Aerospace launch vehicle control: an intelligent adaptive approach

A theoretical analysis of on-line autonomous intelligent adaptive tracking controller based on emotional learning model in mammalians brain (BELBIC) for aerospace launch vehicle is presented. The control algorithm is provided with some sensory inputs and reward signal, subsequently it autonomously seeks the proper control signal to be executed by actuators, thus eliminating tracking error without pre-knowledge of the plant dynamics. The algorithm is very robust and fast in adaptation with dynamical change in the plant, due to its on-line learning ability. Development and application of this algorithm for an aerospace launch vehicle during atmospheric flight in an experimental setting is presented to illustrate the performance of the control algorithm.     

RFNN control for PMLSM drive via backstepping technique

A robust fuzzy neural network (RFNN) control system is proposed in this study to control the position of the mover of a permanent magnet linear synchronous motor (PMLSM) drive system to track periodic reference trajectories. First, an ideal feedback linearization control law is designed based on the backstepping technique. Then, a fuzzy neural network (FNN) controller is designed to be the main tracking controller of the proposed RFNN control system to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the FNN controller. Moreover, to relax the requirement for the bound of uncertainty term, which comprises a minimum approximation error, optimal parameter vectors and higher order terms in Taylor series, an adaptive bound estimation is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. Furthermore, the simulated and experimental results due to periodic reference trajectories demonstrate that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.     

基于自适应模糊系统的空天飞行器非线性预测控制

 针对一类多输入多输出非线性不确定系统,提出了基于自适应模糊系统的非线性预测控制方法。控制器由基于模糊系统的非线性预测控制器和鲁棒自适应控制器两个部分组成。根据系统的跟踪误差在线调整模糊系统的权值,使得模糊系统一致逼近被控对象中的非线性函数,通过泰勒展开设计出基于模糊系统的非线性预测控制律,避免了预测控制在线优化带来的繁重的计算负担。鲁棒自适应控制器则用于减少不确定和模糊逼近误差对系统的影响。所设计的控制器保证了闭环系统的最终一致有界稳定。基于Lyapunov稳定原理,给出了理论证明和分析。最后利用提出的控制方案设计了空天飞行器高超声速飞行姿态的控制系统,仿真结果表明了控制方案的有效性。     

机械手的神经网络自适应滑动模控制器设计

 针对多关节机械手的鲁棒跟随控制器设计问题，提出了一种新的机械手神经网络自适应滑动模控制器设计方法，机械手的动力学非线性假设是完全未知的。在提出的控制结构中，高斯径向基函数神经网络用于在线补偿机械手的动力学非线性，参数学习律由稳定性理论得到。给出了系统稳定性和参数收敛性的证明。最后提出方法的可行性通过仿真得到验证。     

Fuzzy adaptive robust control for space robot considering the effect of the gravity

Space robot is assembled and tested in gravity environment, and completes on-orbit service（OOS） in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of gravity in different working condition. Fully considering the change of kinematic and dynamic models caused by the change of gravity environment, a fuzzy adaptive robust control（FARC） strategy which is adaptive to these model variations is put forward for trajectory tracking control of space robot. A fuzzy algorithm is employed to approximate the nonlinear uncertainties in the model, adaptive laws of the parameters are constructed, and the approximation error is compensated by using a robust control algorithm. The stability of the control system is guaranteed based on the Lyapunov theory and the trajectory tracking control simulation is performed. The simulation results are compared with the proportional plus derivative（PD） controller, and the effectiveness to achieve better trajectory tracking performance under different gravity environment without changing the control parameters and the advantage of the proposed controller are verified.     

考虑执行机构饱和的迎角跟踪控制

设计提出了 1种针对高光谱图像分类任务的 3D-MSCNN模型。在 PCA降维的基础上,利用 3D空谱特征提取网络和 2D多尺度特征提取网络实现高光谱图像特征提取,充分发挥高光谱图像空谱信息价值,增强对不同尺度地表覆盖的表达能力。最后,利用 Softmax分类损失函数实现高光谱图像分类任务。实验结果表明,本文算法在 In. dian Pines和 Pavia University数据集上都取得了较好的分类效果。与 CD-CNN、3D-CNN、SS-Net和 HybirdSN等方法相比,本文算法能够有效提升总体精度、平均精度和 Kappa系数等客观评价指标。     

