混合式光纤陀螺惯导系统在线自主标定

混合式光纤陀螺惯导系统IMU的安装误差、光纤陀螺的漂移及标度因数等参数会随着时间发生变化,对系统误差产生影响,使系统在使用一段时间之后精度发生变化,因而需要重新标定。在混合式系统中,通过台体旋转调制,惯性元件常值漂移误差对系统的影响得到抑制,但安装误差和标度因数误差对系统的影响无法得到完全调制,这些误差会与地速及旋转角速率耦合,引起锯齿形速度误差,降低了系统的各项性能。针对混合式惯导系统,建立了IMU误差模型,设计出一种在线自主标定方法,并进行了可观性分析。该方法采用"速度+位置"匹配,对惯导系统30项相关误差项进行在线标定。系统实验结果表明,系统级在线标定参数较分立式标定参数在导航定位精度上提高了半个数量级。     

Kinematic design of 3-URU pure rotational parallel mechanism to perform precise motion within a large workspace

We present an optimum design of lower-dof parallel mechanism, a 3-URU pure rotational parallel mechanism that reflects issues of workspace and the position error of the center of rotation of the platform. The uncompensatable error determined by position error of center of rotation was used as an evaluation index for the design. The uncompensatable error index, an index used in the optimum design, was proposed taking into account four sources of errors, representing errors between adjacent joints. Based on the application of the mechanism and the error index, the effect of the redundant platform orientation parameter was numerically investigated and the design flow of the mechanism was proposed. We made a kinematic design of a mechanism with a large workspace subject to minimization of platform’s position error of the center of rotation. A prototype of mechanism with a large inclination angle of the platform up to 1.3 rad was shown, and its characteristics are also discussed.     

Nonlinear adaptive control for quadrotor flying vehicle

In this work, we deal with autonomous tracking and disturbance rejection problem of quadrotor vehicle flying in uncertain environment. The vehicles kinematic and modeling error uncertainties are associated with external disturbance, inertia, mass, and nonlinear aerodynamic forces and moments. The proposed method integrate the techniques from adaptive control and robust control theory. Robust and adaptive control algorithms for translational and orientation tracking are derived using Lyapunov method. It is shown in our analysis that the altitude, position, and attitude tracking errors are bounded and their bounds asymptotically converge to zero in Lyapunov sense. Simulation results on a commercial quadrotor flying vehicle are given to demonstrate the effectiveness of theoretical arguments for real world application.     

Neural network-based adaptive output feedback formation control for multi-agent systems

This paper investigates the problem of output feedback formation tracking control for second-order multi-agent systems under an undirected connected graph and in the presence of dynamic uncertainties and bounded external disturbances. Two state tracking error measures (i.e., absolute and relative state tracking errors) are considered for each individual agent in the formation, and linear reduced-order observers are constructed based on the lumped state tracking errors which include absolute and relative state tracking errors. Chebyshev neural networks are used to approximate unknown nonlinear function in the agent dynamics on-line, and the implementation of the basis functions of Chebyshev neural networks depends only on the desired signals. The smooth projection algorithm is applied to guarantee that the estimated parameters remain in some known bounded sets. Numerical simulations are presented to illustrate the performance of the proposed controller.     

Velocity and acceleration level inverse kinematic calculation alternatives for redundant manipulators

For both non-redundant and redundant systems, the inverse kinematics (IK) calculation is a fundamental step in the control algorithm of fully actuated serial manipulators. The tool-center-point (TCP) position is given and the joint coordinates are determined by the IK. Depending on the task, robotic manipulators can be kinematically redundant. That is when the desired task possesses lower dimensions than the degrees-of-freedom of a redundant manipulator. The IK calculation can be implemented numerically in several alternative ways not only in case of the redundant but also in the non-redundant case. We study the stability properties and the feasibility of a tracking error feedback and a direct tracking error elimination approach of the numerical implementation of IK calculation both on velocity and acceleration levels. The feedback approach expresses the joint position increment stepwise based on the local velocity or acceleration of the desired TCP trajectory and linear feedback terms. In the direct error elimination concept, the increment of the joint position is directly given by the approximate error between the desired and the realized TCP position, by assuming constant TCP velocity or acceleration. We investigate the possibility of the implementation of the direct method on acceleration level. The investigated IK methods are unified in a framework that utilizes the idea of the auxiliary input. Our closed form results and numerical case study examples show the stability properties, benefits and disadvantages of the assessed IK implementations.

