飞机自动着陆的一种非线性鲁棒控制器设计

将一种直接基于非线性模型的带神经网络补偿信号的逆系统方法用于具有强非线性和受不确定扰动干扰的飞机自动着陆控制,并对神经网络补偿的方式进行了改进。采用神经网络补偿动态逆反馈线性化后伪系统的逆误差,使得非线性系统在参数受到摄动或外部扰动的情况下仍能保持良好的控制效果。可以证明该方法在理论上是收敛的,对于任意的状态初值和给定的期望输出信号,能控制到指定的精度。神经网络的权值是局部收敛的,同时大量仿真表明,经过较少的神经网络离线训练,即能够获得很好的控制效果。最后通过飞机着陆下滑段的仿真验证表明,该方法具有强的鲁棒性和良好的跟踪精度。     

基于模糊干扰观测器的无人机自主着陆逆控制器设计

针对具有显著非线性和不确定性的无人机自主着陆系统,提出基于模糊干扰观测器的非线性动态逆的控制方法,用于降低控制器对不确定性的要求。基于时标分离原则,将无人机自主着陆系统分为快回路、慢回路、非常慢回路和极慢回路,通过在快回路、慢回路和非常慢回路设计动态逆控制律使状态解耦,设计直线下滑和指数拉平的着陆轨迹,并在极慢回路进行跟踪。设计基于模糊系统的干扰观测器,以逼近外部干扰和内部不确定性等复合干扰,基于李雅普诺夫理论证明系统稳定性。最后给出了无人机自主着陆轨迹跟踪控制仿真,仿真结果表明设计控制器具有良好鲁棒性,完成无人机在外界干扰下的自主着陆控制。     

不确定蔡氏电路混沌系统的神经网络优化控制

该文研究了不确定非线性蔡氏电路混沌系统的动态神经网络在线辨识和跟踪控制问题.利用无源技术得出梯度下降算法调整神经网络辨识器权值的稳定性定理,然后在辨识模型基础上设计局部优化控制器,将蔡氏混沌系统镇定到期望目标轨迹,并保证跟踪误差有界.数值仿真结果表明了所提出方法的有效性.     

基于神经网络仿射非线性系统滑模自适应控制

研究了一类单输入单输出仿射非线性系统的自适应控制问题.采用反馈线性化方法设计控制器,用神经网络逼近系统中的未知非线性函数,并在神经网络权值的自适应律中引入权值误差的概念,以改善系统的动态性能.同时采用滑模控制方法设计补偿器,提高了系统的鲁棒性.理论分析及仿真结果表明,所设计的控制器,不仅能解决该系统的轨迹跟踪控制问题,...     

基于干扰观测器的无人机着陆飞行逆控制器设计

基于干扰观测器设计了无人机着陆飞行逆控制器。根据时标分离的原则,将无人机系统分解为快慢不同的4个回路,采用动态逆的方法设计快回路、慢回路和非常慢回路控制器,并且在慢回路引入干扰观测器估计无人机所受的扰动和在线估计动态逆误差,降低控制器对干扰和模型精确度的要求,增强控制器的鲁棒性。仿真结果说明所设计的无人机着陆控制器是非常有效的。     

执行器带未知不对称死区的一类仿射非线性系统的 神经网络自适应控制器的设计

 本文针对一类执行器带未知死区的仿射非线性系统,提出了一种新型的神经网络自适应控制器的设计方法,该方法首先引入一个神经网络来估计对象的部分未知非线性动态行为,再基于隐函数定理构造另一个静态神经网络作为新型补偿器以补偿执行器的未知不对称的死区非线性.本文利用Lyapunov理论在给出光滑的控制律的同时严格证明了整个闭环系统的跟踪误差以及各个神经网络权参数的一致最终有界性,而且通过调节设计参数可以使系统的跟踪误差收敛到零附近的一个小邻域内.本文提出的控制方案可以保证对象在线稳定地跟踪任何光滑的目标轨迹,仿真研究表明了此控制方案的可行性和有效性.     

