Minimization of reactor noise through external control

An analytical approach based on the work of Wiener is taken to develop an expression for an optimum linear feedback control system that will minimize the random variations in the flux of a nuclear power reactor. The optimization criterion is the minimization of the mean-square value of these random variations. In determining the optimum control system transfer function, an acceleration constraint is placed on the control-rod drive mechanism. To demonstrate the technique presented, an optimum control system transfer function is derived for the experimental boiling water reactor (EBWR) under specified operating conditions. The changes in the mean-square value of reactor flux caused by varying acceleration constraint level is discussed. Results show that a considerable reduction in the mean-square value of the random flux variations can be obtained through external control.     

Approximation-based adaptive tracking control of nonlinear pure-feedback systems with time-varying output constraints

An adaptive neural network control problem of completely non-affine pure-feedback systems with a time-varying output constraint and external disturbances is investigated. For the controller design, we presents an appropriate Barrier Lyapunov Function (BLF) considering both the time-varying output constraint and the control direction nonlinearities induced from the implicit function theorem and mean value theorem. From an error transformation, the BLF dependent on the time-varying constraint is transformed into the explicitly time-independent BLF. Based on the explicitly time-independent BLF, an adaptive dynamic surface control scheme using the function approximation technique is designed to ensure both the constraint satisfaction and the desired tracking ability. It is shown that all signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an adjustable neighborhood of the origin while the time-varying output constraint is never violated.     

Neural Network Structures for Optimal Control of LPCVD Reactors

In this paper, a new approach of LPCVD reactor modelling and control is presented, based on the use of neural networks. We first present the development of a hybrid networks model of the reactor. The objective is to provide a simulation model which can be used to compute online the film thickness on each wafer. In the second section, the thermal control of a LPCVD reactor is studied. The objective is to develop a multivariable controller to control a space- and time-varying temperature profile inside the reactor. A neural network is designed using a methodology based on process inverse dynamics modelling. Good control results have been obtained when tracking space-time temperature profiles inside the LPCVD reactor pilot plant. Finally, global software is elaborated to achieve film thickness control in an experimental LPCVD reactor pilot plant, in order to get a defined and uniform deposition thickness on the wafers all along the reactor. Experimental results are presented which confirm the efficiency of the optimal control strategy.     

Economic model predictive control of parabolic PDE systems: Addressing state estimation and computational efficiency

In a previous work [20], an economic model predictive control (EMPC) system for parabolic partial differential equation (PDE) systems was proposed. Through operating the PDE system in a time-varying fashion, the EMPC system demonstrated improved economic performance over steady-state operation. The EMPC system assumed the knowledge of the complete state spatial profile at each sampling period. From a practical point of view, measurements of the state variables are typically only available at a finite number of spatial positions. Additionally, the basis functions used to construct a reduced-order model (ROM) for the EMPC system were derived using analytical sinusoidal/cosinusoidal eigenfunctions. However, constructing a ROM on the basis of historical data-based empirical eigenfunctions by applying Karhunen-Loève expansion may be more computationally efficient. To address these issues, several EMPC systems are formulated for both output feedback implementation and with ROMs based on analytical sinusoidal/cosinusoidal eigenfunctions and empirical eigenfunctions. The EMPC systems are evaluated using a non-isothermal tubular reactor example, described by two nonlinear parabolic PDEs, where a second-order reaction takes place. The model accuracy, computational time, input and state constraint satisfaction, and closed-loop economic performance of the closed-loop tubular reactor under the different EMPC systems are compared.     

Stochastic closed-loop model predictive control of continuous nonlinear chemical processes

A new predictive control framework for chemical processes is presented, that has a number of fundamental differences to classical MPC. Both future disturbances and future process measurements are explicitly introduced in the model prediction, while back-off prevents violation of the inequality constraints. A feedforward trajectory, used for constraint pushing, is optimized simultaneously with a linear time-varying feedback controller, used to minimize the back-off. No feedback is generated by the receding horizon implementation itself. Via several transformations, the resulting optimization problem is rendered convex. For nonlinear processes, this applies to the sub-problem in a sequential conic optimization approach. A two stage LQG approach reduces the complexity even further for large scale systems. The method is illustrated on a HDPE reactor example and compared to a LTV-MPC.     

