电液伺服系统频率特性测试系统的设计

为满足电液伺服系统的频率特性测试的需要,根据电液伺服控制系统的组成,采用虚拟频率特性测试仪技术分别设计了计算机控制系统以及数据处理程序,构成电液伺服系统频率特性测试系统。该测试系统可完成白噪声激励信号生成,电液伺服系统实时控制以及数据记录等功能,通过数据处理程序可完成电液伺服系统频率特性曲线绘制,该测试系统在电液伺服系统频率特性实验中发挥了重要的作用。     

基于AMEsim的自适应控制在电液力伺服系统上仿真研究

由于实际电液伺服系统大量存在非线性、时变、不确定、时滞及强耦合特性等,一般无法获得精确的教学模型.为了解决参数的不确定性和随机变化性对电液力控伺服系统的影响,设计了自适应控制器.利用AMEsim和Simulink联合仿真技术建立了该电液力控制系统模型,设计一种自适应控制器,进行了电液力控伺服系统的控制仿真.仿真结果表明了自适应控制器对于系统控制的有效性和鲁棒性.     

一种电液伺服系统中伺服控制器的设计与应用

文章设计了一种采用双DSP结构的伺服控制器，介绍了其组成结构、功能以及在电液伺服系统控制中的应用。将DSP芯片和数字信号处理技术结合，采用模糊算法设计以及同步协调设计，完成对原有单一通道、非同步的电液伺服系统扩充。多通道多变量大容量协调同步控制的复杂电液伺服系统具有强大的数据采集能力，64通道动态数据采集，320通道静态数据采集，完全满足大型结构试验的需要。     

状态反馈精确线性化在电液伺服位置控制系统中的应用研究

电液伺服系统是一种非线性系统。本文对采用状态反馈精确线性化方法来控制电液伺服系统的可行性进行了分析,根据该电液伺服位置控制系统的组成和工作原理建立了数学模型,并基于状态反馈精确线性化理论结合最优控制原理进行了系统设计与仿真。仿真结果表明,采用状态反馈精确线性化方法和最优控制理论,可使该电液伺服位置系统具有较好的位移跟踪线性度和较高的稳态精度。     

电液伺服系统多项式非线性建模与控制一体化设计

常规电液伺服系统PID控制无法克服非线性因素影响,存在跟踪准确性和鲁棒性问题.因此,本文提出电液伺服系统多项式非线性H_∞控制律设计方法,改进电液伺服系统的控制性能与鲁棒性.首先利用多项式非线性模型对电液伺服系统进行系统辨识,得到以误差作为状态变量的多项式非线性模型;然后设计多项式非线性控制律,证明所提出控制律可以保证系统从干扰至控制输出L₂增益小于等于设定值,并且在系统干扰为零时保证误差全局渐进收敛,同时给出了控制律的求解方法.最后对提出的控制律进行实验验证.实验结果表明:相较于常规PID控制,多项式非线性控制律能够改善实验台伺服缸控制过程的瞬态响应,具有更好的抗干扰能力.本文提出的设计方法为非线性H_∞控制在电液伺服系统控制领域的实际应用提供了可行方案.     

带钢卷取机跑偏电液伺服系统PID控制器的设计

针对带钢连续轧制过程中的带钢跑偏问题,文章提出了一种带钢卷取机跑偏电液伺服系统PID控制器的设计方案。在介绍带钢卷取机跑偏电液伺服系统的基础上,通过静态计算,建立了该系统的数学模型;根据带钢卷取机跑偏电液伺服系统的开环传递函数及不同的控制量组合,分别设计了比例控制器、比例积分控制器和比例微分控制器,并采用Matlab软件仿真并分析了各控制器作用下系统的阶跃响应性能,根据各时域响应指标评定并确定了PID控制器的参数值。由PID控制器作用下系统的阶跃响应波形及系统开、闭环波德图得出,该PID控制器超调小、调节时间短、响应快、性能稳定;对带钢卷取机跑偏电液伺服系统的稳态误差进行分析后得出,该系统的稳态误差为零,由此证明了该PID控制器的有效性和正确性。     

电液位置伺服系统内模鲁棒控制器的设计

针对电液位置伺服系统存在一定的参数不确定性和较大的负载扰动等问题,建立了电液位置伺服线性系统的数学模型,并将其转化为H∞性能准则问题.利用H∞鲁棒控制理论,设计了电液位置伺服控制系统的鲁棒状态反馈控制器;基于内模原理的设计方法,设计了跟踪控制器.仿真结果表明,该位置鲁棒跟踪控制系统不仅对于伺服系统的参数不确定性有较好的控制效果,而且可以有效地抑制位置伺服系统负载扰动的影响,并有效地提高系统的跟踪特性,使系统具有良好的动态性能.     