结构自适应反步容错控制器设计

基于一类由状态空间描述的非线性系统固有的结构级联特性,在飞行器发生卡死故障的情况下,设计了基本控制律加补偿控制律的控制器结构形式,补偿故障对系统性能造成的影响。提出了确保对参考模型精确跟踪的控制器结构和条件。控制器的基本控制律部分是利用系统的结构特性设计反步(backstepping)控制律,系统发生未知卡死故障时,利用实际对象和参考模型之间的误差,更新控制器的故障补偿部分。控制器同时保证了闭环系统的稳定性和参考模型状态跟踪误差渐近性。仿真结果表明了该控制律对给定参考模型跟踪的有效性。     

基于RBF神经网络的自适应反演大机动飞行控制器设计

针对飞行器在大机动飞行过程中气动参数不确定、外部未知干扰因素较多及系统建模可能存在误差等问题,设计了一种基于RBF神经网络的非线性自适应反演控制器。飞行器大机动飞行过程中的广义不确定性由RBF神经网络在线逼近,神经网络权值矩阵通过自适应律在线更新。反演设计过程中对虚拟控制律的反复求导带来的"项数膨胀"问题,通过引入一阶滤波器来解决。通过构造Lyapunov函数,证明了闭环系统所有信号均有界,并且跟踪误差指数收敛到零的一个小邻域内。对某飞行器进行了大机动飞行仿真,结果表明该控制器具有良好的跟踪效果和鲁棒性。     

基于最优控制的神经网络自适应飞行控制

针对非线性动态逆控制鲁棒性差的缺点,综合最优控制和神经网络控制,提出一种自适应非线性控制策略。首先采用非线性动态逆进行基本控制律设计,然后对由于建模误差或舵面故障等因素导致的动态逆误差,利用神经网络进行在线补偿,根据最优控制理论得到神经网络权值的自适应律,并基于Lyapunov直接法证明该自适应律的稳定性和收敛性。针对某飞机的仿真结果表明,在存在较大逆误差的情况下,所设计的控制系统具有良好的鲁棒性。     

基于在线神经网络的自适应鲁棒飞行控制

一般的自适应神经网络,因为没有长期学习性与全局适应性,只能适应当前的瞬时状态,满足不了导弹高精度飞行的要求.基于李亚普诺夫稳定理论和神经网络的非线性函数的拟合特性,设计了具有背景学习功能的在线自适应神经网络鲁棒控制器.首先分析了逆误差产生的原因,然后用神经网络来补偿系统逆模型误差,并利用李亚普诺夫稳定性理论推导了在线网...     

Neural Network Based Adaptive Tracking of Nonlinear Multi-Agent System

In this paper, the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered. An adaptive control strategy is proposed to smooth the agent’s trajectory, and the neural network is constructed to estimate the system’s unknown components. The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties. Then, the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’ models. Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control. Finally, the theoretical results are verified by numerical simulations, and a comparative experiment is conducted, showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.     

Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor

In this paper, an adaptive cerebellar-model articulation computer (CMAC) neural network (NN) control system is developed for a linear piezoelectric ceramic motor (LPCM) that is driven by an LLCC-resonant inverter. The motor structure and LLCC-resonant driving circuit of an LPCM are introduced initially. The LLCC-resonant driving circuit is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive CMAC NN control system is designed without mathematical dynamic model to control the position of the moving table of the LPCM drive system to achieve high-precision position control with robustness. In the proposed control scheme, the dynamic backpropagation algorithm is adopted to train the CMAC NN online. Moreover, to guarantee the convergence of output tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are utilized to determine the optimal learning-rate parameters of the CMAC NN. The effectiveness of the proposed driving circuit and control system is verified by experimental results in the presence of uncertainties, and the advantages of the proposed control system are indicated in comparison with a traditional integral-proportional position control system. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN with optimal learning-rate parameters.     