An optimal scheme for fast rate fault detection based on multirate sampled data

Multirate systems are abundant in industry. In this paper, the problem studied is designing a residual generator for fault detection based on multirate sampled data. The key new feature of such a residual generator is that it operates at a fast rate for prompt fault detection. The design is based on optimizing a performance index to obtain an optimal parity space based residual generator. The lifting technique is used to convert the time-varying multirate design problem into a time-invariant one with a causality constraint for implementability. A procedure for computing an explicit optimal, causal solution is proposed. The advantages of this design are shown through an example.     

Airborne microwave Doppler measurements of ocean wave directional spectra

Abstract

A technique is presented for measuring ocean wave directional spectra from aircraft using microwave Dopper radar. The technique involves backscattering coherent microwave radiation from a patch of sea surface which is small compared to dominant ocean wavelengths in the antenna look direction, and large compared to these lengths in the perpendicular (azimuthal) direction. The mean Doppler shift of the return signal measured over short time intervals is proportional to the mean sea surface velocity of the illuminated patch. Variable sea surface velocities induced by wave motion therefore produce time-varying Doppler shifts in the received signal. The large azimuthal dimension of the patch implies that these variations must be produced by surface waves travelling near the horizontal antenna look direction thus allowing determination of the direction of wave travel. Linear wave theory is used to convert the measured velocities into ocean wave spectral densities. Spectra measured simultaneously with this technique and two laser profilometers, and nearly simultaneously with a surface buoy, are presented. Applications and limitations of this airborne Doppler technique are discussed.     

Ionic current rectification in a conical nanofluidic field effect transistor

A conical nanofluidic field effect transistor (FET) refers to a conical nanopore embedded with an electrically controllable gate electrode. The surface potential of the nanopore can be effectively regulated by manipulating the gate potential applied to the gate electrode, which in turn controls the ionic current through the nanopore. The field effect on the ionic current rectification (ICR) in the conical nanofluidic FET is comprehensively investigated using a continuum model, composed of Nernst-Planck equations for the ionic concentrations, Poisson equation for the electric potential, and Navier-Stokes equations for the flow field. Under the conditions of a low ionic concentration, a low surface charge density of the nanopore, and a high permittivity of the dielectric nanopore, regulation of ICR by FET is significant. The field effect on the ICR with the gate electrode located in the middle region is opposite to that with the gate electrode located near the tip of the nanopore.     

应用控制系统设计工具箱PIMCSD求解卫星编队重构问题

利用线性时变系统终端约束最优控制方法,为椭圆轨道卫星编队的队形重构控制问题设计了均衡耗能最优控制器.由于描述椭圆轨道卫星编队相对运动的Lawden方程是时变方程,给卫星编队重构的最优控制器设计带来一定的困难.利用基于精细积分算法的控制系统设计工具箱-PIMCSD进行系统设计,求解卫星编队重构所需的时变最优反馈控制律和前馈控制律,最后给出了由三颗卫星组成的编队队形重构控制仿真计算结果.     

Time-varying reactivity reconstruction via Walsh functions

The problem of computing and reconstructing the time-varying reactivity input of nuclear reactor systems by using neutron level data records is considered. The integral equation representation of the nuclear reactor is used, and an expansion of the reactivity in a Walsh function series is made. The method provides simple formulas for computing the coefficients of this series. By using these coefficients and the Walsh functions provided by existing generators the time-varying reactivity can be easily reconstructed in a step-like form. A simple example shows the effectiveness of the method.     

