三轴飞行姿态仿真转台高性能指标及其实现

本文详细讨论了三轴飞行姿态仿真转台（三轴均由液压摆动马达驱动）的主要性能指标及影响它们的主要因素，在此基础上，给出了实现三轴飞行姿态仿真转台高性能指标的途径。同时，对三轴飞行姿态仿真转台中框架双液压马达同步驱动方案进行了探讨。     

基于神经网络学习控制的液压转台偏载干扰的抑制

飞行仿真转台是将飞行器置于一定的实际飞行环境中，研究其传感器，控制系统及执行机构等硬件设备的动态性能和定位精度时所用的种混合仿真设备。哈尔滨工业大学研制的ＨＩＴ－１型三轴飞行仿真转台中框由于存在设计上不可避免的偏载负载力矩，影响了其响应速度和动静态精度的进一步提高。本文将学习控制和神经网络方法结合起来，既避免了前者对重复性的要求，又避免了后者对快速系统实时控制的困难，将其应用于对液压转台中框偏载干扰的抑制，取得了良好的仿真结果     

三轴陀螺漂移测试转台台体动力学建模及控制系统设计

带宽的保证是三轴陀螺漂移测试转台（以下简称三轴转台）伺服系统设计的主要困难，从使用的角度出发，要求转台伺服系统有较大的带宽，以使三轴转台有较快的响应速度，对干扰有较强的抑制能力，提高三轴转台的跟踪精度，但一些客观因素使带宽指标受到限制，其中机械台体的谐振对带宽的影响是决定性的。本所讨论的三轴转台动力学模型，是三轴转台控制系统设计的依据。     

基于MEMS加速度计的弹体滚转角测量方法

为了实时获取弹体飞行时的滚转角姿态信息,提高高速滚转制导弹药的射击精度,提出了一种使用两个MEMS加速度计构成滚转角测量系统的方法。开展了弹上姿态测量惯性传感器的配置方案研究;建立了系统量测方程与基于"当前"统计模型的状态方程;提出了一种改进的自适应UKF非线性滤波算法,采用速度估计自适应方法实现了对过程噪声方差阵的自适应调整,提高了滤波精度。同时,采用最小偏度单形采样策略减少了Sigma点的数量,提高了滤波解算速度。三轴飞行转台仿真实验研究表明:该方法测量精度高,收敛速度快,具有很高的工程应用价值。     

三轴模拟转台的外框结构有限元优化设计

本文建立了某三轴模拟转台的外框结构分析模型和结构优化设计模型，利用基于性态模型的约束变尺度法对其进行了优化设计，得出了在一阶固有频率和静态变形约束下的最优方案。     

三轴液压仿真转台三轴相交度测试方法

根据三轴仿真转台自身的结构特点,针对决定其精度的重要环节——相交度的测试问题,提出了新的简洁的测试方法,从而为三轴仿真转台的安装、调试提供可靠数据,以保证其精度指标达到设计要求。     

一种新型高精度大功率电动飞行仿真转台的驱动方案设计

针对一种新型电动飞行仿真转台高精度、大功率的突出特点,设计了十分有效的驱动方案。首先简要介绍了该电动飞行仿真转台的主要技术指标,然后提出了高精度大功率飞行仿真转台驱动方案的详细设计策略,驱动方案中采用PWM功率放大器驱动,最后给出了该驱动方案在飞行仿真转台控制系统中的设计实现。实际应用表明:该驱动方案可以很好地满足新型电动飞行仿真转台的高精度、大功率要求,具有很强的抗干扰性和鲁棒性,完全适合在高精度大功率电动仿真转台上应用。     

三轴模拟台的交叉耦合及其控制方法的研究

本文主要从三轴转台的模型出发，研究了采用状态反馈的最优控制及状态观测器来提高控制系统的自适应能力，以解决三轴台特有的不确定非线性耦合问题和变结构解耦控制。     

基于四元数自适应卡尔曼滤波的快速对准算法

针对捷联惯导初始对准问题,提出了一种具有干扰抑制能力的四元数自适应卡尔曼滤波初始对准算法。通过将初始对准问题转化为Wahba姿态确定问题,直接建立四元数的滤波模型,并采用自适应卡尔曼滤波对初始时刻姿态四元数进行估计,利用姿态四元数更新求出当前姿态来实时地反映载体的姿态变化。针对直接构建量测模型导致收敛速度慢的问题,提出一种基于最优四元数估计法构造K矩阵原理的改进算法。利用三轴转台模拟不同的摇摆环境进行实验,转台实验表明了改进算法具有较快的收敛速度和良好的稳定性及精度,中等精度的惯导系统在150s至200s的对准时间内,航向角均值误差小于2'。     

