基于BP网络模拟步进电机细分控制优化控制量的方法

用BP网络模拟步进电机细分控制中电机优化控制量与电机角度输出的非线性关系;并对网络结构的确定以及已知实验数据的分析处理作了探讨。对优化控制量模拟的结果表明,BP网络能够实现对这一非线性关系的精确逼近。

航天器多脉冲悬停控制的非线性方法

为解决连续推力空间悬停控制技术对航天器控制推进系统要求较高、工程上难于实现的问题,提出基于Clohessy-Wiltshire方程的多脉冲悬停控制方法。以轨道要素外推的飞行状态非线性预测方法和脉冲悬停控制量优化算法,对悬停脉冲进行了优化,可以实现主动航天器在目标航天器附近任意点的近似稳定悬停。给出的多脉冲悬停控制方法及控制量非线性优化算法考虑了地球非球形引力摄动J2项影响,补偿了Clohessy-Wiltshire方程的线性化误差,能有效提高悬停精度。仿真结果表明,多脉冲悬停控制方法的燃料消耗与连续推力悬停方法相比没有明显增加,不会对主动航天器带来过大的燃料消耗压力。

Optimal control of wireless energy transfer system via decreasing frequency

In order to solve the multiple power extreme value point problem caused by system frequency splitting during wireless energy transmission at short distances a transmission model of the system is established.With the comprehensive consideration of the resonance frequency load parameters and the coupling between coils the internal factors of frequency splitting and boundary conditions are discussed.The results show that under the condition of the fixed load the higher the natural resonance frequency the easier the frequency splitting. As the frequency splitting occurs the frequency of the maximum power transfer is no longer with the natural resonance frequency which can make the system unstable and the transfer power more difficult to control. Therefore a decreasing-frequency method is proposed to avoid the system frequency splitting. And decreasing the system resonance frequency can make the system successfully withdraw the frequency splitting area at a short-distance range.Under the fixed load condition the transmission power of the system can be increased by 400% and the transmission efficiency is reduced by only 14% which greatly improves the transmission performance of the system.

A Method for Surveying Control Network Optimization Based on Reliability Properties

Surveying control network optimization design is related to standards, such as precision, reliability, sensitivity and the cost, and these standards are related closely to each other. A new method for surveying control network simulation optimization design is proposed. This method is based on the inner reliability index of the observation values.

Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms

This paper describes empirical research on the model, optimization and supervisory control of beer fermentation.Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results.The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs).Successful finding of optimal ways to drive these processes were reported.Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.

On Advanced Control Methods toward Power Capture and Load Mitigation in Wind Turbines

This article provides a survey of recently emerged methods for wind turbine control. Multivariate control approaches to the optimization of power capture and the reduction of loads in components under time-varying turbulent wind fields have been under extensive investigation in recent years. We divide the related research activities into three categories： modeling and dynamics of wind turbines, active control of wind turbines, and passive control of wind turbines. Regarding turbine dynamics, we discuss the physical fundamentals and present the aeroelastic analysis tools. Regarding active control, we review pitch control, torque control, and yaw control strategies encompassing mathematical formulations as well as their applications toward different objectives. Our survey mostly focuses on blade pitch control, which is considered one of the key elements in facilitating load reduction while maintaining power capture performance. Regarding passive control, we review techniques such as tuned mass dampers, smart rotors, and microtabs. Possible future directions are suggested.

Optimal distributed resource allocation in a wireless sensor network for control systems

Wireless technology is applied increasingly in networked control systems. A new form of wireless network called wireless sensor network can bring control systems some advantages, such as flexibility and feasibility of network deployment at low costs, while it also raises some new challenges. First, the communication resources shared by all the control loops are limited. Second, the wireless and multi-hop character of sensor network makes the resources scheduling more difficult. Thus, how to effectively allocate the limited communication resources for those control loops is an important problem. In this paper, this problem is formulated as an optimal sampling frequency assignment problem, where the objective function is to maximize the utility of control systems, subject to channel capacity constraints. Then an iterative distributed algorithm based on local buffer information is proposed. Finally, the simulation results show that the proposed algorithm can effectively allocate the limited communication resource in a distributed way. It can achieve the optimal quality of the control system and adapt to the network load changes.

Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes

Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.