一种鲁棒混合自适应控制器

 目前,国内外关于对象具有干扰或未建模动态时的混合自适应控制,仍处于探索阶段。由于连续对象混合控制系统在某些方面优于全连续或全离散系统,因而引起了国内外学者的重视。 本文提出一种新的适用于受扰对象的混合自适应律,并且从理论上证明了由这种控制律所构成的自适应控制器具有鲁棒稳定性。     

挠性卫星大角度机动的混合控制方法研究

对于具有不确定性的多输人多输出非线性系统,仅用v0线性化设计方法不能获得好的控制结果.为此,设计了自适应模糊控制器,把I/O线性化方法和自适应模糊控制相结合,构成混合控制器.并将此控制器引人到卫星大角度机动控制系统中,以补偿由系统的不确定性所造成的跟踪误差,从而增强I/O线性化控制器的鲁棒性,最终实现零误差跟踪.     

Discrete time multiple spacecraft formation flying attitude optimal control in presence of relative state constraints

This paper addresses the challenge of synchronized multiple spacecraft attitude reorientation in presence of pointing and boundary constraints with limited inter-spacecraft communication link. Relative attitude pointing constraint among the fleet of spacecraft has also been modeled and considered during the attitude maneuvers toward the desired states. Formation fling control structure that consists of decentralized path planners based on virtual structure approach joint with discrete time optimal local controller is designed to achieve the mission’s goals. Due to digital computing of spacecraft’s onboard computer, local optimal controller based on discrete time prediction and correction algorithm has been utilized. The time step of local optimal algorithm execution is designed so that the spacecraft track their desired attitudes with appropriate error bound. The convergence of the proposed architecture and stability of local controller’s tracking error within appropriate upper bound are proved. Finally, a numerical simulation of a stereo imaging scenario is presented to verify the performance of the proposed architecture and the effectiveness of the algorithm.     

Adaptive Fuzzy Torque Control of Passive Torque Servo Systems Based on Small Gain Theorem and Input-to-state Stability

Passive torque servo system (PTSS) simulates aerodynamic load and exerts the load on actuation system, but PTSS endures position coupling disturbance from active motion of actuation system, and this inherent disturbance is called extra torque. The most important issue for PTSS controller design is how to eliminate the influence of extra torque. Using backstepping technique, adaptive fuzzy torque control (AFTC) algorithm is proposed for PTSS in this paper, which reflects the essential characteristics of PTSS and guarantees transient tracking performance as well as final tracking accuracy. Takagi-Sugeno (T-S) fuzzy logic system is utilized to compensate parametric uncertainties and unstructured uncertainties. The output velocity of actuator identified model is introduced into AFTC aiming to eliminate extra torque. The closed-loop stability is studied using small gain theorem and the control system is proved to be semiglobally uniformly ultimately bounded. The proposed AFTC algorithm is applied to an electric load simulator (ELS), and the comparative experimental results indicate that AFTC controller is effective for PTSS.     

冲压发动机连续可调喷管模糊控制研究

为了提高几何可调冲压发动机尾喷管控制性能,针对具有静差与动态不对称特性的连续可调喷管作动系统,采用模糊控制算法进行控制器设计,并利用自适应遗传算法进行控制器参数优化,最后将模糊控制系统与PID控制系统进行性能对比。结果表明,相较于常规模糊控制系统,优化后的模糊控制系统消除了稳态误差,减小了大小喉径在调节时间上的差异,鲁棒性较强。相较于优化后的PID控制系统,优化后的模糊控制系统在保证无稳态误差的基础上,具有更短的调节时间与更小的超调量,跟踪信号时具有更小的误差包络,优化后的模糊比PID控制系统性能提高2.6倍。总之,基于自适应遗传算法优化的连续可调喷管模糊控制系统比PID控制系统性能更高,能够满足高动态几何可调冲压发动机提出的响应快、调节精准、超调小的要求。     

高精度激光跟踪系统的综合设计

1.前言 激光跟踪系统是航空航天工程中的重要设备之一。要想建造高精度激光跟踪系统,除了提高有关部件的精度之外,系统的综合设计是一个关键步骤。对于这类系统的综合设计,目前都采用全连续或全离散控制方案,而且不考虑对象未建模动态和子系统间的耦合问题。     

机载光电跟踪系统模糊控制的优化设计与仿真

 分析了机载光电跟踪系统的构成,并对机载光电跟踪系统的最主要组成部分——陀螺稳定平台框架系统设计了基于遗传算法的模糊控制器,通过遗传算法来优化模糊控制器的规则、参数以及量化因子和比例因子。并对系统进行了详细的仿真研究,仿真结果证明基于 GA的模糊控制器设计获得了良好的控制效果。     