三轴飞行模拟仿真转台的设计及控制问题研究

论述了三轴仿真转台的总体设计布局，驱动元件及控制方案等问题，分析了影响转台控制系统性能的因素并提出了相应的解决措施。最后，对转台最重要的指标之一低速问题进行了详细的探讨。     

基于结点应力误差估计的自适应网格划分

提出了基于结点应力误差估计对围绕该结点单元进行自适应网格划分的新方法。首先建立了基于2-范数的结点应力误差估计方法,然后在引入误差在各结点内等分布假定的基础上,给出了基于结点应力误差估计的自适应网格划分策略。最后结合工程实际中对高应力区结点应力的精度需要,给出了考虑结点应力大小的误差分配方案和相应的自适应网格划分方法,从而既对高应力区进行了网格优化,又兼顾了数学意义上误差较大的区域,更符合工程实际要求。算例计算结果表明,本文的方法是可行、有效的。     

Event-triggered robust adaptive control for uncertain nonlinear systems preceded by actuator dead-zone

It is both theoretically and practically important to investigate the problem of event-triggered adaptive tracking control for a class of uncertain nonlinear systems subject to actuator dead-zone, which aims at reducing communication rate and compensating actuator nonlinearity simultaneously. In this paper, to handle such a problem, an event-trigger based adaptive compensation scheme is proposed for the system preceded by actuator dead-zone. The challenges of this work can be roughly classified into two categories: how to compensate the nonsmooth dead-zone nonlinearity and how to eliminate the quantization signal effects caused by event-triggered strategy. To resolve the first challenge, a new decomposition of dead-zone mathematical model is employed so that dead-zone nonlinearity can be successively compensated by using robust approach. In addition, an adaptive controller and its triggering event are co-designed based on the relative threshold strategy, such that an asymptotic tracking performance can be ensured. The proposed scheme is proved to guarantee the globally bounded of all closed-loop signals and the asymptotic convergence performance of tracking error toward zero. The simulation results illustrate the effectiveness of our proposed control scheme.     

Assessment of adaptive and heuristic time stepping for variably saturated flow

The performance of improved initial estimates and ‘heuristic’ and ‘adaptive’ techniques for time step control in the iterative solution of Richards equation is evaluated. The so‐called heuristic technique uses the convergence behaviour of the iterative scheme to estimate the next time step whereas the adaptive technique regulates the time step on the basis of an approximation of the local time truncation error. The sample problems used to assess these various schemes are characterized by nonuniform (in time) boundary conditions, sharp gradients in the infiltration fronts, and discontinuous derivatives in the soil hydraulic properties. It is found that higher order initial solution estimates improve the convergence of the iterative scheme for both the heuristic and adaptive techniques, with greater overall performance gains for the heuristic scheme, as could be expected. It is also found that the heuristic technique outperforms the adaptive method under strongly nonlinear conditions. Previously reported observations suggesting that adaptive techniques perform best when accuracy requirements on the numerical solution are very stringent are confirmed. Overall both heuristic and adaptive techniques have their limitations, and a more general or mixed time stepping strategy combining truncation error and convergence criteria is recommended for complex problems. Copyright © 2006 John Wiley & Sons, Ltd.     

Adaptive Neural Network Control for a DC Motor System with Dead-Zone

In this paper, an adaptive control strategy for tracking of a direct-current (DC) motor system with a dead-zone is developed. The main contribution of the developed scheme is that we successfully integrate an asymmetric barrier Lyapunov function approach to relax the requirements on the initial conditions. The unknown functions in the DC system are approximated by using the radial basis function neural networks (RBFNN). It is shown that the DC motor can follow a selected trajectory and all the signals are guaranteed to be bounded. Simulation results are provided to confirm the effectiveness of the proposed control.     