The control and optimization of macro/micro-structure of ion conductive membranes for energy conversion and storage

Ion conductive membranes(ICMs)are frequently used as separators for energy conversion and storage technologies of fuel cells,flow battery,and hydrogen pump,because of their good ion-selective conduction and low electronic conductivity.Firstly,this feature article reviews the recent studies on the development of new nonfluorinated ICMs with low cost and their macro/micro-structure control.In general,these new nonfluorinated ICMs have lower conductivity than commercial perfluorinated ones,due to their poor ion transport channels.Increasing ion exchange capacity(IEC)would create more continuous hydrophilic channels,thus enhancing the conductivity.However,high IEC also expands the overall hydrophilic domains,weakens the interaction between polymer chains,enhances the mobility of polymer chains,and eventually induces larger swelling.The micro-scale expansion and macro-scale swelling of the ICMs with high IEC could be controlled by limiting the mobility of polymer chains.Based on this strategy,some ef ficient techniques have been developed,including covalent crosslinking,semi-interpenatrating polymer network,and blending.Secondly,this review introduces the optimization of macro/microstructure of both perfluorinated and nonfluorinated ICMs to improve the performance.Macro-scale multilayer composite is an ef ficient way to enhance the mechanical strength and the dimensional stability of the ICMs,and could also decrease the content of per fluorosulfonic acid resin in the membrane,thereby reducing the cost of the perfluorinated ICMs.Long side chain,multiple functionalization,small molecule inducing micro-phase separation,electrospun nano fiber,and organic–inorganic hybrid could construct more ef ficient ion transport channels,improving the ion conductivity of ICMs.

Particle Swarm Optimization based predictive control of Proton Exchange Membrane Fuel Cell (PEMFC)

Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO re-ceding optimization applied to the PEMFC predictive control yielded good performance.

Multi-objective intelligent coordinating optimization blending system based on qualitative and quantitative synthetic model

A multi-objective intelligent coordinating optimization strategy based on qualitative and quantitative synthetic model for Pb-Zn sintering blending process was proposed to obtain optimal mixture ratio. The mechanism and neural network quantitative models for predicting compositions and rule models for expert reasoning were constructed based on statistical data and empirical knowledge. An expert reasoning method based on these models were proposed to solve blending optimization problem, including multi-objective optimization for the first blending process and area optimization for the second blending process, and to determine optimal mixture ratio which will meet the requirement of intelligent coordination. The results show that the qualified rates of agglomerate Pb, Zn and S compositions are increased by 7.1%, 6.5% and 6.9%, respectively, and the fluctuation of sintering permeability is reduced by 7.0%, which effectively stabilizes the agglomerate compositions and the permeability.

Iterative optimal control based on support vector machine modeling within the Bayesian evidence framework

In the paper, an iterative method is presented to the optimal control of batch processes. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonlinearity, high dimension and local minima, support vector regression models are developed for the optimal control of batch processes where end-point properties are required. The model parameters are selected within the Bayesian evidence framework. Based on the model, an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy. Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.

An Improved Control Vector Iteration Approach for Nonlinear Dynamic Optimization. II. Problems with Path Constraints

This paper considers dealing with path constraints in the framework of the improved control vector iteration （CVI） approach. Two available ways for enforcing equality path constraints are presented, which can be directly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the ll penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reaCtor operation problem are in agreement with the literature reoorts, and the comoutational efficiency is also high.

Review of pre-processing technologies for ice cores

High-resolution ice core records covering long time spans enable reconstruction of the past climatic and environmental conditions allowing the investigation of the earth system's evolution. Preprocessing of ice cores has direct impacts on the data quality control for further analysis since the conventional ice core processing is time-consuming, produces qualitative data, leads to ice mass loss, and leads to risks of potential secondary pollution. However, over the past several decades, preprocessing of ice cores has received less attention than the improvement of ice drilling, the analytical methodology of various indices, and the researches on the climatic and environmental significance of ice core records. Therefore, this papers reviews the development of the processing for ice cores including framework, design as well as materials, analyzes the technical advantages and disadvantages of the different systems. In the past, continuous flowanalysis(CFA) has been successfully applied to process the polar ice cores. However, it is not suitable for ice cores outside polar region because of high level of particles, the memory effect between samples, and the filtration before injection. Ice core processing is a subtle and professional operation due to the fragility of the nonmetallic materials and the random distribution of particles and air bubbles in ice cores, which aggravates uncertainty in the measurements. The future developments of CFA are discussed in preprocessing, memory effect, challenge for brittle ice, coupling with real-time analysis and optimization of CFA in the field. Furthermore, non-polluting cutters with many different configurations could be designed to cut and scrape in multiple directions and to separate inner and outer portions of the core. This system also needs to be coupled with streamlined operation of packaging, coding, and stacking that can be implemented at high resolution and rate, avoiding manual intervention. At the same time, information of the longitudinal sections could be scanned andidentified, and then classified to obtain quantitative data. In addition, irregular ice volume and weight can also be obtained accurately. These improvements are recorded automatically via user-friendly interfaces. These innovations may be applied to other paleomedias with similar features and needs.

