Decoupling control based on terminal sliding mode and wavelet network for the speed and tension system of reversible cold strip rolling mill

To weaken the nonlinear coupling influences among the variables in the speed and tension system of reversible cold strip rolling mill, a novel dynamic decoupling control strategy is proposed based on nonsingular fast terminal sliding mode (NFTSM) and wavelet neural network (WNN). First, nonlinear disturbance observers are developed to counteract the mismatched uncertainties, and then input/output dynamic decoupling and linearisation for the speed and tension nonlinear coupling system are realised by utilising the inverse system theory. Second, nonsingular fast terminal sliding mode controller (NFTSMC) for each pseudo linear subsystem is presented based on backstepping and two-power reaching law, so as to improve the global convergence speed and robust stability of the system. Third, adaptive WNNs are used to approximate the uncertain items of the system, so as to improve the control precision of the speed and tension of reversible cold strip rolling mill. Theoretical analyses show that the NFTSMs satisfy reachability condition, the system error variables can converge to equilibrium point in finite time, and the resulting closed-loop system is globally asymptotically stable. Finally, simulation research is carried out on the speed and tension system of a 1422 mm reversible cold strip rolling mill by using the actual data, and results show the superiority of the proposed control strategy in comparison with the strategies of cascade PI, linear sliding mode control and internal model control.     

可逆冷带轧机速度张力系统的耗散Hamilton 控制

研究基于侵入与不变流形(I&I)自适应方法和非线性干扰观测器(NDO)的可逆冷带轧机速度张力系统耗散Hamilton控制问题。首先采用I&I自适应方法估计系统的摄动参数;其次,通过预反馈建立系统速度张力外环的耗散Hamilton模型,并利用互联和阻尼配置以及能量整形方法设计耗散Hamilton控制器;再次,选用NDO对系统电流内环的外扰进行观测,并引入设计的积分滑模控制器中进行补偿;最后将该方法应用于某1422 mm可逆冷带轧机速度张力系统中进行仿真,结果验证了所提出方法的有效性。     

基于Hamilton理论的可逆冷带轧机速度张力系统无张力计控制

基于反馈耗散Hamilton理论研究了可逆冷带轧机速度张力系统的无张力计控制问题. 首先,对系统速度张力外环(主轧机速度环和左、右卷取机张力控制环)进行预反馈控制, 并采用反馈耗散Hamilton理论完成了速度张力外环控制器的设计. 其次, 为了实现系统的无张力计控制及对摄动参数的自适应估计, 基于"扩张系统+反馈"方法完成了系统速度张力外环自适应状态观测器的设计. 再次, 为了实现可逆冷带轧机主轧机速度和左、右卷取机张力间的协调控制及对外扰不确定项的干扰抑制, 基于backstepping方法完成了系统电流内环鲁棒控制器的设计. 理论分析表明, 所提出的控制方法能够保证闭环系统的鲁棒稳定性. 最后, 基于某1422mm可逆冷带轧机速度张力系统的实际数据进行仿真, 并同串级PI控制方法相比较, 结果验证了本文所提方法的有效性.     

可逆冷带轧机速度张力多变量耦合系统的建模及分散控制

针对具有多变量、非线性、强耦合和不确定性的可逆冷带轧机速度张力系统,提出了一种基于扩张状态观测器(extended state observer,ESO)的全局积分滑模自适应反步分散控制方法.首先,采用机理建模方法,建立了相对完备的可逆冷带轧机速度张力多变量耦合系统的数学模型.其次,将各子系统的耦合项和不确定项看成外扰,通过构造的ESO对其进行动态观测,并分别引入所设计的全局积分滑模自适应反步控制器中进行补偿,速度张力系统实现了有效的动态解耦和协调控制.理论分析表明,所提出的控制方法能够保证滑模面的渐近稳定和闭环系统的渐近跟踪性能.最后,基于某1422mm可逆冷带轧机速度张力系统的实际数据进行仿真,结果验证了所提方法的有效性.     

