Developing a neural network-based run-to-run process controller for chemical-mechanical planarization

A new neural network-based run-to-run process control system (NNRtRC) is proposed in this article. The key characteristic of this NNRtRC is that the linear and stationary process estimator and controller in the exponentially weighted moving average (EWMA) run-to-run control scheme are replaced by two multilayer feed-forward neural networks. An efficient learning algorithm inspired by the sliding mode control law is suggested for the neural network-based run-to-run controller. Computer simulations illustrate that the proposed NNRtRC performs better than the EWMA approach in terms of draft suppression and adaptation to environmental change. Experimental results show that the NNRtRC can precisely trace the desired target of material removal rate (MRR) and keep the within wafer non-uniformity (WIWNU) in an acceptable range.     

神经网络自适应控制的研究进展及展望

关于人工神经网络与自适应结合的研究，近年来已成为智能控制学科的热点之一。自适应具有强鲁棒性，神经网络则具有自学习功能和良好的容错能力，神经网络自适应控制由于较好地结合了二者的优点而具有强大的优势。本文系统地综述了神经网络自适应控制的进展，讨论了神经网络自适应的主要模型和算法，并就其存在的一些问题、应用与发展趋势进行了探讨。     

基于神经网络的动态逆方法研究

该文探讨了神经网络与非线性动态逆方法相结合的神经网络动态逆方法，研究了神经网络动态逆的网络结构。为进一步改善直接动态逆控制器的性能，对由动态逆和原系统构成的伪线性系统，给出了两种综合方案。通过仿真研究表明，综合控制策略不仅能够改善系统的动态性能，而且具有良好的鲁棒性。研究成果显示了神经网络动态逆方法的有效性和可行性以及在控制系统设计中所具有的潜在能力。     

Nonlinear model identification and adaptive model predictive control using neural networks

This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.     

On-line lower-order modeling via neural networks

This paper presents a novel method to determine the parameters of a first-order plus dead-time model using neural networks. The outputs of the neural networks are the gain, dominant time constant, and apparent time delay. By combining this algorithm with a conventional PI or PID controller, we also present an adaptive controller which requires very little a priori knowledge about the plant under control. The simplicity of the scheme for real-time control provides a new approach for implementing neural network applications for a variety of on-line industrial control problems. Simulation and experimental results demonstrate the feasibility and adaptive property of the proposed scheme.     

A new adaptive controller for constant turning force

A new computerised adaptive control constraint (ACC) system in turning with a constant cutting force constraint is described in this paper. It is shown that the ACC control system based on the fuzzy linguistic rules can achieve an automatic on-line adjustment of feedrate to optimise the production rate even under the variation of cutting conditions in turning operations.     

基于神经元网络参数自调整的PID控制器

本文介绍一种基于神经元网络自学习的ＰＩＤ控制器。该控制器不仅具有自学习自适应能力,而且具有自调整比例因子功能。实验表明,该控制器能够改善温度控制系统的动态特性和对环境的鲁棒性     

多变量协调自适应控制器初步研究

本文初步研究了多变时过程控制器设计的优化目标和约束的统一表达体系,将模块多变量控制技术引入自适应控制器的设计过程,提出了多变量协调自适应控制器的概念,并初步解决了有约束的多目标优化控制的设计问题。     

模糊神经网络核子秤配料自学习控制系统

提出一种模糊神经网络自学习控制方法，并应用于核子秤配料自动控制系统中。经仿真实验和应用结果表明，该控制方案可改善具有时变及大纯滞后的核子秤配料控制系统，其性能优于一般Fuzzy控制。     

Adaptive control of nonlinear system using online error minimum neural networks

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly.     

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.     

Solving multiple response optimisation problems using adaptive neural networks

This paper describes an integrated methodology using experimental designs and neural networks technologies for solving multiple response problems. This new methodology consists of an experiment reference template for designing and collecting training data samples and a parallel distributed computational adaptive neural network system to provide a powerful tool for data modelling, guiding experimentation and empirical investigations. While the experiment reference template is for determining the measurements to adopt in order to extract maximum information within minimum experimental efforts, the adaptive neural network provides a nonlinear multivariate data-fitting algorithm for analysing the results of the experimental design and providing decision support. This integrated methodology is used to model and optimise a multiple response metal inert gas (MIG) welding process. The neural network is trained with optimum welding experimental data, tested and compared in an actual welding environment in terms of weld quality. The relevant data is established using experimental design methods and is highlighted in the case study. The implementation for this case study was carried out using a semi-automatic welding facility, to mass weld a 20 in.×0.438 in. pin/box onto a 20 in.×0.5 in.×37 ft pipe (tubular drilling products), in an actual workshop which makes oilfield equipment. The entire range of welding combination that the process might be subject to during actual welding operations is included to study the weld quality.     