Sensing Time Optimization in Energy-Harvesting Cognitive Radio with Interference Rate Control

In this paper,an energy-harvesting cognitive radio(CR) is considered,which allows the transmitter of the secondary user(SU) to harvest the primary signal energy from the transmitter of the primary user(PU) when the presence of the PU is detected.Then the harvested energy is converted into the electrical power to supply the transmission of the SU at the detected absence of the PU.By adopting the periodic spectrum sensing,the average total transmission rate of the SU is maximized through optimizing the sensing time,subject to the constraints of the probabilities of false alarm and detection,the harvested energy and the interference rate control.The simulation results show that there deed exists an optimal sensing time that maximizes the transmission rate,and the maximum transmission rate of the energy-harvesting CR can better approach to that of the traditional CR with the increasing of the detection probability.

A new digital approach to design multivariable robust optimal control systems

This paper presents a new design of robust optimal controller for multivariable system. The row characteristic functions of a linear multivariable system and dynamic decoupling of its equivalent system, were applied to change the transfer function matrix of a closed-loop system into a normal function matrix, so that robust H^∞ optimal stability is guaranteed. Furthermore, for the decoupled equivalent control system the I^∞ optimization approach is used to have the closed-loop system embody optimal time domain indexes. A successful application on a heater control system verified the excellence of the new control scheme.

An Improved Control Vector Iteration Approach for Nonlinear Dynamic Optimization (Ⅰ) Problems Without Path Constraints

This study proposes an efficient indirect approach for general nonlinear dynamic optimization problems without path constraints. The approach incorporates the virtues both from indirect and direct methods: it solves the optimality conditions like the traditional indirect methods do, but uses a discretization technique inspired from direct methods. Compared with other indirect approaches, the proposed approach has two main advantages: (1) the discretized optimization problem only employs unconstrained nonlinear programming (NLP) algorithms such as BFGS (Broyden-Fletcher-Goldfarb-Shanno), rather than constrained NLP algorithms, therefore the computational efficiency is increased; (2) the relationship between the number of the discretized time intervals and the integration error of the four-step Adams predictor-corrector algorithm is established, thus the minimal number of time intervals that under desired integration tolerance can be estimated. The classic batch reactor problem is tested and compared in detail with literature reports, and the results reveal the effectiveness of the proposed approach. Dealing with path constraints requires extra techniques, and will be studied in the second paper.

Optimization of the CBSMAP Queueing Model

The present paper is devoted to the research of controlled queueing models at control of CBSMAP （Controlled Batch Semi-Markov Arrival Process）. The control is based on the theory of controlled semi-markov processes for system optimization. The control is carried out using a type of the next batch and moments of batch arrivals.

Enhanced exergy cost optimization of operating conditions in FCCU main fractionator

Exergy indicates the maximal energy that can do work effectively. Different from optimization of product quality or calculation of generic energy conservation in most previous studies, the application of exergy analysis and exergy cost optimization in petrochemical industry is of great economic and environmental significance. Based on the main fractionator in Jiujiang Petrochemical Complex No. 2 FCCU, an enhanced exergy cost optimization under different operating conditions by adjusting set points of temperature and valves opening degree for flow control is studied in this paper in order to reduce exergy cost and improve the quality of energy. A steadystate optimization algorithm to enhance exergy availability and an objective function comprehensively considering exergy loss are proposed. On the basis of ensuring the quality of petroleum products, the economic benefits can be improved by optimizing the controllable variables due to the fact that exergy cost is decreased.

Simultaneous optimization and control for polypropylene grade transition with two-layer hierarchical structure

In this paper,a two-layer hierarchical structure of optimization and control for polypropylene grade transition was raised to overcome process uncertain disturbances that led to the large deviation between the open-loop reference trajectory and the actual process.In the upper layer,the variant time scale based control vector parametric methods(VTS-CVP) was used for dynamic optimization of transition reference trajectory,while nonlinear model predictive controller(NMPC) based on closed-loop subspace and piece-wise linear(SSARX-PWL) model in the lower layer was tracking to the reference trajectory from the upper layer for overcoming high-frequency disturbances.Besides,mechanism about trajectory deviation detection and optimal trajectory updating online were introduced to ensure a smooth transition for the entire process.The proposed method was validated with the real data from an industrial double-loop propylene polymerization reaction process with developed dynamic mechanism mathematical model.