基于Hamilton理论的冷带轧机速度张力系统非奇异 快速终端滑模控制

针对交流异步电机驱动的冷带轧机速度张力系统的跟踪控制问题,给出一种基于Hamilton理论的非奇异快速终端滑模控制器设计方法.首先,设计了一种新型扰动观测器对系统中由参数摄动和负载扰动引起的不确定项进行观测;其次,通过预反馈控制建立了冷带轧机系统速度张力磁链外环的耗散Hamilton模型,进而基于互联–阻尼配置及能量整形方法完成耗散Hamilton控制器的设计;再次,基于串级控制思想完成了冷带轧机系统电流内环非奇异快速终端滑模控制器的设计.通过理论分析证明了所提控制方法能够保证闭环系统全局稳定.最后,基于某交流异步电机驱动的冷带轧机系统的现场实际数据进行仿真对比研究,仿真结果验证了本文所提方法的有效性.     

可逆冷带轧机速度张力系统的动态滑模解耦控制

为削弱可逆冷带轧机速度张力系统中各变量间的非线性耦合影响,本文提出了一种基于幂指数趋近律的微分几何动态滑模解耦控制方法.首先,应用微分几何理论,通过非线性状态反馈和坐标变换,实现了可逆冷带轧机速度张力非线性耦合系统的输入/输出动态解耦和线性化.其次,针对解耦后得到的各独立线性子系统,综合考虑可逆冷带轧机速度张力系统的负载扰动、参数摄动和未建模动态等不确定部分的影响,基于幂指数趋近律设计了动态滑模控制器.理论分析表明,所提出的控制方法能够保证闭环系统渐近稳定,并能有效削弱系统抖振.最后,对某1422mm可逆冷带轧机速度张力非线性耦合系统进行仿真,并同其他解耦控制方法相比较,结果验证了所提出方法的有效性.     

轧机两侧液压伺服位置系统自抗扰同步控制

针对轧机传动侧和操作侧液压伺服位置系统存在不一致性而引起两侧位置不同步的问题, 提出一种自抗扰同步控制方法.首先建立了液压伺服位置同步系统动态机理模型, 并在考虑两侧位置伺服系统都具有参数摄动及外负载波动的情况下, 设计了扩张状态观测器对同步系统中不确定性和不一致性进行估计, 并采用状态误差反馈律给予主动补偿, 同时消除同步误差. 仿真和实验结果表明, 所提出的同步控制方法能够使两侧液压伺服位置系统动态响应和稳态特性保持一致, 并提高了单侧子系统的动态性能及抗干扰能力.     

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.     

基于非奇异快速终端滑模的轧机液压伺服位置系统反步控制

针对具有非线性、参数不确定性和未知负载扰动的非对称缸轧机液压伺服位置系统,提出一种基于模糊自适应观测器和非奇异快速终端滑模面的反步控制方法.首先,基于非奇异快速终端滑模面和双幂次趋近律完成非对称缸轧机液压伺服位置系统反步控制器的设计,并通过构造二阶滑模滤波器对虚拟控制量的微分信号进行估计,有效地避免了反步控制中的微分爆炸现象;然后,选用模糊自适应观测器对系统的不确定项进行逼近估计,并将输出的估计值引入到设计的控制器中进行补偿,有效地提高了系统的跟踪控制精度,且分析表明,所提出的控制方法能够保证闭环系统全局渐近稳定;最后,基于某650mm可逆冷带轧机液压伺服位置系统的实际参数进行仿真研究,并与常规线性滑模控制方法相比较,结果验证了所提出方法能够有效提高系统在整个全局过程的收敛速度和鲁棒稳定性.     

基于神经网络的带钢跑偏电液伺服系统研究

带钢跑偏电液伺服控制系统的非线性和时变性使得传统的PID控制很难达到理想的控制效果,将神经网络与普通PID控制相结合形成神经网络自适应PID控制策略,应用于该系统实现其良好控制.为提高系统的动态响应速度及性能,采用RBF神经网络对系统进行辨识预测.首先建立带钢跑偏电液伺服系统数学模型,然后利用AMESim和Simulink软件对传统PID控制和神经网络自适应PID控制进行联合仿真.结果表明,神经网络自适应PID控制系统响应速度快、超调量小、鲁棒性强,并具有良好的稳定性和控制精度.     