     

Compliance characteristic and force control of antagonistic actuation by pneumatic artificial muscles

This paper presents compliance characteristics of antagonistic actuation by the pair of Mckibben pneumatic artificial muscles and force tracking using a sliding control scheme for safe human-robot interaction. The variable stiffness capability of artificial muscles was investigated carefully by resilience tests from a biased initial position and impact tests based on an intended collision between a stationary object and a rotating linkage actuated by pneumatic artificial muscles. Considering the frequency response analysis of a whole pneumatic circuit for artificial muscles, a sliding control system was designed to control contacting force between a linkage actuated by artificial muscles and a rigid environment. Experimental results showed that the proposed force control scheme gave better tracking performance under model uncertainties due to air flow than the conventional PID controller whose feedback gains were well-tuned experimentally.     

双轴旋转式SINS自主标定技术

在双轴旋转式SINS中,惯性元件常值漂移误差对系统的影响可以得到调制,但安装误差和标度因数误差对系统的影响无法得到调制,同时这些误差会与旋转角速率耦合,引起速度锯齿波等误差从而降低了系统的各项性能指标。为了减少这种影响,分析了光学陀螺双轴旋转式SINS误差传播特性,利用奇异值分解法对系统的可观测程度进行了分析,经分析,与转动轴相关的安装误差和标度因数误差的可观测度较好,据此设计了系统的自主标定方案及滤波算法,进行了数字仿真和半实物仿真验证试验。试验结果表明,利用设计的自主标定方案,在1 h内能估计出转轴上两个陀螺的标度因数误差及与转轴相关的四个安装误差,估计精度能达到95%以上。导航试验验证表明,利用自主标定的参数,相对于传统标定方法,使系统定位精度提高了20%。     

Nonlinear adaptive task space control for a 2-DOF redundantly actuated parallel manipulator

A nonlinear adaptive (NA) controller in the task space is developed for the trajectory tracking of a 2-DOF redundantly actuated parallel manipulator. The dynamic model with nonlinear friction is established in the task space for the parallel manipulator, and the linear parameterization expression of the dynamic model is formulated. Based on the dynamic model, a new control law including adaptive dynamics compensation, adaptive friction compensation and error elimination items is designed. After defining a quadratic performance index, the parameter update law is derived with the gradient descent algorithm. The stability of the parallel manipulator system is proved by the Lyapunov theorem, and the convergence of the tracking error and the error rate is proved by the Barbalat’s lemma. The NA controller is implemented in the trajectory tracking experiments of an actual 2-DOF redundantly actuated parallel manipulator, and the experiment results are compared with the APD controller.     

A Study of the Influence of Calibration Uncertainty on the Global Uncertainty for Digital Image Correlation Using a Monte Carlo Approach

Stereo digital image correlation (DIC) is now a standard measurement technique. It is, therefore, important to quantify the measurement uncertainties when using it for experiments. Because of the complexity of the DIC measurement process, a Monte Carlo approach is presented as a method to discover the magnitude of the stereo-DIC calibration uncertainty. Then, the calibration errors, along with an assumed sensor position error, are propagated through the stereo-triangulation process to find the uncertainty in three-dimensional position and object motion. Details on the statistical results of the calibration parameters are presented, with estimated errors for different calibration targets and calibration image quality. A sensitivity study was done to look at the influence of the different calibration error sources. Details on the best approach for propagating the errors from a statistical perspective are discussed, including the importance of using a “boot-strap” approach for error propagation because of the covariance of many of the calibration parameters. The calibration and error propagation results are then interpreted to provide some best-practices guidelines for DIC.     

A controller for 2-DOF underactuated mechanical systems with discontinuous friction

We propose a controller for a class of 2-DOF underactuated mechanical systems with discontinuous friction in the unactuated joint. The control objective is the regulation of the unactuated variable while the position and speed of the actuated joint remain bounded. The unactuated joint is considered as a mechanical system with discontinuous friction but continuous, artificial control input given by a term depending on the actuated positions and velocities. The proposed controller guarantees the convergence of the position error of the unactuated joint to zero, and it is robust with respect to some uncertainty in the discontinuous friction coefficients. We illustrate the technique with its application to two systems.     

Dynamics and optimal control of multibody systems using fractional generalized divide-and-conquer algorithm

In this paper, a new framework is presented for the dynamic modeling and control of fully actuated multibody systems with open and/or closed chains as well as disturbance in the position, velocity, acceleration, and control input of each joint. This approach benefits from the computed torque control method and embedded fractional algorithms to control the nonlinear behavior of a multibody system. The fractional Brunovsky canonical form of the tracking error is proposed for a generalized divide-and-conquer algorithm (GDCA) customized for having a shortened memory buffer and faster computational time. The suite of a GDCA is highly efficient. It lends itself easily to the parallel computing framework, that is used for the inverse and forward dynamic formulations. This technique can effectively address the issues corresponding to the inverse dynamics of fully actuated closed-chain systems. Eventually, a new stability criterion is proposed to obtain the optimal torque control using the new fractional Brunovsky canonical form. It is shown that fractional controllers can robustly stabilize the system dynamics with a smaller control effort and a better control performance compared to the traditional integer-order control laws.