Dissociated jerk-limited trajectory applied to time-varying vibration reduction

Jerk-limited trajectories are a widespread solution for the trajectory planning of industrial machines-tools or robots. It is known that jerk limitation can reduce vibrations and in some cases can totally suppress residual vibration induced by a lightly damped stationary mode. However, for systems with time-varying mode, which is classically the case for configuration dependent mode or load mass variations, the previous result vanishes. This paper proposes to extend the jerk-limited profile (JL) properties to time-varying vibration problem. First, a guideline for designing a dissociated jerk-limited profile (DJL) based on simple and pragmatic Finite Impulse Response (FIR) filtering methodology is presented. Following the guideline, the time-varying vibration reduction principle is detailed. Then, experiments conducted on an industrial 3-axes Cartesian manipulator are presented. The experimental results show that the residual vibration magnitude is reduced to less than 23% of the original level obtained with JL profile and the settling time is reduced by 10%, demonstrating the efficiency of the proposed DJL trajectory planning.     

Integrated profile and thickness error compensation for curved part based on on-machine measurement

Curved parts are widely used in aerospace, automotive and energy industries. The profile and thickness accuracy are both critical to some curved parts such as hollow blades. Improving the profile accuracy while ignoring the wall thickness error will reduce the qualified rate of these curved parts, and vice versa. This paper proposes a comprehensive compensation method with constraints of both profile and thickness tolerances for the machining of curved parts. The actual geometry and wall thickness of the parts are obtained by on-machine measurement system after rough machining. Both touch-trigger probe and thickness gage are used to reconstruct the actual outer and inner surface of the workpiece. Then, the comprehensive constraint considering both profile and thickness constraints is established based on the reconstructed workpiece geometry. With the comprehensive constraint, a new target outer surface is constructed. The machining error is calculated from the target surface and compensated via toolpath adjustment. At last, machining experiments are conducted to verify the feasibility of the proposed method.     

基于多面体不变集的变终端约束集RMPC

针对一类状态和输入受约束的多胞不确定线性时变系统,提出了一种基于多面体不变集的变终端约束集鲁棒模型预测控制算法.首先采用基于状态反馈增益的多面体不变集计算方法,给出了一种新的控制不变集序列构造方法,然后以控制不变集序列的并集作为终端约束集,结合在线优化和增益切换,实施变终端约束集双模鲁棒预测控制.该算法不仅有效地扩大了...     

A geometric approach for adaptive estimation of unknown growth kinetics in bioreactors

This paper proposes a new approach for the estimation of unknown and time-varying specific growth rate in fed-batch bioprocess. A novel adaptive estimation technique based on the concept of invariant manifold is proposed as an effective approach to estimate growth kinetic parameters. An asymptotic nonlinear observer is used to provide simultaneous on-line estimation of biomass concentration and growth kinetic. The method is easy to implement and requires only one tuning parameter. The effectiveness of the proposed algorithm is illustrated with representative bioreactor simulation examples.     

Set-based adaptive estimation for a class of nonlinear systems with time-varying parameters

In this paper, an adaptive estimation technique is proposed for the estimation of time-varying parameters for a class of continuous-time nonlinear system. A set-based adaptive estimation is used to estimate the time-varying parameters along with an uncertainty set. The proposed method is such that the uncertainty set update is guaranteed to contain the true value of the parameters. Unlike existing techniques that rely on the use of polynomial approximations of the time-varying behaviour of the parameters, the proposed technique does require a functional representation of the time-varying behaviour of the parameter estimates. A simulation example and a building systems estimation example are considered to illustrate the developed procedure and ascertain the theoretical results.     

A synthesis of Lyapunov functions for non-linear time-varying control systems

The formulation of a class of Lyapunov functions for time-varying nonlinear control systems which occur in the stability analysis of control systems is considered. A new approach is presented, which is an extension of a generation technique for time-invariant non-linear systems. This approach, which uses by analogy the classical theory of Hamilton, permits stability and transient information to be obtained from system equations with time-varying damping as well as time-varying gain. A second and third-order example is used to illustrate the application of this new result.     

