dynamics for each vehicle. A linear matrix inequality-based analysis is performed for the stability of the predictor. Then, distributed output-feedback tracking controllers are developed to achieve synchronization between vehicles in the presence of unknown dynamics and unmeasured velocities. For both cases, the stability properties of the closed-loop network are established via Lyapunov analysis. Simulation results demonstrate the effectiveness of the proposed methods.     

Data-based hybrid reduced order modeling for vortex-induced nonlinear fluid–structure interaction at low Reynolds numbers

Fluid–structure interaction (FSI) phenomena are of significant importance in several engineering fields. Recently developed active flow control devices regulate the FSI in order to control the dynamic response of the structure that is involved. As a first step to use active control, computationally efficient reduced-order models are required. The reduced-order models must be able to predict the nonlinear structural dynamic response given an incoming flow condition. This paper presents a computationally efficient method for the construction of a hybrid reduced-order model for FSI problems based on data obtained through high-fidelity numerical simulations. The model splits the force and the structural dynamic response into two separate blocks and uses model reduction techniques to account for the flow field information. The current model is tested on a vibrating rigid cylinder submerged in a flow at low Reynolds number regime.     

A self-tuning guidance and control system for marine surface vessels

An integrated guidance and control system has been developed to enable underactuated marine surface vessels to operate autonomously and yield robust tracking performance in spite of significant external disturbances and modeling imprecision. A nonlinear ship model, accounting for all six degrees-of-freedom of the ship, has been used as a test bed to assess the performance of the proposed scheme. The controller combines the advantages of the variable structure systems (VSS) theory with the self-tuning fuzzy logic scheme. It does not require an accurate dynamic model of the ship or the construction of a rule-based expert system. Its asymptotic stability is ensured by knowing the upper bounds on modeling imprecision and external disturbances and by forcing the tuning parameters to satisfy the sliding conditions. The guidance system is based on the concepts of the variable radius line-of-sight (LOS) and the acceptance circle around the waypoints. The current system varies the LOS radius exponentially with the cross track error in order to achieve a fast convergence rate of the ship to its desired trajectory. The simulation results demonstrate the robust tracking characteristic of the integrated guidance and control system in spite of significant modeling uncertainties and environmental disturbances.