基于自适应神经网络的非线性飞行控制

结合反馈线性化和神经网络提出了一种新的飞行控制系统设计方法,通过反馈线性化将非线性耦合系统等效转换为线性解耦系统,利用具有在线学习能力的神经网络补偿反馈线性化的误差,建立了基于自适应神经网络的控制结构,并利用李亚普诺夫函数导出了网络权值的自适应调整规则。在巡航弹地形跟踪应用中的结果表明,该控制系统不仅具有精确的跟踪性能而且具有良好的鲁棒性。     

Adaptive controller design for a linear motor control system

Three different adaptive controllers for a permanent magnet linear synchronous motor (PMLSM) position-control system are proposed. The proposed controllers include: a backstepping adaptive controller, a self-tuning adaptive controller, and a model reference adaptive controller. The detailed systematic controller design procedures are discussed. A PC-based position control system is implemented. Several experimental results including transient responses, load disturbance responses, and tracking responses of square-wave, sinusoidal-wave, and triangular-wave commands are discussed and compared. The proposed system has a good robustness performance even though the inertia of the system is increased to 10 times. The experimental results validate the theoretical analysis.     

Adaptive recurrent-neural-network control for linear inductionmotor

In this study an adaptive recurrent-neural-network controller (ARNNC) is proposed to control a linear induction motor (LIM) servo drive. First, the secondary flux of the LIM is estimated with an adaptive flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, an ARNNC is proposed to control the mover of the LIM for periodic motion. In the proposed controller, the LIM servo drive system is identified by a recurrent-neural-network identifier (RNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the RNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RNNI and the optimal learning rate of the adaptive controller. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system     

高速开关阀控电液位置伺服系统自适应鲁棒控制

为研究高精度的液压缸位置跟踪控制问题，设计了高速开关阀和换向阀组合控制液压缸的结构方案，并通过实验分析了高速开关阀的静态流量特性。建立了液压缸的连续可微摩擦模型，利用粒子群优化算法对其参数进行辨识。建立了系统的非线性数学模型，基于非连续参数映射和反步法设计了直接自适应鲁棒控制器，通过参数在线自适应调节来更新估计值和鲁棒反馈项支配参数不确定性，实验结果表明:在跟踪幅值为5mm，频率为0.4Hz的正弦信号时，最后一个周期的最大跟踪误差、平均跟踪误差及其标准差分别为0.638、0.25mm和0.405mm，与传统PID控制器相比，控制精度显著提升，旨在为实现高精度的数字阀控位置伺服技术提供有价值的参考。      

NONLINEAR PREDICTIVE CONTROL FOR TERRAIN FOLLOWING

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压电陶瓷叠层作动器迟滞蠕变非线性自适应混合补偿控制方法

压电陶瓷叠层作动器的迟滞蠕变非线性特性严重影响了控制系统的稳定性及动态跟踪精度。针对其迟滞蠕变非线性补偿控制问题,提出了一种高精度动态补偿压电陶瓷叠层作动器非线性特性的自适应混合补偿控制方法,即迟滞蠕变前馈补偿与自适应滤波反馈补偿结合的前馈-反馈混合控制方法。采用改进的Prandtl-Ishlinskii（Modified Prandtl-Ishlinskii,MPI）模型对压电陶瓷叠层作动器迟滞蠕变非线性特性进行精细化建模,并得到其逆补偿模型进行前馈补偿。根据前馈补偿误差,采用自适应滤波反馈控制对输入信号进行实时调控,实现对压电陶瓷叠层作动器的迟滞非线性及lg（t）型蠕变特性的实时精确补偿控制。数值仿真与实验结果表明,相比于常规前馈迟滞蠕变补偿,所提出的自适应混合补偿控制方法可以有效降低压电陶瓷叠层作动器的迟滞补偿误差,极大提高了迟滞蠕变非线性动态跟踪精度以及自适应性。     

Adaptive variable structure tracking control for constrained robots

The problem of controller design for constrained robots with the consideration of computational efficiency is addressed. An efficient adaptive variable structure control algorithm based on a reduced dynamics formulation, is presented for trajectory tracking of an end-effector on a constrained surface with specified constraint forces. It is shown that the objective can be achieved without exact knowledge of robot dynamics and on-line calculation of nonlinear dynamic functions. The control algorithm is constructed with at most three control parameters to be adjusted adaptively, and that number is determined independently of the number of degrees of freedom of the robotic manipulators. A numerical example is presented to illustrate the developed method