Adaptive robust control of fully-constrained cable driven parallel robots

In this paper, adaptive robust control (ARC) of fully-constrained cable driven parallel robots is studied in detail. Since kinematic and dynamic models of the robot are partly structurally unknown in practice, in this paper an adaptive robust sliding mode controller is proposed based on the adaptation of the upper bound of the uncertainties. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The proposed controller not only keeps all cables under tension for the whole workspace of the robot, it is chattering-free, computationally simple and it does not require measurement of the end-effector acceleration. The stability of the closed-loop system with proposed control algorithm is analyzed through Lyapunov second method and it is shown that the tracking error will remain uniformly ultimately bounded (UUB). Finally, the effectiveness of the proposed control algorithm is examined through some experiments on a planar cable driven parallel robot and it is shown that the proposed controller is able to provide suitable tracking performance in practice.     

Probabilistic seismic damage assessment of reinforced concrete buildings considering directionality effects

Most of buildings and structures are usually projected according to two main axes. However, the geographical position of these buildings varies randomly. Such random distributions of the azimuthal positions of structures, in most of the cities, generally, are not accounted for when assessing their seismic risk; certainly, the direction of the seismic loads is another highly random variable. Moreover, an additional important source of uncertainty is related to the structural response, mainly due to the random character of the mechanical properties. There is a consensus that uncertainties must be considered for adequately assessing the seismic risk of structures, but these directionality effects have not been deeply explored so far. In this article, the influence of the high uncertainty involved in these input variables on the expected seismic damage is analysed. Thus, an actual earthquake, which affected the southern part of Spain, is studied. Notably, damages on a group of affected buildings, located close to the epicentre, are analysed and discussed in detail. The results show that the influence of the random azimuthal position of structures is an important source of uncertainty and that it should be taken into account when estimating the expected seismic risk in urban areas.     

非匹配条件不确定非线性系统的变结构控制

研究不确定非线性系统的输出跟踪问题。针对系统中的未建模动态和广义的不确定性,基于李亚普诺夫稳定性理论提出了一种变结构控制方案,它不要求系统的不确定性满足匹配条件,仅要求已知不确定性的有界约束。从理论上证明了闭环跟踪系统全局渐近稳定,系统信号保持有界。仿真实例证实了理论结果。     

不确定性高通量筛选系统的双子代数控制

近年来,对于高通量筛选系统的控制,在确定性情形下已进行了不少工作,其中,双子代数框架下的控制策略执行相对更灵活,效率更高.然而,现有的输出反馈优化控制对性能指标期望系统行为还有特殊的构造要求须满足;而且,实际运行中存在有人工干预、设备维护、意外干扰、故障等带来的不确定性.对这两者考虑的缺失限制了目前高通量筛选系统自动控制的效率.为进一步提高这类新兴离散事件系统的控制效率,基于区间双子代数,将该输出反馈控制结构拓展到系统参数不确定的高通量筛选系统,使之对不确定性高通量筛选系统能自动产生优化控制.继而,将输出反馈与预处理补偿相结合.后者对指标的构造并无特殊要求.综合后的控制结构亦避免了原反馈结构下对性能指标构造的限制.最后通过不同实例说明了对不确定性高通量筛选系统应用该控制结构的方法和有效性.     

Fractional-order adaptive backstepping control of a class of uncertain systems with external disturbances

Fractional control schemes are powerful tools for fulfilling robust tracking performance of different systems. This paper is the pioneering one in developing a fractional-order adaptive backstepping controller (FOABC) for a general class of integer-order and fractional-order (FO) systems. Model uncertainties and external disturbances can perturb system response and the controller is designed such that it can suppress the performance degradation caused by these factors. Moreover, rigorous mathematical analyses are carried out based on fractional Lyapunov theorems to ensure stability of the controlled systems. To justify the claims, worked-out examples including integer-order and FO systems are simulated. Good tracking performance of the proposed controller as well as robustness against uncertainties and insensitivity to external disturbances make it a good candidate for a broad range of systems. The results of implementing the proposed controller on different systems are compared with some newly proposed control approaches which highlight the outperformance of the FOABC.     