Designing of a micromachined gyroscope system with a closed-loop DC biased interface ASIC

In this paper, a micromachined gyroscope system composed of a vibratory gyroscope with its in- terface ASIC is presented. The system adopts a DC sensing method to detect the capacitive motion, which is insensitive to the mismatch of the gyroscope capacitors and can eliminate high frequency signals from the chip. Therefore it offers a commendable noise performance with simplified topology. Low noise design can be achieved by a continuous-time charge sensitive amplifier with the input-referred noise voltage of 9.833 nV/rtHz at 10 kHz. A novel high voltage （HV） buffer is adopted in the drive mode to strengthen its drive signal, so that the common-mode voltage of it is made at 5 V that is compatible with the gyroscope. The HV buffer utilizes two sets of power supply to achieve both good noise performance and HV output. The ASIC chip is fabricated in the 0.35 μm 2P4M BCD HV process, and is 2.5×2.0 mm2 in dimension. The test results prove that the system achieves a stable closed-loop oscillation in the drive mode. Furthermore, the in-phase demodulation result of the gyroscope system achieves a nonlinearity of 0.14% within the sense range of 0° 500°/s.     

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances.      

Robust adaptive NN-based output feedback control for a dynamic positioning ship using DSC approach

A robust adaptive NN-based output feedback control scheme is presented for a dynamic positioning ship with uncertainties and unknown external disturbances. We tackle the problem that velocity vector of a ship is not available by employing a high-gain observer, and develop the proposed control approach by combing vectorial backstepping with dynamic surface control approach, which is simpler and easier to implement in engi- neering practice. The neural network （NN） approximation technique is used to compensate for the uncertainties and unknown external disturbances, and it removes the requirement for the prior knowledge about the vessel parameters and external disturbances. Also, it is demonstrated that the proposed control strategy can force the position and yaw angle of a dynamic positioning ship to approach the desired point while guaranteeing all singles of the designed closed-loop dynamic positioning system semi-globally uniformly ultimately bounded by means of the Lyapunov function. Simulation results of a supply ship illustrate the effectiveness of the proposed scheme.     

Decentralized adaptive neural network control of cascaded DC–DC converters with high voltage conversion ratio

Decentralized output voltage tracking of cascaded DC–DC converters is an interesting topic to obtain a high voltage conversion ratio. The control purpose is challenging due to the load resistance changes, renewable energy supply voltage variations and interaction of the individual converters. In this paper, four novel decentralized adaptive neural network controllers are designed on the cascaded DC–DC buck and boost converters under load and DC supply voltage uncertainties. In the beginning, individual buck and boost converter average models that can operate in both continuous and discontinuous conduction modes are derived. Then, the interconnected and decentralized state-space models of cascaded buck and boost converters are extracted. These models are highly nonlinear with unknown uncertainties which can be estimated by neural networks. Further, two decentralized adaptive backstepping neural network voltage controllers are proposed on cascaded buck converters to deal with uncertainties and interactions. However, these control strategies are not applicable to a boost converter due to its non-minimum phase nature. Then, two novel decentralized adaptive neural network with a conventional proportional–integral reference current generator are developed on the cascaded boost converters. Practical stability of the overall system is guaranteed for the proposed controllers using Lyapunov stability theorem. Finally, four control strategies provide good quality of output voltage in the presence of uncertainties and interactions. Comparative simulations are carried out on cascaded buck and boost converters to validate the effectiveness and performance of the designed methods.     

