Neural‐hybrid control of systems with sandwiched dead‐zones

The control of systems that have sandwiched nonsmooth nonlinearities, such as a dead‐zone sandwiched between two dynamic blocks, is addressed. An adaptive inverse control scheme using a hybrid controller structure and a neural network based inverse compensator, is proposed for such systems with unknown sandwiched dead‐zone. This neural‐hybrid controller consists of an inner loop discrete‐time feedback structure incorporated with an adaptive inverse using a neural network for the unknown dead‐zone, and an outer‐loop continuous‐time feedback control law for achieving desired output tracking. The dead‐zone compensator consists of two neural networks, one used as an estimator of the sandwiched dead‐zone function and the other for the compensation itself. The compensator neural network has neurons that can approximate jump functions such as a dead‐zone inverse. The weights of the two neural networks are tuned using a modified gradient algorithm. Simulation results are given to illustrate the performance of the proposed neural‐hybrid controller. Copyright © 2002 John Wiley & Sons, Ltd.     

Maximum power extraction on wind turbine systems using block‐backstepping with gradient dynamics control

In this work, a novel adaptive control scheme that allows driving a stand‐alone variable‐speed wind turbine system to its maximum power point is presented. The scheme is based on the regulation of the optimal rotor speed point of the wind turbine. In order to compute the rotor speed reference, a model‐based extremum‐seeking algorithm is derived. The wind speed signal is necessary to calculate this reference, and a novel artificial neural network is derived to approximate this signal. The neural network does not need off‐line learning stage, because a nonlinear dynamics for the weight vector is proposed. A block‐backstepping controller is derived to stabilize and to drive the system to the optimal power point; to avoid singularities, the gradient dynamics technique is applied to this controller. Numerical simulations are carried out to show the performance of the controller and the estimator. Copyright © 2016 John Wiley & Sons, Ltd.     

Indirect adaptive control of nonlinear system via dynamic multilayer neural networks with multi‐time scales

This paper deals with adaptive nonlinear identification and trajectory tracking problem via dynamic multilayer neural network with different time scales. By means of a Lyapunov‐like analysis, we determine stability conditions for the on‐line identification. Then, a sliding mode controller is designed for trajectory tracking with consideration of the modeling error and disturbance. The main contributions of the paper lie in the following aspects. First, we extend our prior identification results of single‐layer dynamic neural networks with multi‐time scales to those of multilayer case. Second, the e‐modification in standard use in adaptive control is introduced in the on‐line update laws to guarantee bounded weights and bounded identification errors. Third, the potential singularity problem in controller design is solved by using new update laws for the NN weights so that the control signal is guaranteed bounded. The stability of proposed controller is proved by using Lyapunov function. Simulation results demonstrate the effectiveness of the proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Active fault tolerant control systems by the semi‐Markov model approach

This paper investigates the active fault tolerant control problem via the H _∞ state feedback controller. Because of the limitations of Markov processes, we apply semi‐Markov process in the system modeling. Two random processes are involved in the system: the failure process and the fault detection process. Therefore, two corresponding semi‐Markov processes are integrated in the closed‐loop system model. This framework can generally accommodate different types of system faults, including the randomly happening sensor faults and actuator faults. A controller is designed to guarantee the closed‐loop system stability with a prescribed noise/disturbance attenuation level. The controller can be readily solved by using convex optimization techniques. A vertical take‐off and landing vehicle example with actuation faults is used to demonstrate the effectiveness of the proposed technique. Copyright © 2013 John Wiley & Sons, Ltd.     

永磁直线同步伺服系统混合递归模糊神经网络控制

针对直线电机易受诸多不确定因素的影响，提出了采用递归模糊神经网络和扰动观测器的控制方案。系统采用IP位置控制器；扰动观测器将所观测的扰动力前馈，提高了系统的抗干扰能力。为改善系统受到突加减扰动时的伺服性能，引进了递归模糊神经网络补偿器，采用动态反馈学习算法，在线调整。仿真结果表明，该控制方案可以有效增强系统的鲁棒性。     

永磁直线同步电机的鲁棒自适应神经网络控制

针对永磁直线同步电动机的端部效应和非线性摩擦问题,采用一种鲁棒自适应神经网络控制方法,实现了永磁直线电机的跟踪控制.所设计的控制器包含两个部分:一部分是自适应神经网络控制器,用来逼近理想控制器,该神经网络的输入为滑模切换函数;另一部分是鲁棒控制器,用来消除逼近误差.通过李亚普诺夫稳定性定理验证了所设计的控制器能够保证控...     