Robust optimization of nonlinear dynamic systems with application to a jacketed tubular reactor

We present robust optimization techniques for dynamic systems which are affected by time-varying uncertainties. After reviewing existing techniques from the field of reachability analysis and ellipsoidal calculus, we discuss how to over-estimate the influence of uncertainty in nonlinear dynamic systems. The corresponding strategies lead to a framework which can be used to solve min–max optimal control problems in a conservative approximation. The technique is illustrated by applying it to a robust optimal control problem for a nonlinear jacketed tubular reactor. Inside this reactor a highly nonlinear and exothermic chemical reaction takes place which is uncertain due to fouling at the reactor wall. We regard safety constraints on the temperature which must be satisfied for all possible scenarios.     

Applying neural networks to on-line updated PID controllers for nonlinear process control

The inherent time-varying nonlinearity and complexity usually exist in chemical processes. The design of control structure should be properly adjusted based on the current state. In this paper, an improved conventional PID control scheme using linearization through a specified neural network is developed to control nonlinear processes. The linearization of the neural network model is used to extract the linear model for updating the controller parameters. In the scheme of the optimal tuning PID controller, the concept of general minimum variance and constrained criterias are also considered. In order to meet most of the practical application problems, several variations of the proposed method, including the momentum filter, the updating criterion and the adjustment of the step size of the control action, are presented to make the proposed algorithm more practical. To demonstrate the potential applications of the proposed strategies, two simulation problems, including a pH neutralization and a batch reactor, are applied.     

Least costly closed-loop performance diagnosis and plant re-identification

The inherent time-varying nature of dynamics in chemical processes often limits the lifetime performance of model-based control systems, as the plant and disturbance dynamics change over time. A critical step in the maintenance of model-based controllers is distinguishing control-relevant plant changes from variations in disturbance characteristics. In this paper, prediction error identification is used to evaluate a hypothesis test that detects if the performance drop arises from control-relevant plant changes. The decision rule is assessed by verifying whether an identified model of the true plant lies outside the set of all plant models that lead to adequate closed-loop performance. A unified experiment design framework is presented in the least costly context (i.e., least intrusion of nominal plant operation) to address the problem of input signal design for performance diagnosis and plant re-identification when the performance drop is due to plant changes. The application of the presented performance diagnosis approach to a (nonlinear) chemical reactor demonstrates the effectiveness of the approach in detecting the cause of an observed closed-loop performance drop based on the designed least costly diagnosis experiment.     

一种基于感光干膜-铟锡氧化物电极的简易细胞阻抗传感器实现细胞形态学和阻抗信息同时检测

加工一种基于感光干膜-铟锡氧化物DFP-ITO( Dry Film Photoresist-Indium Tin Oxide)电极的细胞阻抗生物传感器并实现细胞形态学和阻抗信息同时检测。35μm厚的感光干膜层压在ITO导电玻璃表面上作为绝缘层,通过照相制版技术在感光干膜绝缘层上蚀刻不同直径圆孔;以DFP-ITO作为工作电极,通过夹具和测量小池与Ag/AgCl参比电极、Pt丝对电极相连构成三电极阻抗测量系统;考察了不同直径DFP-ITO工作电极阻抗谱特征;通过细胞粘附实验及细胞毒性实验考察了感光干膜细胞生物相容性;通过光学显微镜和阻抗谱技术分别对接种在DFP-ITO电极上人肺癌细胞株A549粘附、增殖过程中的形
　　态学和阻抗信息进行检测和分析。研究结果发现不同直径DFP-ITO电极具有相似的阻抗特性;充分固化的感光干膜表面适宜A549细胞粘附且无明显的细胞毒性;基于DFP-ITO电极构建的细胞阻抗传感器能够通过光学显微镜获取A549细胞形态学数据,同时通过阻抗谱技术能够解析A549细胞粘附、增殖过程中的细胞质膜电容、细胞-细胞间隙电阻、细胞-ITO电极间隙电阻变化。本文发展了基于DEP-ITO电极的细胞阻抗传感器结构简单,可实现细胞形态学和阻抗信息的双通道获取,未来可用于细胞生理病理学行为和药物细胞毒性研究。