     

基于方位旋转调制的平台式惯导系统“一点校”

为了提高惯导系统长时间导航精度,需要在导航阶段对系统进行综校。设计了一种基于方位旋转调制技术的平台式惯导系统一点校方案。方位旋转调制技术可以有效地调制水平惯性敏感元件误差,降低其对系统工作精度的不利影响,这为"一点校"方案的实施提供了前提。分析了方位旋转式平台惯导系统的误差模型,得到了系统误差与误差源之间的解析关系。通过分析研究系统的误差传播特性,建立了方位陀螺漂移与系统位置误差的数学模型,完成了方位旋转式平台惯导系统的"一点校"方案设计,通过系统试验验证其有效性,方位陀螺常值漂移为0.003(°)/h的条件下,经10 h一点校,40 h一点校后,72 h定位误差小于1nmile,航向误差小于1′。     

Adaptive robust control of Mecanum-wheeled mobile robot with uncertainties

This paper presents a novel implementation of an adaptive robust second-order sliding mode control (ARSSMC) on a mobile robot with four Mecanum wheels. Each wheel of the mobile robot is actuated by separate motors. It is the first time that higher-order sliding mode control method is implemented for the trajectory tracking control of Mecanum-wheeled mobile robot. Kinematic and dynamic modeling of the robot is done to derive an equation of motion in the presence of friction, external force disturbance, and uncertainties. In order to make the system robust, second-order sliding mode control law is derived. Further, adaptive laws are defined for adaptive estimation of switching gains. To check the tracking performance of the proposed controller, simulations are performed and comparisons of the obtained results are made with adaptive robust sliding mode control (ARSMC) and PID controller. In addition, a new and low-cost experimental approach is proposed to implement the proposed control law on a real robot. Experimental results prove that without compromising on the dynamics of the robot real-time implementation is possible in less computational time. The simulation and experimental results obtained confirms the superiority of ARSSMC over ARSMC and PID controller in terms of integral square error (ISE), integral absolute error (IAE), and integral time-weighted absolute error (ITAE), control energy and total variance (TV).     

利用ESO和TD进行的激光捷联惯组误差参数外场标定方法

对外场条件下激光捷联惯组9个误差参数的标定问题进行了研究,包括加速度计零偏、加速度计标度因数误差以及陀螺零偏。对外场静基座条件下9个误差参数的可观测性进行了分析,并且从理论上推导出在不需要其他外界基准信息的前提下,仅根据导航速度误差和位置误差来完成9个误差参数标定的最少位置数,给出了一种利用扩张状态观测器(ESO)和跟踪微分器(TD)提取导航速度误差的微分信息,从而快速估计惯组9个误差参数的算法。用一组可行的多位置编排进行了惯组的9个误差参数标定的仿真验证,结果表明,该算法简单,精度高,易于在外场实现。     

A new adaptive fuzzy sliding mode observer for a class of MIMO nonlinear systems

In this paper, a new adaptive fuzzy sliding mode (AFSM) observer is proposed which can be used for a class of MIMO nonlinear systems. In the proposed algorithm, the zero-input dynamics of the plant could be unknown. In this method, a fuzzy system is designed to estimate the nonlinear behavior of the observer. The output of fuzzy rules are tuned adaptively, based on the observer error. The output connection matrix is used to combine the observer errors of individual subsystems. A robust term, which is designed based on the sliding mode theory, is added to the observer to compensate the fuzzy estimation error. The estimation error bound is adjusted by an adaptive law. The main advantage of the proposed observer is that, unlike many of the previous works, the measured outputs is not limited to the first entries of a canonical-form state vector. The proposed observer estimates the closed-loop state tracking error asymptotically, provided that the output gain matrix includes Hurwitz coefficients. The chattering is eliminated by using boundary layers around the sliding surfaces and the observer convergence is proved using a Lyapunov-based approach. The proposed method is applied on a real multilink robot manipulator. The performance of the observer shows its effectiveness in the real world.     

