pH过程仿人智能测控算法研究

pH酸碱中和反应过程是一个典型的非线性物理和化学变化过程,难以建立精确的数学模型.本文基于模拟人的pH测控行为、思维方式、经验及分析方式等,利用pH动态过程中的输入和输出所表现出来的相关动态特征,详细设计与分析了适应于pH过程的仿人智能测控算法,以便智能定性推理和自适应定量控制pH过程,使被测对象pH值控制稳定.实验与工程应用表明,仿人智能测控算法比传统的测控算法具有更好的控制效果,是pH过程的有效测控技术之一.

用于型砂质量直接优化控制系统中的测控算法

本文提出一种新型的型砂质量控制方法，即直接优化控制。该方法的基本原理是运用双电源二次激励法，在线快速测定型涛的含水量和有效粘土含量，根据控制算法完成型砂组分加入量的控制，从而实现从成分测试到成分调节的直接控制。

用“黄金分割法”实现某型航空检测仪器的快速测试

针对航空设备地面测试的快速性要求 ,叙述了在航空设备地面检测仪器中应用“黄金分割法”进行快速检测的原理 ,并结合检测设备研究和设计的实践 ,详细说明了用“黄金分割法”实现快速检测的方法。在空军某航空设备检测仪器中使用该方法以后 ,检测效率和精度明显提高。

基于Fibonacci法的某型航空检测仪器的快速测试

针对使用航空测试仪器检查航空设备工作性能以及排除故障时,要求既准确又快速地使航空装备尽快恢复到良好状态的这一基本要求,在开发和研制某型航空地面检测仪器时,在计算机程序设计中采用"Fi-bonacci法"的原理编制测控算法程序,不仅使控制测试的仪器快速地检测航空设备,而且极大地提高了检测的精度。

NEURAL NETWORK PREDICTIVE CONTROL WITH HIERARCHICAL GENETIC ALGORITHM

A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

A finite horizon predictive control algorithm, which applies a saturated feedback control law as its local control law, is presented for nonlinear systems with time-delay subject to input constraints. In the algorithm, N free control moves, a saturated local control law and the terminal weighting matrices are solved by a minimization problem based on linear matrix inequality （LMI） constraints online. Compared with the algorithm with a nonsaturated local law, the presented algorithm improves the performances of the closed-loop systems such as feasibility and optimality. This model predictive control （MPC） algorithm is applied to an industrial continuous stirred tank reactor （CSTR） with explicit input constraint. The simulation results demonstrate that the presented algorithm is effective.

Improved Generalized Predictive Control Algorithm with Offline and Online Identification and Its Application to Fixed Bed Reactor

An improved generalized predictive control algorithm is presented in thispaper by incorporating offline identification into online identification. Unlike the existinggeneralized predictive control algorithms, the proposed approach divides parameters of a predictivemodel into the time invariant and time-varying ones, which are treated respectively by offline andonline identification algorithms. Therefore, both the reliability and accuracy of the predictivemodel are improved. Two simulation examples of control of a fixed bed reactor show that this newalgorithm is not only reliable and stable in the case of uncertainties and abnormal disturbances,but also adaptable to slow time varying processes.

Model predictive control synthesis algorithm based on polytopic terminal region for Hammerstein-Wiener nonlinear systems

An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.

Mixed-Weights Least-Squares Stable Predictive Control Algorithm with Soft and Hard Constraints

Mixed-weight least-squares (MWLS) predictive control algorithm, compared with quadratic programming (QP) method, has the advantages of reducing the computer burden, quick calculation speed and dealing with the case in which the optimization is infeasible. But it can only deal with soft constraints. In order to deal with hard constraints and guarantee feasibility, an improved algorithm is proposed by recalculating the setpoint according to the hard constraints before calculating the manipulated variable and MWLS algorithm is used to satisfy the requirement of soft constraints for the system with the input constraints and output constraints. The algorithm can not only guarantee stability of the system and zero steady state error, but also satisfy the hard constraints of input and output variables. The simulation results show the improved algorithm is feasible and effective.