铣削加工薄壁工件的变形仿真

以平板薄壁工件为研究对象,在Matlab平台实现其铣削加工过程的变形仿真。根据瞬时刚性力学模型将轴向铣刀离散化处理,得到微元的切削力,进而求得整个铣刀切削力,建立了铣削加工过程铣削力模型。利用理论计算和有限元分析方法,推导了平板薄壁工件有限元模型的变形控制方程。模拟了铣削过程中瞬态铣削力和变形量的相互作用,预测了工件在铣削力作用下的变形情况。通过实例验证了该仿真方法的可行性与有效性。     

Intelligent control using multiple models and neural networks

Most adaptive control algorithms for nonlinear discrete time systems become invalid when the controlled systems have non‐minimum phase properties and large uncertainties. In this paper, an intelligent control method using multiple models and neural networks (NN) is developed to deal with those problems. The proposed control method includes a set of fixed controllers, a re‐initialized neural network (NN) adaptive controller and a free‐running NN adaptive controller. The bounded‐input‐bounded‐output (BIBO) stability and performance convergence of the system are guaranteed by the free‐running adaptive controller, while the multiple fixed controllers and the re‐initialized adaptive controller are used to improve the transient response. Simulation results are presented to demonstrate the effectiveness of the proposed method. Copyright © 2007 John Wiley & Sons, Ltd.     

Force‐based disturbance observer for dynamic force control and a position/force hybrid controller

In this paper, a force‐based disturbance observer (DOB) and a force control system using the DOB are proposed to obtain dynamic force control under disturbances. A DOB can reduce the effect of disturbances and modeling errors on robots. In a conventional DOB, an acceleration response is fed back to a reference, enabling highly precise position control. In other words, the effect of disturbances is decreased by emphasizing the effect of inertial forces. When a force controller is implemented, however, inertial forces are regarded as disturbances respect to a force response. Because inertial forces increase according to the acceleration, conventional DOBs are not suitable for dynamic force control. In the proposed DOB, a force response is fed back instead of an acceleration response. The effect of inertial forces is thus eliminated, thereby improving the tracking performance of force controllers. The proposed method's validity is verified analytically and experimentally. A position/force hybrid controller and a DOB for the controller are proposed as an extension of the proposed DOB. A bilateral controller is given as an example of the proposed hybrid controller, and its tracking performance is demonstrated experimentally. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Functional adaptive controller for multivariable stochastic systems with dynamic structure of neural network

The article deals with a challenging problem of adaptive control design for multivariable stochastic systems with a functional uncertainty. Model of the system is based on multi‐layered perceptron neural networks where both the unknown parameters and the structure are found in real time without a necessity of any off‐line training process. The unknown parameters are estimated by a global estimation method, the Gaussian sum filter, and the structure of the neural network model is optimized by a proposed pruning method. The control law is based on a bicriterial approach to the suboptimal dual control. Two individual criteria are designed and used to introduce conflicting efforts between the estimation and control; probing and caution. A comparison of the proposed dual control and its alternative with an implementation of the pruning algorithm is shown in a numerical example. Copyright © 2011 John Wiley & Sons, Ltd.     

逆变式等离子切割电源变间距模糊-PI控制

针对切割过程的强非线性及不确定性,以逆变式等离子切割电源为研究对象,提出了一种在全论域范围内带有自调整因子的变间距模糊量化算法,并与PI控制结合构成模糊-PI双模控制器。控制算法避免了冗杂的非线性系统建模过程,且本身具有优化的特性,通过模糊-PI双模控制,有效解决了传统模糊算法在提高系统稳态精度和动态性能之间的矛盾。仿真结果表明,与PI控制或模糊控制相比,所设计的双模控制器在稳态性能和鲁棒性两方面都得到了改善。     

基于微粒群单神经元的BLDCM调速算法研究

无刷直流电机(BLDCM)是一种多变量、非线性的系统,传统控制算法难以满足系统的控制要求,针对这一现状,提出了一种基于微粒群单神经元的自适应速度控制算法,该算法利用单神经元在线调整连接权值的能力实现自适应控制,以微粒群优化算法对单神经元的学习步长进行在线优化,提高了单神经元的自学习、自适应能力。仿真实验表明,系统超调量小、转速响应快、转速波动小,比传统PID速度控制具有更好的动静态特性和鲁棒性。研究结果为分析和设计BLDCM控制策略提供了有效的平台,具有一定的理论和工程实际意义。     

大型目标模拟器方位伺服电机的对偶自校正PID控制

大型红外成像目标模拟器方位啊电机量大惯量、变参数非标准装置,针对该电机的具体情况,提出了一种对偶自校正ＰＩＤ控制器。在每一自适应步,通过谱分解和在线求解Ｄｉｏｐｈａｎｔｉｎｅ方程得到最优ＰＩＤ参数,然后基于双重指标进行对偶校正,得到一种既保持对偶效应,又十分简单易行的对偶自校正ＰＩＤ控制器;成功地消除了传统自适应控制系统的关断、终止和猝发等现象,收到了良好的控制效果。该控制器适于参数随机快时变系统     

