3-DOF并联拟人肩鲁棒自适应控制

针对一类3-DOF球面并联拟人肩的轨迹跟踪问题,提出一种鲁棒自适应算法.考虑系统中存在的参数不确定性和外部扰动,利用该模型的参数性质,由集中不确定性描述此机构的参数不确定性和外部扰动,并证明了集中不确定性范数有界.该控制器通过自适应算法在线学习集中不确定性的上界参数,进而实时补偿了由参数不确定性及干扰引起的误差,避免了参数过于保守而导致的控制器饱和,保证了系统的全局指数稳定.严格的证明和仿真结果验证了此算法的有效性.     

机械臂自适应反演超螺旋全局终端滑模控制

针对机械臂系统存在的系统参数摄动、非线性摩擦及外部干扰等不确定问题,提出一种自适应反演超螺旋全局终端滑模轨迹跟踪控制方法。该方法基于反演法、Lyapunov理论和全局快速终端滑模理论设计控制器,保证系统稳定性及全局收敛性,增强系统的鲁棒性。为解决系统集总扰动上界未知的问题,采用自适应技术设计切换控制律,抵消不确定性的影响,同时引入超螺旋算法抑制滑模控制固有的抖振现象。最后,通过理论分析和仿真算例验证了该控制器的有效性与可行性。     

基于自适应滑模的不确定Euler-Lagrange多智能体系统抗扰动蜂拥控制

针对具有参数不确定性和未知外部扰动的Euler-Lagrange多智能体系统,设计一种基于自适应滑模控制的分布式蜂拥算法.该算法使用自适应滑模控制和自适应控制律分别补偿未知的外部扰动与模型中可线性参数化回归的不确定项,从而在实现蜂拥控制的同时,避免智能体对外部扰动先验知识的要求.理论分析表明,在多智能体达成蜂拥的同时,算法保证滑模的自适应增益有界.此外,所提出的算法同时考虑虚拟领导者追踪与基于目标区域的跟踪问题,并给出碰撞避免的条件.最后,通过算例仿真验证所提出算法的有效性.     

一类具有未知死区MIMO系统的自适应模糊控制

针对一类具有未知死区并具有下三角函数控制增益矩阵的不确定MIMO非线性系统, 根据滑模控制原理, 并利用Nussbaum函数的性质, 提出了一种自适应模糊控制器的设计方案. 该方案取消了函数控制增益符号已知和死区模型参数上界、下界已知的条件. 通过引入积分型李亚普诺夫函数及最优逼近误差与死区扰动上界的自适应补偿项，证明了闭环系统是稳定的，跟踪误差收敛到零. 仿真结果表明了该方法的有效性.     

运输机低空重装空投增益自适应滑模控制

本文针对带有不确定性且不确定性边界未知的低空重装备空投过程控制问题,提出了基于增益自适应全局滑模的飞行控制方法.该方法采用反馈线性化技术对重装空投过程模型进行线性化,解决了空投模型的强非线性问题,在此基础上,设计了切换增益自适应全局滑模控制器,保证了系统在响应全程的鲁棒性,克服了滑模到达阶段系统初始误差对切换增益自适应过程的影响.提出了一种改进的增益自适应方法,解决了滑动阶段的切换增益过度自适应问题.基于Lyapunov理论证明了控制器的稳定性和鲁棒性.仿真验证了控制方法的控制性能和优越性.     

基于高阶滑模观测器的自适应时变滑模再入姿态控制

针对再入飞行器鲁棒姿态控制问题, 提出一种基于高阶滑模观测器的自适应时变滑模控制器设计方法. 首先, 设计了一种时变滑模面, 并在此基础上推导了相应的时变滑模控制律, 其中滑模控制中切换增益通过一种自适应算法获得, 消除了控制器设计过程中对系统不确定性上界已知的要求; 然后, 利用高阶滑模观测器对控制器设计过程中用到的姿态角导数信息进行观测, 同时能够获得系统扰动估计值, 从而构造一种基于观测器的控制器形式; 最后, 通过仿真验证了所提出的控制算法在提高再入飞行器姿态控制精度以及系统鲁棒性方面的有效性.     

