Adaptive integral dynamic surface control based on fully tuned radial basis function neural network

An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

An Adaptive Identification and Control SchemeUsing Radial Basis Function Networks

In this paper, adaptive identification and control of nonlinear dynamical systems are investigated using radial basis function networks (RBF). Firstly, a novel approach to train the RBF is introduced, which employs an adaptive fuzzy generalized learning vector quantization (AFGLVQ) technique and recursive least squares algorithm with variable forgetting factor (VRLS). The AFGLVQ adjusts the centers of the RBF while the VRLS updates the connection weights of the network. The identification algorithm has the properties of rapid convergence and persistent adaptability that make it suitable for real-time control. Secondly, on the basis of the one-step ahead RBF predictor, the control law is optimized iteratively through a numerical stable Davidon's least squares-based (SDLS) minimization approach. Four nonlinear examples are simulated to demonstrate the effectiveness of the identification and control algorithms.

Global approximation based adaptive RBF neural network control for supercavitating vehicles

A global approximation based adaptive radial basis function（RBF） neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles（SV）.A nominal model is built firstly with the unknown disturbance.Next, the control scheme is established consisting of a computed torque controller（CTC） for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.

An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems

This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC

For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

Trajectory linearization control of an aerospace vehicle based on RBF neural network

An enhanced trajectory linearization control （TLC） structure based on radial basis function neural network （RBFNN） and its application on an aerospace vehicle （ASV） flight control system are presensted. The influence of unknown disturbances and uncertainties is reduced by RBFNN thanks to its approaching ability, and a robustifying itera is used to overcome the approximate error of RBFNN. The parameters adaptive adjusting laws are designed on the Lyapunov theory. The uniform ultimate boundedness of all signals of the composite closed-loop system is proved based on Lyapunov theory. Finally, the flight control system of an ASV is designed based on the proposed method. Simulation results demonstrate the effectiveness and robustness of the designed approach.

Hardware-in-loop adaptive neural control for a tiltable V-tail morphing aircraft

This paper proposes an adaptive neural control(ANC)method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail.A nonlinear model with sixdegrees-of-freedom is established.The first-order sliding mode differentiator(FSMD)is applied to the control scheme to avoid the problem of“differential explosion”.Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model,and an ANC controller is proposed based on this design idea.The stability of the proposed ANC controller is proved using Lyapunov theory,and the tracking error of the closed-loop system is semi-globally uniformly bounded.The effectiveness and robustness of the proposed method are verified by numerical simulations and hardware-in-the-loop(HIL)simulations.

Robust adaptive control of hypersonic vehicle considering inlet unstart

In this paper,a model reference adaptive control(MRAC)augmentation method of a linear controller is proposed for air-breathing hypersonic vehicle(AHV)during inlet unstart.With the development of hypersonic flight technology,hypersonic vehicles have been gradually moving to the stage of weaponization.During the maneuvers,changes of attitude,Mach number and the back pressure can cause the inlet unstart phenomenon of scramjet.Inlet unstart causes significant changes in the aerodynamics of AHV,which may lead to deterioration of the tracking performance or instability of the control system.Therefore,we firstly establish the model of hypersonic vehicle considering inlet unstart,in which the changes of aerodynamics caused by inlet unstart is described as nonlinear uncertainty.Then,an MRAC augmentation method of a linear controller is proposed and the radial basis function(RBF)neural network is used to schedule the adaptive parameters of MRAC.Furthermore,the Lyapunov function is constructed to prove the stability of the proposed method.Finally,numerical simulations show that compared with the linear control method,the proposed method can stabilize the attitude of the hypersonic vehicle more quickly after the inlet unstart,which provides favorable conditions for inlet restart,thus verifying the effectiveness of the augmentation method proposed in the paper.

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances. © 2014 Chinese Association of Automation.

