遗传算法—神经网络结构控制系统研究

提出了将遗传算法及神经网络应用于结构主动控制的新方法。该方法利用遗传算法在线计算控制力,利用神经网络模拟结构的动力特性,从而代替结构进行动力分析。该系统充分发挥了遗传算法及神经网络各自的特点,是非常具有发展前途的新型的主动控制系统。     

Dynamic optimization of chemical engineering problems using a control vector parameterization method with an iterative genetic algorithm

An approach that combines genetic algorithm (GA) and control vector parameterization (CVP) is proposed to solve the dynamic optimization problems of chemical processes using numerical methods. In the new CVP method, control variables are approximated with polynomials based on state variables and time in the entire time interval. The iterative method, which reduces redundant expense and improves computing efficiency, is used with GA to reduce the width of the search region. Constrained dynamic optimization problems are even more difficult. A new method that embeds the information of infeasible chromosomes into the evaluation function is introduced in this study to solve dynamic optimization problems with or without constraint. The results demonstrated the feasibility and robustness of the proposed methods. The proposed algorithm can be regarded as a useful optimization tool, especially when gradient information is not available.     

基于遗传算法策略的含时滞结构振动主动控制研究

该文提出一种新的含时滞结构振动控制方法-基于遗传算法优化反馈控制增益的控制方法,该方法以时滞微分动力学方程为依据,以遗传算法为优化工具,然后根据特定的优化控制目标实现控制增益的求解.遗传算法优化控制增益的方法具有以下优点:其一,由于优化所得的控制增益充分考虑了时滞对结构振动控制的影响,因而能够有效地解决由于时滞的存在使...     

基于遗传算法的结构主动控制

提出了应用遗传算法进行结构主动控制的新方法，该方法能够在线计算控制力，对结构实施控制，并提出了解决该控制系统中时间滞后问题的方法，为该控制系统在实际中的应用打下了基础。     

Closed loop control-based real-time dispatching heuristic on parallel batch machines with incompatible job families and dynamic arrivals

In this paper, a real-time closed loop control dispatching heuristic (RCLC) algorithm is proposed to address the scheduling problem of parallel batch machines with incompatible job families, limited waiting time constraints, re-entrant flow and dynamic arrivals in the diffusion and oxidation areas of a semiconductor wafer fabrication system (SWFS), which is known to be strongly NP-hard. The basis of this algorithm is the information of lots in the buffer when the parallel batch machines are idle and available. In RCLC, if the number of any family lots is less than the maximum batch size, the dispatching heuristic can be seen as a pull–pull–push–push (P⁴) strategy; otherwise, a genetic algorithm (GA). A look-itself strategy, P⁴ strategy and GA can build a closed loop control system. The experiments are implemented on the Petri nets-based real-time scheduling simulation platform of SWFS, and demonstrate the effectiveness of our proposed method.     

基于遗传算法的模糊神经控制及其应用

将遗传算法和模糊神经网络相结合，提出了一类智能控制方案，仿真系统和实际温控表明，这类智能控制器可改善具有时变、非线性及大纯滞后系统的控制品质，其性能优于一般模糊控制。     

Application of genetic algorithm to the observer‐based controller design for neutral systems

Abstract

In this paper, the dynamic observer‐based controller design for a class of neutral systems with known and uncertain time delays is considered. Delay‐dependent and delay‐independent stabilizability criteria are proposed to guarantee the stability for the feedback control systems. Linear matrix inequality (LMI) and genetic algorithm (GA) are used to design the observer‐based control. Design procedure for the observer‐based control is provided. A numerical example is given to illustrate our results.     

Pneumatic motor speed control by trajectory tracking fuzzy logic controller

In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is defined to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to find the TTFLC boundary values of membership functions (MF) and weights of control rules. In addition, artificial neural networks (ANN) modelled dynamic behaviour of PM is given. This ANN model is used to find the optimal TTFLC parameters by offline GA approach. The experimental results show that designed TTFLC successfully enables the PM speed track the given trajectory under various working conditions. The proposed approach is superior to PID controller. It also provides simple and easy design procedure for the PM speed control problem.     

一种封闭空间自适应有源噪声控制系统优化方法

将遗传算法（GA）引人封闭空间自适应有原噪声控制（AANC）系统优化设计,以解决长期以来缺乏有效的确定封闭空间次级声源和误差传感器最优位置方法的问题。以小阻尼矩形空间为例,进行了计算机仿真。结果证明：此方法对封闭空间AANC系统优化是有效的。     

Scheduling semiconductor in-line steppers in new product/process introduction scenarios

This paper presents a scheduling method for an in-line stepper operating in a new process/production introduction (NPI) scenario. An in-line stepper is a bottleneck machine in a semiconductor fab. Its interior is comprised of a series of chambers, while its exterior is a dock equipped with a limited number of ports. The transportation unit for each chamber is a piece of wafer, while that for each port is a job that can contain up to 25 wafers. This transportation incompatibility may lead to an unexpected capacity loss for an in-line stepper–in particular in an NPI scenario that, by nature, includes a substantial number of small-sized jobs. Such a capacity loss can be alleviated by effective scheduling. A genetic algorithm (GA) scheduling method is proposed to enhance the productivity of in-line steppers. Four other sequencing methods are compared with the GA method. Numeric experiments indicate that the GA method outperforms the four benchmarks. The higher the percentage of small-sized jobs, the better the performance of the GA.     