基于上界估计的变参数Lorenz系统模糊滑模控制

传统滑模控制设计方法根据系统不确定性上界函数得到的切换增益,不能随着受控状态的变化而相应变化,存在滑模控制量反应不够及时的问题,并且需要预先知道系统不确定性的上界函数,对滑模控制应用到混沌系统控制当中造成了限制.针对变参数的Lorenz混沌系统提出了一种模糊滑模控制方法,控制系统到达平衡状态.模糊控制器的输入为滑模面S,通过设计合理的输入输出隶属度函数以及模糊规则,输出的μ值能在线调节滑模控制切换量中的切换增益,有效地解决了滑模控制量反应不够及时的问题;另外设计了状态观测器,对系统的不确定性函数进行了估计.仿真实验结果验证了所提出的模糊滑模控制有着良好的控制效果.     

基于神经网络的不确定机器人自适应滑模控制

提出一种机器人轨迹跟踪的自适应神经滑模控制。该控制方案将神经网络的非线性映射能力与变结构控制理论相结合,利用RBF网络自适应学习系统不确定性的未知上界,神经网络的输出用于自适应修正控制律的切换增益。这种新型控制器能保证机械手位置和速度跟踪误差渐近收敛于零。仿真结果表明了该方案的有效性。     

动力定位船舶自适应滑模无源观测器设计

针对带有模型参数不确定性的动力定位船舶,提出一种动力定位船全速域自适应滑模无源观测器,解决了现有观测器只能应用于低速作业动力定位系统的问题.采用速度估计误差作为滑模面,设计切换自适应律估计模型不确定项上界,保证了观测器增益的有界性和系统鲁棒性.对速度估计回路的无源性进行了分析,并证明了观测器的稳定性.最后利用船舶动力定位系统半实物仿真平台,验证了算法的有效性.     

鲁棒自适应滑模虚拟执行器设计

为了解决具有不确定性的非线性系统发生执行器故障后的安全运行问题,针对未建模动态、外界扰动和参数不确定等各种不确定性的综合影响,提出了一种鲁棒自适应滑模虚拟执行器控制重构策略.该方法利用滑模控制具有鲁棒性的特点消除不确定性的影响,采用参数自适应方法使得无须己知不确定项的上界,同时具有虚拟执行器不改变标称控制器结构和参数的特点.采用李雅普诺夫稳定性理论证明了闭环系统的稳定性和鲁棒性.通过动力定位船仿真验证了该方法的有效性.     

On adaptive sliding mode control without switching gain overestimation

This paper presents some further results on adaptive sliding mode control (ASMC) for a class of nonlinear systems with bounded uncertain parameters. Given a large initial tracking error, current ASMC design generally produces an unnecessarily large switching gain, consequently leading to a serious chattering problem or a large‐amplitude control jump for the continuous counterpart. To solve such an overadaptation problem, the switching gain adaptation mechanism is first analyzed in this paper, and the adaptation induced by the initial tracking error is suggested to be removed. Then, by exploiting the global sliding mode feature of time‐varying sliding mode control and integral sliding mode control, we present two effective methodologies for ASMC design. The proposed ASMC algorithms ensure that there is no overestimation of the switching gain and the system response is not slowed down when a small switching gain is generated. The validity of the proposed methods is verified by both theoretical analysis and simulation results. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive sliding‐mode control for Mars entry trajectory tracking with finite‐time convergence

This paper presents a novel adaptive sliding‐mode control (ASMC) method for Mars entry guidance and the finite‐time convergence in the presence of large uncertainties can be guaranteed. With the help of gain adaptive law, the nonoverestimating value of control gains can be achieved, and then, the chattering can be attenuated by the proposed ASMC method. Meanwhile, the extended state observer is introduced to estimate and compensate for uncertainties and the nonoverestimating problem is resolved further. In addition, the proposed method does not require any knowledge on the upper bound of uncertainty, which yields to be used in practical systems. Finally, the numerical simulation results are given to demonstrate the effectiveness of the proposed guidance law.     

Adaptive fuzzy-neural-network control for maglev transportation system

A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.     

Self-triggered linear quadratic control

Self-triggered control is a recently proposed paradigm that abandons the more traditional periodic time-triggered execution of control tasks with the objective of reducing the utilization of communication resources, while still guaranteeing desirable closed-loop behavior. In this paper, we introduce a self-triggered strategy based on performance levels described by a quadratic discounted cost. The classical LQR problem can be recovered as an important special case of the proposed self-triggered strategy. The self-triggered strategy proposed in this paper possesses three important features. Firstly, the control laws and triggering mechanisms are synthesized so that a priori chosen performance levels are guaranteed by design. Secondly, they realize significant reductions in the usage of communication resources. Thirdly, we address the co-design problem of jointly designing the feedback law and the triggering condition. By means of a numerical example, we show the effectiveness of the presented strategy. In particular, for the self-triggered LQR strategy, we show quantitatively that the proposed scheme can outperform conventional periodic time-triggered solutions.     