Model predictive control for line pacing in steel processing

Line pacing control in steel processing lines is known to be a difficult task for operators.?They usually take a conservative control policy in order to avoid shutdown, and the processing capacity of the line has not been fully utilized yet. This paper presents a new modeling and control technique for line pacing based on a hybrid petri net (HPN) and mixed logical dynamical system (MLDS) expressions. The proposed modeling method can harmoniously integrate continuous and discrete aspects of the system. Moreover, the proposed method can provide a suitable formulation for numerical optimization. Based on this hybrid system modeling, the closed‐loop control can be realized by the receding horizon scheme. Numerical examples demonstrate that the processing capacity can be fully utilized by the proposed method. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

矢量控制系统的速度控制及速度辨识新方法研究

介绍了异步电动机矢量控制系统神经网络速度控制器的设计方法;同时提出了将开环直接计算与模型参考自适应方法相结合的神经网络混合转速辨识模型。仿真结果表明,基于该速度控制器和速度估计器的矢量控制系统动静态性能好,解决了瞬时无功模型参考自适应方法的转速不稳定问题,转速估计精度高。     

神经网络和模糊算法相结合的永磁同步电机的鲁棒控制

提出一种神经网络和模糊理论相结合的控制算法,用于永磁同步电机的控制.该算法用基于BP神经网络的PID算法作为速度控制器,实现控制系统的在线自适应调整;同时用模糊理论算法作为神经网络控制器输出的限制,实现了良好的控制动态性能.在与传统的PI控制仿真比较中,该算法显示出了较好的控制性能,对负载和电机参数的变化不再敏感,且控制器可以在误差较大的时候快速跟踪指令,而在误差较小的时候实现稳定运行.     

Direct adaptive neural tracking control for a class of stochastic pure‐feedback nonlinear systems with unknown dead‐zone

This paper considers the problem of adaptive neural tracking control for a class of nonlinear stochastic pure‐feedback systems with unknown dead zone. Based on the radial basis function neural networks' online approximation capability, a novel adaptive neural controller is presented via backstepping technique. It is shown that the proposed controller guarantees that all the signals of the closed‐loop system are semi‐globally, uniformly bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the suggested control scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

LCC-MMC混合级联型直流输电系统启动控制策略研究

为实现逆变侧采用电网换相换流器(line commutated converter, LCC)和多个并联模块化多电平换流器(modular multilevel converter, MMC)阀组串联的混合级联直流输电系统的安全、可靠启动,提出了一种按照不可控充电和系统控制解锁两阶段划分的启动控制策略。首先建立该类混合结构下直流系统的数学模型。在对低压端MMC不可控充电阶段暂态特性分析的基础上,推导了MMC最大启动冲击电流和预充电时间的等效计算公式,并根据最大冲击电流和预充电时间为MMC启动过程中限流电阻的选取提供依据。其次,在系统级控制器解锁至系统稳态运行阶段,针对MMC并联组在控制器解锁时产生的不平衡启动电流问题进行了分析,提出一种基于不同换流器间控制时序配合与自适应MMC功率参考值的启动优化策略。最后,通过PSCAD/EMTDC仿真结果表明,所提启动方案可以有效实现混合级联型直流输电系统的平稳启动。     

Direct adaptive neural control of nonlinear strict‐feedback systems with unmodeled dynamics using small‐gain approach

In this paper, a novel direct adaptive neural control approach is presented for a class of single‐input and single‐output strict‐feedback nonlinear systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. Radial basis function neural networks are used to approximate the unknown and desired control signals, and a direct adaptive neural controller is constructed by combining the backstepping technique and the property of hyperbolic tangent function. It is shown that the proposed control scheme can guarantee that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded in mean square. The main advantage of this paper is that a novel adaptive neural control scheme with only one adaptive law is developed for uncertain strict‐feedback nonlinear systems with unmodeled dynamics. Simulation results are provided to illustrate the effectiveness of the proposed scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

Robust control and data‐driven tuning of a hybrid integrator‐gain system with applications to wafer scanners

A hybrid integrator‐gain system is discussed that aims for improved low‐frequency disturbance rejection, while, at the same time, does not deteriorate overshoot and settling times when compared with a linear integrator. The hybrid integrator has similar phase advantages as the well‐known Clegg integrator but without inducing the discontinuous behavior resulting from resetting system state values. Optimal tuning of the controller parameters of the hybrid integrator is strongly influenced by machine‐specific properties and therefore favors a data‐driven optimization approach. However, as a time‐domain optimization algorithm can easily lead to nonrobust solutions in the sense of large peaking of the closed‐loop frequency response functions, frequency‐domain robustness constraints will be imposed. By means of an adaptive weighting filter design, the parameter updates are penalized upon violation of said robustness constraints. Posed in an unconstrained problem formulation, this is subsequently solved by applying a Gauss‐Newton–based parameter update scheme. Closed‐loop stability of the linear time‐invariant plant and controller in feedback connection with a hybrid integrator‐gain system element follows from a circle‐criterion‐like analysis, which is based on evaluating (measured) frequency response data. Measurement results obtained from an industrial wafer scanner demonstrate the effectiveness of the approach.