存在关节力矩输出死区及外部干扰的漂浮基空间机械臂积分滑模神经网络自适应控制

探讨了载体位置和姿态都不受控时,漂浮基空间机械臂在带有关节力矩输出死区及外部干扰情况下轨迹跟踪的控制算法设计问题。死区与外部干扰影响系统的跟踪精度与稳定性。为此引入积分型切换函数,减少外部干扰引起的稳态误差,并利用径向基函数神经网络逼近动力学方程的未知部分,设计了一种积分滑模神经网络控制方案。控制算法的优点是,在死区斜率与边界参数不确定及最优逼近误差上确界未知的条件下,可以利用最优逼近误差、死区及干扰的补偿项来消除影响。李亚普诺夫稳定性分析证明了闭环系统的稳定性,且轨迹跟踪误差将收敛到0的某个小邻域内。仿真算例证实了该控制算法的有效性,实现了空间机械臂的轨迹跟踪控制。     

A particle swarm optimization approach for fuzzy sliding mode control for tracking the robot manipulator

In this paper, an optimal fuzzy sliding mode controller is used for tracking the position of robot manipulator, is presented. In the proposed control, initially by using inverse dynamic method, the known sections of a robot manipulator’s dynamic are eliminated. This elimination is done due to reduction over structured and unstructured uncertainties boundaries. In order to overcome against existing uncertainties for the tracking position of a robot manipulator, a classic sliding mode control is designed. The mathematical proof shows the closed-loop system in the presence of this controller has the global asymptotic stability. Then, by applying the rules that are obtained from the design of classic sliding mode control and TS fuzzy model, a fuzzy sliding mode control is designed that is free of undesirable phenomena of chattering. Eventually, by applying the PSO optimization algorithm, the existing membership functions are adjusted in the way that the error tracking robot manipulator position is converged toward zero. In order to illustrate the performance of the proposed controller, a two degree-of-freedom robot manipulator is used as the case study. The simulation results confirm desirable performance of optimal fuzzy sliding mode control.     

Nonlinear adaptive neural network control for a model-scaled unmanned helicopter

In this paper, a nonlinear adaptive neural network control is proposed for trajectory tracking of a model-scaled helicopter. The purpose of this research is to reduce the ultimate bounds of tracking errors resulted from small coupling forces (or small parasitic body forces) and aerodynamic uncertainties. The proposed control is designed under backstepping framework, with neural network compensators being added. Updating laws of neural networks are designed through projection algorithm, so that adaptive parameters are bounded. Derivatives of virtual controls are obtained through command filters. It is proved that, by using neural network compensators, tracking errors of the closed-loop system can be restricted within very small ultimate bounds. Superiority of the proposed nonlinear adaptive neural network control over a backstepping control is demonstrated by simulation results.     

Par2: a spatial mechanism for fast planar two-degree-of-freedom pick-and-place applications

This paper introduces a new two-degree-of-freedom (dof) parallel manipulator producing two translations in the vertical plane. One drawback of existing robots built to realize these dof is their lack of transversal stiffness, another one being their limited ability to provide very high acceleration. Indeed, these architectures cannot be lightweight and stiff at the same time. The proposed parallel architecture is a spatial mechanism which guarantees a good transversal stiffness. It is composed by two actuated kinematic chains, and two passive chains built in the transversal plane. The key feature of this robot comes from the two passive chains which are coupled to create a kinematic constraint: the platform stays in one plane. A stiffness analysis shows that the robot can be lighter and stiffer than a classical 2-dof mechanism. A prototype of this robot is presented and preliminary tests show that accelerations above 400 ms⁻¹ can be achieved while keeping a low tracking error.     

Rich dynamics caused by delay in a nonchaotic Rulkov map

This paper proposes a new nonlinear control scheme incorporating a state observer, a fuzzy neural network (FNN) and a new Nussbaum function for strict-feedback nonlinear systems by considering several challenges. These challenges are external disturbances, uncertain dynamics, unmeasured states, constrained input, unknown control direction, singularity issue, and actuator’s faults of different types. The scheme uses approximations of the unknown system’s dynamics provided by the FNN, the system’s states variables estimation provided by a model-free high-gain observer, and the control direction provided by the Nussbaum function. Compared to existing schemes, in addition to the fact that the new scheme can tackle simultaneously all the aforementioned challenges with better tracking performances, it also cancels the assumption about the positive definiteness of the control gain function found in many works. Thus, the scheme suites for more applications as it can be applied in cases where the control gain can be either semi-negative/negative-definite, semi-positive/positive-definite. Furthermore, the knowledge of the bounds for uncertain dynamics, actuation faults, FNN approximation errors and external disturbances is not required as it is for many other schemes. The effectiveness of the scheme is illustrated by its successful application to three examples, which are the pitch angle control for a Boeing 747-100/200 represented by the ultimate approximate nonlinear longitudinal model over up-and-away flight regime, the trajectory tracking control of a one-link manipulator actuated by a brush DC (BDC) motor, and the position tracking control for an inverted pendulum.