小型无人直升机姿态非线性鲁棒控制设计

本文针对小型无人直升机的姿态控制问题,通过系统参数辨识,获得了较为准确的无人直升机姿态动力学模型.并根据无人直升机的动态特性,设计了基于神经网络前馈与滑模控制的非线性鲁棒姿态控制律,该控制律对直升机模型的先验知识要求较低.利用基于Lyapunov的分析方法证明,设计的控制律能够实现对无人直升机姿态角的半全局指数收敛镇定控制,并能确保闭环系统的稳定性.基于姿态飞行控制实验平台的实时飞行控制实验结果表明,提出的控制设计取得了很好的姿态控制效果,并对系统不确定性和外界风扰动具有较好的鲁棒性.     

基于神经网络的小型无人直升机非线性鲁棒控制设计

针对小型无人直升机的姿态控制问题,为补偿系统参数不确定性和外界扰动的影响,设计一种连续的非线性鲁棒控制器.首先,利用神经网络在线估计系统不确定性,采用基于误差符号函数积分的鲁棒控制算法抑制外界扰动,同时补偿神经网络估计误差; 然后,利用基于Lyapunov函数的分析方法,证明所设计控制器的闭环稳定性,确保无人直升机姿态误差的半全局渐近收敛;最后,在无人直升机飞行实验平台上,进行无人机抗风扰控制实验.实验结果表明,所提出的控制方法具有良好的控制效果,对系统不确定性和外界扰动具有良好的鲁棒性.     

Neural network-based asymptotic tracking control of unknown nonlinear systems with continuous control command

ABSTRACT

This paper proposes a robust tracking controller for a class of nonlinear second-order systems with time-varying uncertainties. The controller is mainly based on the robust integral of the sign of the error (RISE) control approach to achieve an asymptotic stability result with a continuous control command in the presence of additive uncertainties. An adaptive feedforward neural network control term is blended with a new RISE controller to improve the system's transient performance. The proposed RISE controller is a modified version of the existing saturated RISE controller such that only sign of the derivative of the output is needed. The stability of the closed-loop system is well studied, where a local asymptotic stability is proven. The controller performance is validated through simulations on a two-degree-of-freedom lower limb robotic exoskeleton.     

基于神经网络前馈的无人直升机非线性鲁棒控制设计

针对小型无人直升机的控制问题,设计了一种基于神经网络前馈的非线性鲁棒控制算法.算法主要由两部分组成:基于三层神经网络的前馈,用以补偿无人直升机姿态动力学模型中的不确定项;基于符号函数积分的鲁棒控制,用以补偿未知外界扰动;基于Lyapunov分析方法证明了控制器可实现姿态角的半全局渐近跟踪.在三自由度实验平台上对所设计的控制算法进行了实验验证,结果表明:提出的设计取得了较好的姿态控制效果,并对外界未知风扰具有较好的鲁棒性.     

Neural network robust control of a 3-DOF hydraulic manipulator with asymptotic tracking

Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high-performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three-axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high-gain feedback issue in RISE feedback control will be avoided. The proposed RISE-based neural network robust controller theoretically guarantees an excellent semi-global asymptotic stability. Comparative simulation is performed on a 3-DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller.     

A new smooth robust control design for uncertain nonlinear systems with non-vanishing disturbances

In this paper, we consider the control problem for a general class of nonlinear system subjected to uncertain dynamics and non-varnishing disturbances. A smooth nonlinear control algorithm is presented to tackle these uncertainties and disturbances. The proposed control design employs the integral of a nonlinear sigmoid function to compensate the uncertain dynamics, and achieve a uniformly semi-global practical asymptotic stable tracking control of the system outputs. A novel Lyapunov-based stability analysis is employed to prove the convergence of the tracking errors and the stability of the closed-loop system. Numerical simulation results on a two-link robot manipulator are presented to illustrate the performance of the proposed control algorithm comparing with the layer-boundary sliding mode controller and the robust of integration of sign of error control design. Furthermore, real-time experiment results for the attitude control of a quadrotor helicopter are also included to confirm the effectiveness of the proposed algorithm.     

Virtual-Point-Based asymptotic tracking control of 4WS vehicles

This paper studies the lateral and longitudinal path tracking control of four-wheel steering vehicles. By the introduction of virtual points, a robust and adaptive path tracking control strategy is proposed to simultaneously counteract modeling uncertainties, unexpected disturbances, and coupling effects. An adaptive model-based feedforward adaptive term and the robust integral of the sign of the error (RISE) feedback term can be used to yield an asymptotic tracking result, which improve the tracking performance and reduce the control effort. The stability of closed-loop system is analyzed using a Lyapunov-based method. Simulation results are provided to demonstrate the performance of the proposed controller under different driving conditions.     