低轨无拖曳卫星的自适应神经网络控制器设计

低轨无拖曳(Drag-free)卫星为相对论的验证、引力波探测以及地球重力场的测量提供了低干扰的试验环境。目前已有的工作主要对无拖曳卫星模型进行线性化,然后进行控制器设计,此种方法忽略了无拖曳卫星控制系统的非线性环节,因此降低了控制器的精度。本文将基于Lyapunov稳定性理论和自适应反步控制,直接针对无拖曳卫星控制系统的非线性模型进行分析,设计一种自适应神经网络控制器。针对系统建模过程中的线性化和未建模动态,利用RBF神经网络对非线性项进行拟合和补偿,建立自适应神经网络权值自适应律,保证闭环系统具有较好的鲁棒稳定性能和抗干扰性能,实现无拖曳卫星控制系统的设计要求。仿真结果表明控制器的有效性,满足了无拖曳卫星的控制精度要求。     

双电机驱动伺服系统径向基函数神经网络反推自适应控制

双电机驱动伺服系统中存在齿隙非线性环节,为了削弱齿隙非线性对系统的动态和稳态性能产生的不利影响,本文提出了一种新的自适应控制方法.首先给出了系统的状态空间模型并分析了双电机同步联动控制的原理,然后应用改进的反推方法,在考虑系统所有的状态变量都能收敛的基础上,引入虚拟控制量,通过逐步递推选择Lyapunov函数,利用径向基函数(radial basis function, RBF)神经网络在线逼近系统中的不确定函数,设计了基于状态反馈的RBF神经网络反推自适应控制器,并进行了稳定性分析.将单纯的反推控制和RBF神经网络反推自适应控制的仿真结果对比,发现后者的优越性高于前者.最后在实际系统中进行试验,验证了所提控制策略的可行性.     

近空间飞行器纵向抗干扰轨迹控制研究

针对近空间飞行器( nearspace vehicle,NSV) 在高超音速飞行时,气动参数变化剧烈且容易受到外界干扰的特点,研究了NSV 纵向轨迹系统的干扰问题,提出了鲁棒自适应动态面的回馈递推控制方法。首先对高度非线性、高度复杂的NSV 的纵向运动的模型进行坐标变换,采用输入－输出反馈线性化方法,将其转化为仿射非线性模型; 然后通过一阶低通滤波器对控制器设计中的虚拟控制律进行估计,从而避免了对其求导带来的计算膨胀问题; 再结合神经网络逼近理论以及虚拟控制器中的鲁棒项,一起消除近空间飞行器的纵向系统中存在的参数摄动不确定和外界干扰。最后通过稳定性分析,表明了该方法在降低系统控制器复杂性的同时仍具有很好的鲁棒性。     

基于反步自适应神经网络的船舶航迹控制

针对欠驱动船舶在稳定航速条件下轨迹跟踪问题,提出了一种基于自适应神经网络与反步法相结合的控制算法.该算法将实际的欠驱动船舶视为模型完全未知的非线性系统,利用神经网络的函数逼近特性实现控制器中非线性部分的在线估计,采用同时调整输入层-隐层、隐层-输出层间的权值阵的方法进行神经网络权值调整.通过选取积分型Lyapunov函数证明了闭环系统的稳定性.仿真实验表明该控制策略具有良好的跟踪特性,可以实现对期望航迹的精确跟踪.     

Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.     

可逆冷带轧机速度张力系统的分散重叠控制

针对可逆冷带轧机速度张力系统的耦合和协调控制问题,提出了速度张力分散重叠控制方法.该方法首先利用包含原理和重叠结构分解,扩展原系统的状态空间,得到多个解耦的重叠子系统,并应用线性二次型(LQ)最优控制和顺序设计方法,设计各子系统的控制律.其次,将所设计的控制律收缩至原系统的状态空间,得到原系统的控制器.最后对某1422mm可逆冷带轧机的速度张力控制系统进行了仿真研究.结果表明,本文所提出的速度张力分散重叠控制方法能有效弱化速度与张力间的耦合,实现主轧机与左、右卷取机间的协调控制,同时改善了张力控制系统的动态性能,保证了轧机升降速时的张力控制精度.