Stochastic optimal control of networked control systems with control packet dropouts

This paper considers the stochastic optimal control problem for networked control systems(NCSs)with control packet dropouts.The proportional plus up to the third-order derivative(PD3)compensation strategy is adopted to compensate for control packet dropouts at the actuator by using the past control packets stored in the buffer.Based on the strategy,a new NCS structure model with packet dropouts is provided,where the packet dropout is assumed to obey the Bernoulli random binary distribution.In terms of the given model,the stochastic optimal control law is proposed. Numerical examples illustrate the effectiveness of the results.     

基于控制周期计算的锌液净化除铜过程优化控制

净化除铜过程是锌直接浸出冶炼工艺中硫酸锌溶液净化的第1个步骤,其效果直接影响着后续工序的正常进行以及最终锌产品的质量.本文针对除铜过程中控制周期与锌粉添加量不确定性,造成出口铜离子浓度波动大及锌粉浪费等问题,研究基于控制周期的除铜过程锌粉添加优化控制方法.首先通过分析反应器中氧化还原电位(oxidation-reduction-potential,ORP)与锌粉的时序变化关系计算除铜过程反应响应时间,然后分析响应时间的统计特性,从而确定锌粉控制周期.在确定控制周期的基础上,采用基于控制周期的固定节点控制参数化方法,从而将最优控制求解问题转化为非线性规划问题.最后,采用状态转移算法对该非线性规划问题进行求解.采集工业现场数据进行实验证明了该优化控制方法的有效性,为类似的净化过程的优化控制提供了新思路.     

非线性动态系统神经模糊建模与内模/PID双重控制系统设计

非线性动态系统的内模控制要求建立精确的对象正模型和逆模型,这对于大多数实际对象是难以做到.提出了基于一类神经模糊模型的非线性动态系统建模方法,并在此基础上研究了基于神经模糊模型的非线性系统的内模控制设计.基于输入输出数据辨识的对象正模型和逆模型存在着模型失配问题,导致神经模糊内模控制范围变窄和控制鲁棒性降低,为了改善系统的性能,提出了神经模糊内模控制与PID控制结合的双重控制策略.对CSTR的反应物浓度控制研究表明,双重控制策略能有效地拓宽系统可控范围,改善系统性能.仿真结果证明该控制策略简单而有效.     

Adaptive sliding mode control – convergence and gain boundedness revisited

This paper reviews the main adaptive sliding mode controller (ASMC) designs for nonlinear systems with finite uncertainties of unknown bounds. Different statements of convergence referring to uniformly ultimate boundedness (UUB), asymptotic convergence (AC) and finite-time convergence (FTC) for ASMC shown in recent papers are analysed. Weaknesses and incomplete proofs apropos FTC are pointed out. Thereafter, a new approach is proposed to successfully demonstrate FTC of the so-called sliding variable. We identify a compensating phase and a reaching phase during the ASMC process. A new explicit form for estimating the upper-bound reaching time is provided for any bounded perturbation. An amended form of the real ASMC is recalled showing improved accuracy and chattering reduction. Finally, numerical and experimental applications are performed to convey the discussed results.     

网络控制系统中的时戳预测函数控制

传统预测函数控制算法不能有效处理网络控制系统中的随机延迟,为此提出了时戳预测函数控制算法.该算法使用时间戳来估计由网络引入的控制延迟,在预测系统未来输出时明确考虑了该延迟,然后借鉴传统预测函数控制算法的基本策略,得到了适用于网络控制系统的控制规律.进一步由TrueT im e工具箱搭建了网络控制系统仿真平台,对比了时戳预测函数控制算法和传统预测函数控制算法.仿真结果表明随着网络引入延迟的增大,时戳预测函数控制算法给出控制品质明显优越于传统预测函数控制算法.     

Optimal periodic output feedback control: a continuous-time approach and a case study

This article deals with the problem of optimal static output feedback control of linear periodic systems in continuous time, for which a continuous-time approach, which allows to deal with both stable and unstable open loop systems, is presented. The proposed approach is tested on the problem of designing attitude control laws for a Low-Earth Orbit (LEO) satellite on the basis of feedback from a triaxial magnetometer and a set of high-precision gyros. Simulation results are used to demonstrate the feasibility of the proposed strategy and to evaluate its performance.