无人直升机的姿态增强学习控制设计与验证

针对小型无人直升机的姿态控制问题,考虑到现有基于模型的控制方法对直升机动力学模型的先验信息依赖较大,以及未建模动态系统的影响等问题,设计了一种基于增强学习(RL)的飞行控制算法.仅利用直升机的在线飞行数据,补偿了未建模不确定性的影响.同时为了抑制外界扰动,提高系统的鲁棒性,设计了一种基于误差符号函数积分的鲁棒(RISE)控制算法.将两种算法结合,并利用基于Lyapunov分析的方法,证明了无人机姿态控制误差的半全局渐近收敛.最后在无人直升机飞行控制实验平台上,进行了姿态控制的实时实验验证.实验结果表明,本文提出的控制方法具有良好的控制效果,对系统不确定性和外界风扰具有良好鲁棒性.     

Global robust tracking control of non-affine nonlinear systems with application to yaw control of UAV helicopter

In this paper, a novel robust nonlinear tracking control scheme is proposed for the yaw channel of an unmanned-aerial-vehicle helicopter that is non-affine in the control input. By a novel dynamic modeling technique, the non-affine nonlinear systems are approximated to facilitate the desired control design. In the controller design procedure, the terminal sliding model control method is introduced to deal with the unknown uncertainties/disturbances. Moreover, filter and disturbance estimator are combined to further reduce the chattering. A systematic procedure is developed and related theoretical and practical issues are discussed. The proposed nonlinear tracking control scheme can guarantee the asymptotic output tracking of the closed-loop control systems in spite of unknown uncertainties/disturbances. Finally, the simulation results on the dynamic model of a real helicopter-on-arm are provided to demonstrate the effectiveness of the proposed new control techniques.     

基于强化学习的小型无人直升机有限时间收敛控制设计

针对小型无人直升机精确动力学模型难以获取以及姿态控制易受未知外界风扰影响的问题,设计一种基于强化学习(reinforcement learning, RL)与super twisting相结合的非线性控制算法.利用直升机在线飞行数据,训练执行者-评价者(actor-critic, AC)网络以逼近系统建模不确定部分.为了抑制未知外界风扰,提高系统鲁棒性,同时补偿AC网络逼近误差,设计基于super twisting的鲁棒控制算法.进而,利用Lyapunov稳定性分析方法证明无人直升机姿态误差能在有限时间内收敛到零.最后对所提出的算法进行实验验证,实验结果表明,所提出算法具有良好的控制效果,对系统不确定性和外界扰动具有良好的鲁棒性.     

Hierarchical inversion‐based output tracking control for uncertain autonomous underwater vehicles using extended Kalman filter

In this study, a hierarchical inversion‐based output tracking controller (HIOTC) is developed for an autonomous underwater vehicle (AUV) subject to random uncertainties (e.g., current disturbances, unmodeled dynamics, and parameter variations) and noises (e.g., process and measurement noises). The proposed HIOTC respectively utilizes a combination of feedforward and feedback controls in a hierarchical structure based on the kinematic and dynamic models of the system. Moreover, to obtain uncontaminated or unavailable states for implementing the proposed control law, the extended Kalman filter (EKF) is employed to estimate the system states. Then, the position outputs, orientation, and velocity of the AUV are reached with guaranteed asymptotic stability. The robustness of the proposed HIOTC is verified through injection of random uncertainties into the system model. The closed‐loop stability of the proposed individual subsystems is respectively guaranteed to have uniformly ultimately bounded (UUB) performance based on the Lyapunov stability criteria. In addition, the asymptotic tracking of the overall system is demonstrated using Barbalat's lemma. Finally, the feasibility and effectiveness of the proposed control scheme are evaluated through computer simulations and it is shown that the overall system achieves good asymptotic tracking performance.     

Trajectory linearization based output tracking control of an unmanned tandem helicopter with variance constraints

An output tracking control problem for an unmanned tandem rotor helicopter with variance constraints is investigated in this paper. A modified Trajectory Linearization Control (TLC) is proposed to stabilize a nonlinear continuous-time flight dynamics system of the tandem helicopter. The tracking controller structure of TLC is designed by using two-time-scale nonlinear dynamic inversion. The base control law of the translational and attitude loops is designed in a pseudo-inversion feedforward controller to deal with nonlinear features of the plant and a proportional integral controller to stabilize the linear slowly time-variant error system resulted from the nonlinear flight system. Furthermore, a feasible TLC strategy is designed to meet a performance index set including steady trajectory tracking error variance and desired Parallel D-spectrum (PD-) eigenvalues to achieve good flight quality. The Variance-constrained Trajectory Linearization Control (VCTLC) is designed to realize the desired steady tracking precision and agile capability. Flight simulation results show the VCTLC method is feasible and effective in attitude and altitude tracking.     

基于NDO的ROV变深自适应终端滑模控制器设计

针对ROV的深度控制问题, 提出基于非线性干扰观测器的自适应终端滑模控制方法. 详细叙述了控制器的设计过程, 并利用Lyapunov 稳定性判据, 验证了存在模型参数不确定性和外干扰时, 系统的全局渐近稳定性和跟踪误差的收敛性. 仿真实验表明, 所提出的控制器不仅能够很好地估计并克服外干扰和模型不确定性等因素, 具有很好的鲁棒性能, 而且还可以实现在任意规定时刻变深运动的快速收敛.