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.     

A neural information retrieval system

Group technology is an approach to manufacturing that attempts to enhance production efficiency by grouping similar activities and tasks together. The results of this process are then used in the execution of similar tasks and activities. This concept can be applied to a variety of activities such as design retrieval, purchasing, sales, and process planning [1]. Traditionally, classification and coding has been used to implement group technology. In this paper, however, we discuss a novel approach using neural networks, a technology noted for its powerful pattern-matching capability. Although this approach can be applied to the entire spectrum of group technology applications, we focus on an application to the retrieval and reuse of engineering part designs.Neural networks based on adaptive resonance theory (ART) are being developed for application in the retrieval and reuse of engineering designs. Two-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or clusters of similar parts. These representations, in their basic form, amount to bit maps of the design, and can become very large when the design is represented in high resolution. We describe a neural information retrieval system (NIRS) under development. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall a family of similar parts when queried with a new part design. This application is of large practical value to industry because it aids in the identification, retrieval, and reuse of engineering designs.     

Distributed adaptive neural networks leader-following formation control for quadrotors with directed switching topologies

The leader-following formation problem is discussed for a team of quadrotors under directed switching topologies. To obtain a more general dynamic model, we describe the quadrotor system in a non-affine pure-feedback form with mismatched unknown nonlinearities. By employing an adaptive neural networks state observer to approximate the unknown nonlinear functions and to reconstruct the immeasurable inner states, we propose a novel distributed output feedback formation control protocol with the backstepping method combining with the dynamic surface control technique. From the Lyapunov stability theorem, all signals in the closed-loop formation system are proven to be cooperatively semiglobally uniformly ultimately bounded for any given bounded initial conditions. Finally, we proved that we verify the performance of the proposed formation control approach by a simulation study.     

Adaptive nonlinear control using input normalized neural networks

An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small.     

A study on the control of an IPMC actuator using an adaptive fuzzy algorithm

The Ionic Polymer Metal Composite (IPMC) is one of the electroactive polymers (EAP) that was shown to have potential application as an actuator. It bends by applying a low voltage current (1–3 V) to its surfaces when containing water. In this paper, the basic characteristics and the static & dynamic modeling of IPMC is discussed. In modeling and analysis, the equations of motion, which describe the total dynamics of the system, are driven. To control the position of the IPMC actuator, an adaptive fuzzy algorithm is used. IPMC is a time varying system because the some parameters vary with the passage of time. In this paper, the modeling and control of IPMC is introduced.     

一种新的自适应PID控制算法

针对大惯性工业对象，设计了一种新的自适应PID调节器控制算法并应用手工业温度控制系统中，实验结果表明，利用人工智能算法与PID自适应算法的有机结合，可以使温度控制曲线在不同的阶段平滑过渡，使系统控制过程达到最优。     

压电陶瓷执行器的神经网络实时自适应逆控制

目的：为了提高压电陶瓷执行器执行精度,提出消除压电陶瓷的非线性、非光滑的迟滞特性的方法。 方法：提出了基于内积的压电陶瓷动态神经网络非线性、非光滑的迟滞逆模型,采用反馈误差学习方法,避免了求取压电陶瓷的Jacobian信息,快速地在线得到压电陶瓷的逆模型,并结合PID反馈控制,在dSPACE系统平台上,实现压电陶瓷的神经网络自适应逆控制,为了提高实时性,程序采用效率高、速度快的C-MEX S Function编程。结果：实验结果表明：神经网络自适应逆控制的控制精度为：0.13μm,而PID控制精度为：0.32μm 。结论：所提出方法有效地消除了迟滞的影响,控制精度高。     

基于启发式遗传算法的非线性神经网络预测控制器

本文提出一种以小脑模型（CMAC）网络为多步预测模型的非线性预测控制算法，并将启发式遗传算法引入到滚动优化中，以提高优化过程中的收敛速度和求解精度。仿真结果表明该算法是有效可行的。