基于滑模状态观测器的电液位置伺服系统控制

在电液伺服系统优化设计的研究中,针对电液位置伺服系统的高阶非线性特性、系统参数不确定性以及系统状态信号测量困难的情况,提出一种基于滑模状态观测器的反演控制策略.策略采用滑模方法设计状态观测器,只需要位置传感器,不需要速度传感器和加速度传感器.对采用状态观测器之后的系统,设计反演控制器,针对系统中的不确定性,在反演控制的最后一步采用滑模控制设计,基于Lyapunov方法证明了系统中所有信号是一致最终有界的,闭环系统是稳定的.仿真结果表明,上述策略为电液伺服系统优化设计提供了参考.     

自适应与迭代学习复合控制的电液位置系统的研究

电液伺服系统具有高阶性、非线性、时变性的特点,而且运行过程中还将受到温度变化等因素的干扰,所以常规的PID控制方法难以取得令人满意的效果。为了使电液伺服系统拥有较好的控制效果,提出一种将自适应与迭代学习控制相结合的控制策略,应用于电液伺服系统的位置控制中。应用位置作为反馈,采用开闭环PI的控制策略,以及自校正的控制原理设计控制器,最后运用MATLAB来对其进行仿真。仿真结果表明,该控制策略能够提高系统的控制精度,提升系统的响应速度,提高其收敛与鲁棒性能。     

一类轻载电液位置伺服系统线性自抗扰控制

为解决一类轻载电液位置伺服系统线性自抗扰控制器设计过程中面临的阶次选择问题,本文从系统特性、频域等角度,分析自抗扰框架中“积分器串联结构”与轻载电液位置伺服系统之间的内在联系,得到轻载电液位置伺服系统在自抗扰控制框架下是本质“一阶”系统的结论,从而合理设计了1阶线性自抗扰控制器.在此基础上,提出了有效的控制器参数整定方法,并分析了闭环系统的稳定性.仿真和试验结果表明,与高阶相比1阶线性自抗扰控制器可以更好地控制动态过程较快、负载较轻的惯性负载电液位置伺服系统,为自抗扰控制在液压伺服领域的工程应用提供了参考.     

基于神经网络的无人机负载模拟器的复合控制

衡量负载模拟器系统性能的关键指标是多余力矩的抑制。针对无人机负载模拟器系统的非线性及多余力矩强扰动的特点,依据神经网络的非线性逼近和自学习特性,提出了一种基于神经网络和前馈相结合的复合控制器,用来提高系统的性能。复合控制器利用前馈来补偿定常多余力,利用神经网络进行在线辨识、控制来补偿系统的非线性部分,很好地抑制了多余力矩。该文给出了具体的控制结构和算法。仿真结果还表明该方法极大地改善了系统动态加载性能,有很强的鲁棒性。     

Self-learning neurofuzzy control of a liquid helium cryostat

The paper demonstrates that a self-learning neurofuzzy controller is able to regulate the temperature in a liquid helium cryostat. In order to simplify the task of commissioning the controller, a strategy for choosing the user-selected parameters from an equivalent proportional-plus-integral controller (PI) is derived. Experimental results which illustrate the potential of the proposed control scheme are presented. The performance of the self-learning neurofuzzy controller is also compared with that of a commercial gain-scheduled PI controller.     

支持向量机-模糊推理自学习控制器设计

常规的模糊推理系统大多由专家经验建立模糊规则,自学习能力不强.提出了一种支持向量机-模糊推理系统,由支持向量机实现模糊推理系统的自学习,并设计了一种支持向量机-模糊推理自学习控制器.文章给出了自学习控制器的结构和学习算法,对比研究了变尺度梯度优化和混沌优化两种学习算法.针对非线性对象的仿真实验验证了该控制器的优良性能,控制效果比模糊逻辑控制器更好.     

SLIDING MODE CONTROL SYNTHESIS OF UNCERTAIN TIME‐DELAY SYSTEMS

Sliding mode control synthesis is developed for a class of uncertain time‐delay systems with nonlinear disturbances and unknown delay values whose unperturbed dynamics is linear. The synthesis is based on a new delay‐dependent stability criterion. The controller constructed proves to be robust against sufficiently small delay variations and external disturbances. An admissible upperbound such that the corresponding closedloop system remains globally asymptotically stable for each delay value less than this up‐perbound is derived. Performance issues of the controller are illustrated in a simulation study.     