     

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

A boiler‐turbine unit is a primary module for coal‐fired power plants, and an effective automatic control system is needed for the boiler‐turbine unit to track the load changes with the drum water level kept within an acceptable range. The aim of this paper is to develop a nonlinear tracking controller for the Bell‐Åström boiler‐turbine unit. A Takagi‐Sugeno fuzzy control system is introduced for the nonlinear modeling of the Bell‐Åström boiler‐turbine unit. Based on the Takagi‐Sugeno fuzzy models, a nonlinear tracking controller is developed, and the proposed control law is comprised of a state‐feedforward term and a state‐feedback term. The stability of the closed‐loop control system is analyzed on the basis of Lyapunov stability theory via the linear matrix inequality approach and Schur complement. Moreover, model uncertainties are also considered, and it is proved that with the proposed control law the tracking error converges to zero. To assess the performance of the proposed nonlinear state‐feedback state‐feedforward control strategy, a nonlinear model predictive control strategy and a linear strategy are presented as comparisons. The effectiveness and the advantages of the proposed nonlinear state‐feedback state‐feedforward control strategy are demonstrated by simulations.     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

干扰下的无人直升机自适应反步法鲁棒跟踪控制

针对无人直升机干扰下的鲁棒轨迹跟踪问题,设计了一种自适应反步控制方法.鉴于作用在直升机上的干扰是产生跟踪误差的主要原因,该方法的主要思想是寻求一种方法来补偿这种干扰.首先,将未建模动态如外部阵风干扰、配平误差、机身、垂尾、平尾以及其他可忽略的动态产生的力和力矩看成一种组合干扰,从而建立了一个方便反步法控制器设计的简化模型.当设计好反步法控制器后,设计了一个非线性自适应律来估计这种组合干扰,并通过将干扰估计值整合到反步控制器中,使得闭环跟踪系统的鲁棒稳定性得到了保证,即基于李雅普诺夫稳定性理论证明了所设的控制器对于干扰主动阻隔,特别是低频干扰的主动阻隔是有效的.最后,两个仿真研究验证了该方法是优于常规反步法和积分反步法的.     

Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems

This work presents an adaptive fuzzy sliding mode controller (AFSMC) that combines a robust proportional integral control law for use in designing single-input single-output (SISO) nonlinear systems with uncertainties and external disturbances. The fuzzy logic system is used to approximate the unknown system function and the AFSMC algorithm is designed by used of sliding mode control techniques. Based on the Lyapunov theory, the proportional integral control law is designed to eliminate the chattering action of the control signal. The simplicity of the proposed scheme facilitates its implementation and the overall control scheme guarantees the global asymptotic stability in the Lyapunov sense if all the signals involved are uniformly bounded. Simulation studies have shown that the proposed controller shows superior tracking performance.     

An asymptotically stable robust controller formulation for a class of MIMO nonlinear systems with uncertain dynamics

In this work, we present a novel continuous robust controller for a class of multi-input/multi-output nonlinear systems that contains unstructured uncertainties in their drift vectors and input matrices. The proposed controller compensates uncertainties in the system dynamics and achieves asymptotic tracking while requiring only the knowledge of the sign of the leading principal minors of the input gain matrix. A Lyapunov-based argument backed up with an integral inequality is applied to prove the asymptotic stability of the closed-loop system. Simulation results are presented to illustrate the viability of the proposed method.     

Discrete terminal sliding mode repetitive control for a linear actuator with nonlinear friction and uncertainties

A robust discrete terminal sliding mode repetitive controller is proposed for a class of nonlinear positioning systems with parameter uncertainties and nonlinear friction. The terminal sliding mode control (TSMC) part is designed to improve the transient characteristics of the system, as well as the robustness against parameter uncertainties, nonperiodic nonlinearities, and disturbances. The repetitive control (RC) part is then integrated to eliminate the effects of the periodic uncertainties present in the system. Moreover, a pure phase lead compensator is incorporated into the RC to improve the tracking at high frequencies. A robust stability analysis and an analysis of the finite time convergence properties of the proposed controller are also provided in this paper. Simulation testing and an experimental validation using a linear actuator system with nonlinear friction and parameter uncertainties are conducted to verify the effectiveness of the proposed controller.     

Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

In this paper, a data-driven control approach is developed by reinforcement learning (RL) to solve the global robust optimal output regulation problem (GROORP) of partially linear systems with both static uncertainties and nonlinear dynamic uncertainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncertainties. The closed-loop system is ensured to be input-to-output stable regarding the static uncertainty as the external input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-output stability for the closed-loop system and thus solves the original GROORP. Simulation results validates the efficacy of the proposed methodology.