基于径向基函数神经网络的移动机器人多变量固定时间编队控制

针对带多不确定性的一组非完整移动机器人的编队控制收敛问题,提出了基于径向基函数神经网络的移动机器人多变量固定时间领航者-跟随者编队控制算法.RBFNN补偿了系统所受的多不确定性,并消除了鲁棒控制的抖振现象.基于固定时间理论和Lyapunov方法进行了控制算法设计,使所提出的控制方法保证了编队控制系统中的所有信号全局固定时间收敛,在任意系统初始条件下,在通过参数设计的固定时间内,使机器人编队达到期望编队.仿真结果显示了所提出算法的有效性.     

基于互补滑模控制和迭代学习控制的永磁直线同步电动机速度控制

为满足永磁直线同步电动机(PMLSM)伺服系统高速度高精度的要求,抑制不确定性对系统性能的影响,提出一种互补滑模控制(CSMC)和迭代学习控制(ILC)相结合的控制方法.该方法结合了CSMC强鲁棒性的优点和ILC跟踪精度高的特点,以CSMC中积分滑模面为基础设计新型迭代学习律,既可利用ILC对系统未建模动态进行估计,抑制端部效应、齿槽效应和摩擦力等周期不确定性的影响,又可利用CSMC减小参数变化和外部扰动等非周期不确定性对系统的影响,从而提高控制器的收敛速度和收敛精度,保证系统具有较强的速度跟踪性能.实验结果表明,该方法有效地提高了系统的动态响应能力,改善了速度跟踪精度.     

Robust vertex‐dependent H∞ and H2 estimation for stochastic linear systems

We consider linear continuous‐time systems with multiplicative noise and polytopic‐type parameter uncertainty, and we address the problems of H_∞ and H₂ filtering of these systems. These problems are solved by applying a vertex‐dependent Lyapunov function that considerably reduces the overdesign associated with the classical design that is based on a single Lyapunov function for the whole parameter range. A new approach of the Finsler lemma is used that decreases the overdesign entailed in the usual derivation of the robust estimation problem. The developed theory is also extended to the robust gain scheduling case where online measurement is used to improve the estimation. Two examples are given that demonstrate the tractability and applicability of the design methods.     

Modeling and robust adaptive iterative learning control of a vehicle‐based flexible manipulator with uncertainties

In this brief, this paper deals with a robust adaptive iterative learning control (ILC) problem for a flexible manipulator attached to a moving vehicle with uncertainties. To begin with, considering the infinite dimensionality of the flexible distributed parameter system, a coupled ordinary differential equation and partial differential equation model is established without any discretization. Then, it is followed by a presentation of an adaptive ILC strategy, which can drive the vehicle and joint to the desired positions based on a proportional‐derivative feedback structure with unmodeled dynamics and external disturbances. The deformation of the flexible manipulator can also be suppressed simultaneously under the proposed control laws. By using Lyapunov's direct method, the stability of the closed‐loop system is demonstrated. The simulation results are provided to illustrate the effectiveness of the proposed control laws.     

Adaptive architectures for control of uncertain dynamical systems with actuator and unmodeled dynamics

In adaptive control of uncertain dynamical systems, it is well known that the presence of actuator and/or unmodeled dynamics in feedback loops can yield to unstable closed‐loop system trajectories. Motivated by this standpoint, this paper focuses on the analysis and synthesis of multiple adaptive architectures for control of uncertain dynamical systems with both actuator and unmodeled dynamics. Specifically, we first analyze model reference adaptive control architectures with standard, hedging‐based, and expanded reference models for this class of uncertain dynamical systems and develop sufficient stability conditions. We then synthesize a robustifying term for the latter architecture and analytically show that this term can allow for a relaxed sufficient stability condition. The proposed theoretical treatments involve Lyapunov stability theory, linear matrix inequalities, and matrix mathematics. Finally, we compare the resulting sufficient stability conditions of the considered adaptive control architectures on a benchmark mechanical system subject to actuator and unmodeled dynamics.