Integrated adaptive-robust control of robot manipulators

In this article, an integrand adaptive-robust approach along with a smooth adaptive-robust friction compensation strategy are applied to tracking control of robot manipulators with joint stick-slip friction. The designed controller takes advantage of both adaptive and robust approaches. It has the ability to learn and the ability to reject disturbance and to handle various uncertainties including stick-slip friction. The uncertainties due to unknown robot link parameters, unknown viscous friction coefficient, and unknown maximum static friction, which are linear in parameters, are compensated by the integrated adaptive-robust control method. On the other hand, the exponential friction (used to model the Stribeck effect: the downward bend of friction torque at low velocities), which are nonlinear in parameters, and external disturbances, are compensated by a robust compensator with a self-learning upper bounding function when no a priori knowledge on the exponential friction and disturbances are available. The employed robust compensators produce smooth control action, and avoid motion intermittency, which are commonly associated with stick-slip friction. The proposed controller guarantees global asymptotic stability of the closed-loop system, as proved by Lyapunov's direct method. © 1998 John Wiley & Sons, Inc.     

Global disturbance rejection of a class of nonlinear systems with unknown exosystems

An asymptotic rejection algorithm is proposed for a class of nonlinear systems that have not only additive nonlinear uncertainties but also unknown disturbances. The disturbances are generated from an unknown exosystem, and are assumed to be sinusoidal disturbances with unknown amplitude and frequency. By using the technique of backstepping and adaptive control, a nonlinear state feedback controller is designed. Under the proposed controller, the system＇s state variables asymptotically converge to zero, and the disturbances are rejected completely. The approach used is an integration of the robust stabilization technique, adaptive technique, and backstepping technique.     

Adaptive fuzzy fault-tolerant attitude control of spacecraft

This paper investigates the attitude control of spacecraft in the presence of unknown mass moment of inertia matrix, external disturbances, actuator failures, and control input constraints. A robust adaptive controller is proposed with the utilization of fuzzy logic and backstepping techniques. The unit quaternion is employed to describe the attitude of spacecraft for global representation without singularities. The system uncertainty is estimated by introducing a fuzzy logic system. The adaptive mechanism has only two parameters to be adapted on-line because the adaptive law of the proposed controller is derived from the norm of the weight matrix. The stability of the closed-loop system is guaranteed by Lyapunov direct approach. Results of numerical simulations state that the proposed controller is successful in achieving high attitude performance in the presence of parametric uncertainties, external disturbances, actuator failures, and control input constraints.     

不确定串联非线性系统H∞鲁棒自适应控制

针对一类含有未知参数和干扰的非最小相位串联非线性系统,结合H∞控制和自适应控制方法并利用李雅普诺夫函数递推设计方法设计了状态反馈H∞自适应控制器,避免了求解Hamilton-Jacobi-Isaacs不等式设计控制器的困难.该控制器不仅保证闭环系统ISS(input-to-state)稳定,而且使得系统对于所有允许的参数不确定从干扰输入到可控输出的 L 2增益不大于给定的值.最后,给出了一个仿真例子,仿真结果充分表明了所设计的控制器的可行性和有效性.     

Self-learning active vibration control of a flexible plate structure with piezoelectric actuator

In this paper, an active vibration control (AVC) incorporating active piezoelectric actuator and self-learning control for a flexible plate structure is presented. The flexible plate system is first modelled and simulated via a finite difference (FD) method. Then, the validity of the obtained model is investigated by comparing the plate natural frequencies predicted by the model with the reported values obtained from literature. After validating the model, a proportional or P-type iterative learning (IL) algorithm combined with a feedback controller is applied to the plate dynamics via the FD simulation platform. The algorithms were then coded in MATLAB to evaluate the performance of the control system. An optimized value of the learning parameter and an appropriate stopping criterion for the IL algorithm were also proposed. Different types of disturbances were employed to excite the plate system at different excitation points and the controller ability to attenuate the vibration of observation point was investigated. The simulation results clearly demonstrate an effective vibration suppression capability that can be achieved using piezoelectric actuator with the incorporated self-learning feedback controller.     

Neural network-based self-learning control for power transmission line deicing robot

Recently, the application of the maintenance transmission line robot has been very popular in the power industry. However, difficulties in the control of maintenance transmission line robot exist due to multiple nonlinearities, plant parameter variations and external disturbances. This paper investigates the possibility of using neural network as a promising self-learning control alternative for the control problem of inspection and deicing transmission line robot. We first discuss the mechanical structure, as well as dynamic model of a deicing robot. And then, a neural network-based self-learning control strategy consists of a fuzzy neural network controller and an ELM-based single-layer-feedback neural networks identifier are proposed for this deicing transmission line robot. Both the structure and the learning algorithm of the control system are presented. The proposed controller is verified by computer simulations and experiments.