An improved constrained model predictive control approach for Hammerstein-Wiener nonlinear systems

Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.

SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control

A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

Conventional and Predictive Control Algorithms for Controlling Nonlinear Processes Using Multiple-Model Approach

The objective of this work is to formulate and demonstrate the methodology of multi-models for improving the performance of existing advanced control strategies. Multiple models are used to capture the nonlinear process dynamics relating to gain and time constant variations. The multi-model strategy was implemented on several controllers such as Smith-Predictor using PI （Proportional-lntegral） and GPC （Generalized Predictive Control）. Computer simulations and experiments were conducted on several nonlinear systems and compared to the original form of these controllers. The enhanced approach was tested on controlling the screw speed of an injection molding machine and temperature of a steel cylinder.

Application of clutch to clutch gear shift technology for a new automatic transmission

For the purpose of engineering development for a new 8-step speed automatic transmission,a simplified dynamic model for this gearbox was established and key parameters which affected the shift quality were analyzed.Aiming at four different shift types,the ideal characteristics of shift clutch and engine control were set up.By using torque estimation method,PI slip control algorithm and engine coordinated control principle,the control model and transmission controller were well developed for three shift phases which included rapid-fill phase,torque phase and inertia phase.The testing environment on the rig and prototype vehicle level was built and the testing results obtained in ultimate condition could verify the accuracy and feasibility of this shift control strategy.The peak jerk during shift process was controlled within ±2 g/s where the smooth gearshift was obtained.The development proposal and algorithm have a high value for engineering application.

The IMC Structure of Multi-rate Multivariable Predictive Control Systems and An Improved Algorithm

Multirate multivariable predictive control system with the sampling mechanism that adjusts the plant inputs only once but detects the plant outputs several times during a period is examined. The IMC structure of the system is derived, and its robust stability and zero steady state error characteristics are analyzed. A new control algorithm is developed by adding the variation of the outputs to the index performance. The simulation results show that the method is effective and has zeros steady-state error.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

A fast MPC algorithm for reducing computation burden of MIMO

The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is proposed in this paper to solve this problem, in which real-time values are modulated to bit streams to simplify the multiplication. In addition, manipulated variables in the prediction horizon are deduced to the current control horizon approximately by a recursive relation to decrease the dimension of QR optimization. The simulation results demonstrate the feasibility of this fast algorithm for MIMO systems.

Research of the test generation algorithm based on search state dominance for combinational circuit

On the basis of EST (Equivalent STate hashing) algorithm, this paper researches a kind of test generation algorithm based on search state dominance for combinational circuit. According to the dominance relation of the E-frontier (evaluation frontier), we can prove that this algorithm can terminate unnecessary searching step of test pattern earlier than the EST algorithm through some examples, so this algorithm can reduce the time of test generation. The test patterns calculated can detect faults given through simulation.

Determination of Wood Moisture Properties Using a CT-Scanner in a Controlled High-Temperature Environment

Theaim of the present work wasto analyze moisture flow and moisture content data for high-temperature drying by usingan advanced image- processing algorithm.Since wood starts to shrink below the fibre saturation point during drying, the size and shape of wood will change. The dry wood image was thoroughly transformed to the shape of the wet wood image prior to calculating the dry weight moisture content. The use of the image- processing algorithm for the dry weight moisture content on density data from the CT-scanning during drying in a controlled high-temperature environment showed that this method is a powerful tool for analyzing the moisture flow inside the wood piece. Furthermore, the new CT-scanner together with the climate chamber gave unique results, as it has not been possible to study high-temperature drying with this method before.

Scheme for Implementing Quantum Search Algorithm in a Cluster State Quantum Computer

Using cluster state and single qubit measurement one can perform the one-way quantum computation. Here we give a detailed scheme for realizing a modified Grover search algorithm using measurements on cluster state. We give the measurement pattern for the cluster-state realization of the algorithm and estimated the number of measurement needed for its implementation. It is found that O（2^3n/^2n^2） number of single qubit measurements is required for its realization in a cluster-state quantum computer.