简支板振动无线主动控制及驱动器优化布置研究

推导光电层合简支板结构动力学方程及模态控制方程,以规格化后模态控制力指数作为遗传算法的适应度函数,基于二进制编码的遗传算法对用于简支板振动控制的单对、双对光致伸缩驱动器布局进行优化,计算机仿真结果表明优化后的驱动器布局方案可有效提高板结构振动控制的有效性。在此基础上进一步对板结构多模态振动控制进行探讨,提出适用于板结构多模态振动控制的驱动器布局优化方法及振动控制方案,仿真算例表明该方法可有效地对简支板前二阶模态进行振动无线控制。     

Implementation of feedback-linearization-modelled induction motor drive through an adaptive simplified neuro-fuzzy approach

A simple modified version of neuro-fuzzy controller (NFC) method based on single-input, reduced membership function in conjunction with an intuitive flux–speed decoupled feedback linearization (FBL) approach of induction motor (IM) model is presented in this paper. The proposed NFC with FBL remarkably suppresses the torque and speed ripple and shows improved performance. Further, the modified NFC is tuned by genetic algorithm (GA) approach for optimal performance of FBL-based IM drive. Moreover, the GA searches the optimal parameters of the simplified NFC in order to ensure the global convergence of error. The proposed simplified NFC integrates the concept of fuzzy logic and neural network structure like a conventional NFC, but it has the advantages of simplicity and improved computational efficiency over the conventional NFC as the single input introduced here is an error (speed and torque) instead of two inputs, error and change in error, as in the conventional NFC. This structure makes the proposed NFC robust and simple as compared with conventional NFC and thus, can be easily applied to real-time industry application. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. The effectiveness of the proposed method using FBL of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using the proposed simple NFC as compared to the conventional NFC; rather, it shows superior performance over PI-controller-based drive.     

Genetic algorithm based optimal design for vibration control of composite shell structures using piezoelectric sensors and actuators

The present article deals with the design of optimal vibration control of smart fiber reinforced polymer (FRP) composite shell structures using genetic algorithm (GA) based linear quadratic regulator (LQR) and layered shell coupled electro-mechanical finite element analysis. Open loop procedure has been used for optimal placement of actuators considering the control spillover of the higher modes to prevent closed loop instability. An improved real coded GA based LQR control scheme has been developed for designing an optimal controller in order to maximize the closed loop damping ratio while keeping actuators voltages within limit. Results show that increased closed loop-damping has been achieved with a large reduction of control effort considering control spillover.     

Economic and Economic‐Statistical Designs of Phase II Profile Monitoring

In economic design of profiles, parameters of a profile are determined such that the total implementation cost is minimized. These parameters consist of the number of set points, n, the interval between two successive sampling, h, and the parameters of a control chart used for monitoring. In this paper, the Lorenzen–Vance cost function is extended to model the costs associated with implementing profiles. The in‐control and the out‐of‐control average run lengths, ARL₀ and ARL₁, respectively, are used as two statistical measures to evaluate the statistical performances of the proposed model. A genetic algorithm (GA) is developed for solving both the economic and the economic‐statistical models, where response surface methodology is employed to tune the GA parameters. Results indicate satisfactory statistical performance without much increase in the cost of implementation. Copyright © 2013 John Wiley & Sons, Ltd.     

基于数控加工的熔融沉积成型精度控制

目的 为了提高零件熔融成型精度,提高零件的加工质量.方法 提出基于数控加工的熔融沉积成型精度控制方法,根据零件图纸利用计算机实现加工代码的自动编制,通过熔融沉积成型精度自学习,准确生成熔融沉积成型微调值,进而实现熔融沉积成型精度控制.结果 在相同的测试时间内,设计控制方法的控制偏度系数比对照组的低.结论 设计的控制方法能够确保熔融沉积成型精度.     

Scheduling trucks in container terminals using a genetic algorithm

Trucks are the most popular transport equipment in most mega-terminals, and scheduling them to minimize makespan is a challenge that this article addresses and attempts to resolve. Specifically, the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times is investigated, and the use of a genetic algorithm (GA) to address the scheduling problem is proposed. The scheduling problem is formulated as a mixed integer program. It is noted that the scheduling problem is NP-hard and the computational effort required to solve even small-scale test problems is prohibitively large. A crossover scheme has been developed for the proposed GA. Computational experiments are carried out to compare the performance of the proposed GA with that of GAs using six popular crossover schemes. Computational results show that the proposed GA performs best, with its solutions on average 4.05% better than the best solutions found by the other six GAs.     

An enhanced genetic algorithm for structural topology optimization

Genetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problem‐specific knowledge. The original discrete black‐and‐white (0–1) problem is directly solved by using a bit‐array representation method. To address the related pronounced connectivity issue effectively, the four‐neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well‐posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge‐free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM‐based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near‐optimum solutions and viable topologies with checkerboard free, mesh independent and hinge‐free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem‐specific knowledge can be a powerful global search tool for structural topology optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.     

Investigating the optimal passive and active vibration controls of adjacent buildings based on performance indices using genetic algorithms

This study proposes the optimal passive and active damper parameters for achieving the best results in seismic response mitigation of coupled buildings connected to each other by dampers. The optimization to minimize the H₂ and H_∞ norms in the performance indices is carried out by genetic algorithms (GAs). The final passive and active damper parameters are checked for adjacent buildings connected to each other under El Centro NS 1940 and Kobe NS 1995 excitations. Using real coded GA in H_∞ norm, the optimal controller gain is obtained by different combinations of the measurement as the feedback for designing the control force between the buildings. The proposed method is more effective than other metaheuristic methods and more feasible, although the control force increased. The results in the active control system show that the response of adjacent buildings is reduced in an efficient manner.