Adaptive Backstepping Control for Uncertain Systems with Compound Nonlinear Characteristics

An adaptive backstepping multi-sliding mode approximation variable structure control scheme is proposed for a class of uncertain nonlinear systems.An actuator model with compound nonlinear characteristics is established based on the model decomposition method.The unmodeled dynamic term of the radial basis function neural network approximation system is presented.The Nussbaum gain design technique is utilized to overcome the problem that the control gain is unknown.The adaptive law estimation is used to estimate the upper boundary of neural network approximation and uncertain interference.The adaptive approximate variable structure control effectively weakens the control signal chattering while enhancing the robustness of the controller.Based on the Lyapunov stability theory,the stability of the entire control system is proved.The main advantage of the designed controller is that the compound nonlinear characteristics are considered and solved.Finally,simulation results are given to show the validity of the control scheme.

Modeling and robust adaptive control for a coaxial twelve-rotor UAV

Compared with the quad-rotor unmanned aerial vehicle (UAV), the coaxial twelve-rotor UAV has stronger load carrying capacity, higher driving ability and stronger damage resistance. This paper focuses on its robust adaptive control. First, a mathematical model of a coaxial twelve-rotor is established. Aiming at the problem of model uncertainty and external disturbance of the coaxial twelve-rotor UAV, the attitude controller is innovatively adopted with the combination of a backstepping sliding mode controller (BSMC) and an adaptive radial basis function neural network (RBFNN). The BSMC combines the advantages of backstepping control and sliding mode control, which has a simple design process and strong robustness. The RBFNN as an uncertain observer, can effectively estimate the total uncertainty. Then the stability of the twelve-rotor UAV control system is proved by Lyapunov stability theorem. Finally, it is proved that the robust adaptive control strategy presented in this paper can overcome model uncertainty and external disturbance effectively through numerical simulation and prototype of twelve-rotor UAV tests.

基于混合Elman网络的非线性自适应逆控制

针对Elman动态递归神经网络的特点,在现有结构的基础上提出一种隐层包含径向基函数的混合Elman神经网络,给出了网络结构和训练算法,并将其应用于非线性自适应逆控制系统中。仿真实验表明该动态神经网络自适应性强,动态特性优异。此方法为实现非线性系统自适应逆控制提供了理想的途径。

An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and reallife systems, where the results obtained by our method are highly promising.

无模型自适应滑模控制的微波加热过程温度控制

微波加热模型具有无限维、非线性和时变等特点,导致控制器难于设计和实现。针对此问题,提出了一种适用于微波加热过程的无模型自适应滑模控制方法。首先,对微波加热过程传热数学模型进行分析,建立了微波加热过程输入功率与温度之间的全格式动态线性化数据模型。然后,根据该数据模型设计了无模型自适应滑模控制器,并给出了数据模型中相关未知时变参数和未知干扰的估计算法。最后,利用COMSOL和MATLAB进行仿真,仿真结果验证了所提控制方法的有效性。

基于自适应扰动观测器的旋转弹神经网络过载驾驶仪设计

旋转弹在飞行过程中受多种干扰的影响,包括跨域飞行气动参数剧烈变化引起的模型不确定性以及飞行过程中受到的外部扰动。为了解决高动态飞行环境中双通道旋转弹的鲁棒控制问题,基于轨迹线性化控制方法,设计伪逆反馈控制器。采用径向基函数神经网络,设计自适应前馈补偿控制器,有效实现对模型不确定性的精确逼近。将神经网络逼近误差和外部扰动处理为总扰动,并基于固定时间稳定理论设计一种自适应扰动观测器,实现对总扰动的精确估计及补偿。通过Lyapunov理论,严格证明了闭环系统的最终一致有界性。通过数值仿真验证了所设计方法的有效性。