基于神经网络广义逆两电机同步控制研究

基于神经网络广义逆系统,提出二自由度的内模控制。来改善两电机同步系统的解耦控制性能和鲁棒性能。提出先对原来系统的数学模型进行广义逆的存在性分析,进而推出原系统广义逆的数学模型,再用神经网络逼近广义逆,接在原系统前组成具有等价效果的伪线性系统,来实现系统的解耦线性化。有利于系统的综合。然后对广义伪线性系统引入二自由度内模控制,以保证系统的鲁棒稳定性。     

Nonlinear predictive control with application to manipulator withflexible forearm

A neural network is constructed to represent the input-output relation of a dynamical model. The parameters are calculated by means of a second-order training algorithm. Then, a nonlinear predictive controller is designed on the basis of a neural network plant model using the receding-horizon control approach. Based on the neural model, the control is calculated by minimizing a projected cost function that penalizes future tracking errors. As an illustration of the approach, the nonlinear dynamics of a planar two-joint arm with a flexible forearm are modeled using a sigmoidal network and an offline estimation procedure for a range of motions. The applicability of the approach is illustrated through computer simulations     

Decoupling control for two-axis inertially stabilized platform based on an inverse system and internal model control

This paper describes a decoupling control scheme for a two-axis inertially stabilized platform (ISP) used in the airborne power line inspection system. The dynamic model of the ISP has been obtained by using the Newton–Euler equation first. The inverse system method combining with the internal model control has been proposed to deal with the nonlinearity and coupling of the ISP. The key idea is to design an inverse system with measured system states such as angular positions, rates and accelerations. Then a pseudo-linear system is constructed when the inverse system is connected with the original system in series. As a result, the coupled nonlinear MIMO (Multiple-Input Multiple-Output) system is converted to two linear decoupled SISO (Single-Input Single-Output) subsystems. Model uncertainties or unmeasurable disturbances existing objectively can be solved by introducing internal model control. Better decoupling effect and disturbance rejecting ability are demonstrated by numerical simulations and experiments carried out on a two-axis ISP system.     

Robust Control of PM Spherical Stepper Motor Based on Neural Networks

There are many uncertainties and disturbances in the real dynamic system of a spherical stepper motor that make traditional control methods with lower precision, such as uncertain changes of magnetic field, load, and friction that generate speed ripple and deteriorate the 3-D tracking performance of the spherical motor system. In this paper, an available method is proposed to solve them by using neural networks (NNs) and a robust control scheme for improving the performance. First, a simplified torque calculation model based on finite-element method results can guarantee quick prediction of electromagnetic torque with lower error. Thus, the system model considering the friction, load, and disturbances is developed. Second, a robust NN (RNN) control scheme is presented to eliminate uncertainties to improve the tracking robust stability and overcome the undesired influence of uncertainties based on the nonlinear system dynamic model under continuous-trajectory tracking mode. Finally, as an example, the step-response and continuous-tracking processes of the motor using an RNN controller are simulated, and experiments, including the tracking using RNN proportional–differential control, are carried out to confirm the usefulness of the proposed control scheme. The simulation and experimental results of the proposed control scheme on the spherical stepper motor system demonstrate the effectiveness on satisfactory tracking performance.      

Linear and nonlinear ARMA model parameter estimation using anartificial neural network

Addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, the authors investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, the authors show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. They compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, the authors show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations     

无人机自主低空突防航迹控制系统设计

设计了一种无人机自主低空突防航迹控制系统。提出了一种满足无人机低空突防要求的航迹预规划方法和航迹控制指令生成算法。针对动态逆方法需要被控对象精确数学模型的难点,在分析无人机误差动力学特性的基础上,采用神经网络在线动态补偿模型逆误差,并运用李亚普诺夫稳定性理论推导网络权值更新律,确保指令跟踪误差和网络权值的有界性。数字仿真结果表明,该系统满足无人机自主低空突防时航迹精确跟踪要求。     

Development of intelligent position control system using optimal design technique

This paper addresses the application of an intelligent optimal control system (IOCS) to control an indirect field-oriented induction servo motor drive for tracking periodic commands via a wavelet neural network. With the field orientation mechanism, the dynamic behavior of an induction motor is rather similar to a linear system. However, the uncertainties, such as mechanical parametric variation, external load disturbance and unmodeled dynamics in practical applications, influence the designed control performance seriously. Therefore, an IOCS is proposed to confront these uncertainties existing in the control of the induction servo motor drive. The control laws for the IOCS are derived in the sense of the optimal control technique and Lyapunov stability theorem, so that system-tracking stability can be guaranteed in the closed-loop system. With the proposed IOCS, the controlled induction servo motor drive possesses the advantages of good tracking control performance and robustness to uncertainties under wide operating ranges. The effectiveness of the proposed control scheme is verified by both simulated and experimental results. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system.