Robust control of electrically driven robots by adaptive fuzzy estimation of uncertainty

This paper presents a novel robust decentralized control of electrically driven robot manipulators by adaptive fuzzy estimation and compensation of uncertainty. The proposed control employs voltage control strategy, which is simpler and more efficient than the conventional strategy, the so-called torque control strategy, due to being free from manipulator dynamics. It is verified that the proposed adaptive fuzzy system can model the uncertainty as a nonlinear function of the joint position error and its time derivative. The adaptive fuzzy system has an advantage that does not employ all system states to estimate the uncertainty. The stability analysis, performance evaluation, and simulation results are presented to verify the effectiveness of the method. A?comparison between the proposed Nonlinear Adaptive Fuzzy Control (NAFC) and a Robust Nonlinear Control (RNC) is presented. Both control approaches are robust with a very good tracking performance. The NAFC is superior to the RNC in the face of smooth uncertainty. In contrast, the RNC is superior to the NAFC in the face of sudden changes in uncertainty. The case study is an articulated manipulator driven by permanent magnet dc motors.     

Adaptive–adaptive robust boundary control for uncertain mechanical systems with inequality constraints

In this paper, adaptive–adaptive robust boundary control is proposed for uncertain mechanical systems with inequality constraints. First, inequality constraints are taken into consideration, which are derived from the system or environment constraints on state bounds and control input bounds. Moreover, the original system with inequality constraints is transformed into a novel system with merely equality constraints by constraint reorganization techniques. Second, an adaptive robust control with a two-layer adaptive law is initiated. Here, the lower-layer adaptive law is used to overcome the (possibly rapidly time-varying) system uncertainty, which is bounded but unknown. Additionally, the adaptive law design parameters are chosen online by the upper-layer adaptive law, rather than according to the empirically set fixed values. Finally, the performance of the controller with uniform boundedness and uniform ultimate boundedness is theoretically and experimentally verified. The control strategy allows the electric cylinder-driven pitch system to achieve highly accurate and error-controllable motion within the motor drive capability.

     

Pragmatical adaptive synchronization of different orders chaotic systems with all uncertain parameters via nonlinear control

A new adaptive synchronization scheme by pragmatical asymptotically stability theorem is proposed in this paper. Based on this theorem and nonlinear control theory, a new adaptive synchronization scheme to design controllers can be obtained and especially the constraints for minimum values of feedback gain K in controllers can be derived. This new strategy shows that the constraint values of feedback gain K are related to the error of unknown and estimated parameters if the goal system is given. Through this new strategy, an appropriate feedback gain K can be always decided easily to obtain controllers achieving adaptive synchronization. Two identical Lorenz systems with different initial conditions and two completely different nonlinear systems with different orders, augmented R?ssler??s system and Mathieu?Cvan der Pol system, are used for illustrations to demonstrate the efficiency and effectiveness of the new adaptive scheme in numerical simulation results.     

基于自适应多可信度多项式混沌-Kriging模型的高效气动优化方法

多可信度代理模型已经成为提高基于代理模型的优化算法效率和可信度水平最有效的手段之一.然而目前流行的co-Kriging和分层Kriging (HK)等多可信度代理模型泛化能力不足,缺乏对高阶/高非线性建模问题的适应性,难以广泛应用.文章基于发展的自适应多可信度多项式混沌-Kriging (MF-PCK)代理模型,在提高建模效率和对高阶/高非线性问题近似准确率的同时,建立了基于该自适应MF-PCK模型的高效全局气动优化方法.在发展的方法中,提出了基于MF-PCK模型的新型变可信度期望改进加点方法,使代理优化算法效率进一步提高.为了验证发展方法的全面表现,将其应用在经典的数值函数算例以及多个跨音速气动外形的确定性优化和稳健优化设计中,并与基于Kriging和HK模型的代理优化算法进行了全面比较.结果表明,发展的新型多可信度全局气动优化方法其优化效率相对于基于Kriging和HK模型的优化效率显著提高,结果更好也更加可靠,并且稳健优化设计效率和结果也更符合工程应用需求,证明了其相对于基于Kriging和HK模型的代理优化算法的显著优势.     

Adaptive inverse control of variable speed wind turbine

The efficiency and reliability of wind power has been shown to be depending on the applied control strategy of the wind turbine. In this paper, an adaptive control strategy is proposed for variable speed wind turbine (VSWT), producing energy limitation above rated wind speed. In the proposed control strategy, the process is modeled using a neural networks based identifier, providing the sensitivity information of the process to the control input. Another neural networks is employed as an inverse model controller established via inverse system method. These two neural networks are off-line learned firstly and are on-line updated using the back propagation algorithm. Simulation results have shown the effectiveness of the proposed adaptive control strategy.     

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.