基于RBF神经网络补偿的ROV运动控制算法

针对作业型遥控水下航行器(ROV)在模型参数不确定和外部环境干扰下的运动控制问题,提出了一种基于径向基函数(RBF)神经网络的自适应双环滑模控制策略。首先,对于ROV外环位置控制采用改进趋近律的积分滑模控制方法,对于ROV内环速度控制采用指数趋近律的积分滑模控制方法;其次,为进一步改善滑模控制的抖振问题,引入双曲正切函数作为滑模切换项;然后,利用RBF神经网络控制技术对ROV模型的不确定参数和外部扰动进行估计与补偿;最后,利用李雅普诺夫稳定性理论证明了整个闭环系统的稳定性,并对作业型ROV的运动控制进行了数值仿真。仿真结果验证了所设计的控制器可以实现ROV航行的精确控制,并能够有效抑制模型不确定参数和外部扰动对ROV运动的影响。

基于分数阶自适应神经网络的电动舵机伺服系统摩擦干扰补偿控制

摩擦干扰力矩影响电动舵机伺服系统的跟踪性能,造成位置和速度跟踪偏差,甚至可能导致伺服系统不稳定。针对摩擦力矩干扰下的电动舵机伺服系统跟踪性能差的问题,本文提出了一种分数阶自适应神经网络摩擦补偿算法(FOANN),估计并补偿摩擦干扰力矩。首先,建立基于LuGre模型的电动舵机伺服系统模型,利用径向基神经网络估计模型中的不可测状态变量。其次,设计FOANN摩擦补偿控制器,利用李雅普诺夫稳定性理论证明电动舵机闭环系统的稳定性。最后,利用仿真和实验平台,对比分析了FOANN、传统PD控制和模型自适应控制的性能。结果表明,基于本文所提出的FOANN摩擦力矩补偿控制算法,电动舵机伺服系统的位置跟踪误差和速度跟踪误差均大幅减小,FOANN算法能够有效估计并补偿摩擦力矩,降低摩擦干扰对电机舵机伺服系统的影响,提高伺服系统的动态性能。

基于RBF的VSG虚拟惯量和动态阻尼补偿自适应控制

虚拟同步机(virtual synchronous generator,VSG)技术可以使并网逆变器具有与同步发电机类似的外特性。VSG系统暂态稳定性的主要影响因素是虚拟惯量和阻尼系数,但现有的控制策略在参数调节过程中存在灵活性不足的缺点,不能有效解决系统暂态稳定性和暂态恢复时间的问题。针对这一问题,提出动态调节阻尼补偿量的概念。将阻尼系数和阻尼补偿量共同作为系统的等效阻尼系数,设计了基于径向基函数(radial basis function, RBF)的VSG虚拟惯量和动态阻尼补偿自适应控制策略,实现了参数之间的解耦,使系统的阻尼随着系统频率的变化进行动态调整。通过建立VSG数学模型,确定了参数的具体取值范围。最后,在仿真平台上搭建VSG系统,分别在出力波动和低压穿越两种工况下验证了所提控制策略相较于传统RBF控制策略的优越性。

多智能体系统的动态面渐近补偿算法

针对一类具有磁滞输入的多智能体系统协同控制问题,设计一种基于动态面的神经网络有限时间性能渐近控制补偿算法。首先,通过Funnel控制结合有限时间性能函数,确保一致性误差可以在有限时间内进入预定义范围。其次,使用径向基函数神经网络(RBFNN)和不等式变换消除系统内未知非线性函数和未知外部扰动带来的不利影响。此外,通过估计一些未知变量的上界,大幅减少设计过程中所需自适应律数;同时,提出一种具有双曲正切函数的非线性滤波器,避免传统反步控制中的“微分爆炸”问题,并消除滤波器误差。最后,基于所提非线性滤波器设计一种磁滞伪逆补偿信号,在不需要构建磁滞逆的情况下有效补偿未知磁滞。利用李雅普诺夫稳定性理论,验证了闭环系统内所有信号都有界,一致性误差渐近收敛至零。仿真实例也表明了所提算法的有效性。