基于隐性记忆的非平稳时变污水处理过程多目标运行优化

针对污水处理过程中运行能耗大和水质超标严重等问题,基于隐性记忆策略,提出一种适用于无规律且非平稳时变过程的多目标运行优化方法.首先,采用集成即时学习在线建模算法建立运行指标模型,实现时变工况下运行优化目标的准确描述;然后,提出基于隐性记忆的多目标优化算法,通过充分考虑复杂时变工况,挖掘埋藏在历史优化信息中的结构化知识,引导进化搜索过程,并结合平均距离聚类指标进行均匀随机局部搜索,提高算法收敛性、多样性和分布性;最后,利用基于集成即时学习的智能决策方法,获取操作变量溶解氧浓度和硝态氮浓度的优化设定值并进行跟踪控制.数据实验表明:所提出方法能够实现污水处理过程的稳定运行,在满足操作限制的情况下,提高污染物去除性能,降低运行成本.     

Multiobjective hierarchical control architecture for greenhouse crop growth

The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.     

Optimal trajectory planning for robots under the consideration of stochastic parameters and disturbances

Efficient control strategies of robots should cause only low on-line correction expenses. Hence, the mostly available statistical and a priori informations about the random parameters and disturbances of the underlying mechanical system and its environment should be considered already for off-line programming of robots. Measuring the violations of the basic mechanical conditions by means of expected penalty costs, a stochastic optimization problem is obtained for the computation of an optimal open-loop control. The stochastic optimization problem can be solved — after discretization — by parameter optimization.     

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation. In order to improve the operating energy utilization rate of trains, a multi-objective particle swarm optimization (MPSO) algorithm with energy consumption, punctuality and parking accuracy as the objective and safety as the constraint is built. To accelerate its the convergence process, the train operation progression is divided into several modes according to the train speed-distance curve. A human-computer interactive particle swarm optimization algorithm is proposed, which presents the optimized results after a certain number of iterations to the decision maker, and the satisfactory outcomes can be obtained after a limited number of adjustments. The multi-objective particle swarm optimization (MPSO) algorithm is used to optimize the train operation process. An algorithm based on the important relationship between the objective and the preference information of the given reference points is suggested to overcome the shortcomings of the existing algorithms. These methods significantly increase the computational complexity and convergence of the algorithm. An adaptive fuzzy logic system that can simultaneously utilize experience information and field data information is proposed to adjust the consequences of off-line optimization in real time, thereby eliminating the influence of uncertainty on train operation. After optimization and adjustment, the whole running time has been increased by 0.5 s, the energy consumption has been reduced by 12%, the parking accuracy has been increased by 8%, and the comprehensive performance has been enhanced.     

Multi-objective real-time dispatching for integrated delivery in a Fab using GA based simulation optimization

In a wafer fabrication Fab, the “integrated delivery”, which integrates the automated material handling system (AMHS) with processing tools to automate the material flow, is difficult to implement due to the system complexity and uncertainty. The previous dispatching studies in semiconductor manufacturing have mainly focused on the tool dispatching. Few studies have been done for analyzing combinatorial dispatching rules including lot dispatching, batch dispatching and automated guided vehicle (AGV) dispatching. To handle this problem, a GA (genetic algorithm) based simulation optimization methodology, which consists of the on-line scheduler and the off-line scheduler, is presented in this paper. The on-line scheduler is used to monitor and implement optimal combinatorial dispatching rules to the semiconductor wafer fabrication system. The off-line scheduler is employed to search for optimal combinatorial dispatching rules. In this study, the response surface methodology is adopted to optimize the GA parameters. Finally, an experimental bay of wafer fabrication Fab is constructed and numerical experiments show that the proposed approach can significantly improve the performance of the “integrated delivery system” compared with the traditional single dispatching rule approach.     

Plantwide control: the search for the self-optimizing control structure

Plantwide control is concerned with the structural decisions involved in the control system design of a chemical plant (C.S. Foss, Critique of chemical process control theory, AIChE Journal 19(2), 1973) 209–214; “Which variables should be controlled, which variables should be measured, which inputs should be manipulated, and which links should be made between them?” In particular, the first issue about which variables to control has received little attention. It is argued that the answer is related to finding a simple and robust way of implementing the economically optimal operating policy. The goal is to find a set of controlled variables which, when kept at constant setpoints, indirectly lead to near-optimal operation with acceptable loss. This is denoted “self-optimizing” control. Since the economics are determined by the overall plant behavior, it is necessary to take a plantwide perspective. A systematic procedure for finding suitable controlled variables based on only steady-state information is presented. Important steps are degree of freedom analysis, definition of optimal operation (cost and constraints), and evaluation of the loss when the controlled variables are kept constant rather than optimally adjusted. A case study yields very interesting insights into the control and maximum throughput of distillation columns.     

Robust Practical Point Stabilization of a Nonholonomic Mobile Robot Using Neural Networks

A control structure that makes possible the integration of a kinematiccontroller and a neural network (NN) computed-torque controller fornonholonomic mobile robots is presented. A combined kinematic/torque controllaw is developed and stability is guaranteed by Lyapunov theory. Thiscontrol algorithm is applied to the practical point stabilization problemi.e., stabilization to a small neighborhood of the origin. The NN controllercan deal with unmodeled bounded disturbances and/or unstructured unmodeleddynamics in the vehicle. On-line NN weight tuning algorithms that do notrequire off-line learning yet guarantee small tracking errors and boundedcontrol signals are utilized.     

基于改进人工蜂鸟算法的VRV空调需求响应功率削减策略

针对夏季用电高峰时期用户对空调设定温度随意调节造成能源浪费以及需求侧对电网控制指令响应不够精确的问题，提出了一种基于功率削减的空调温度分档需求响应调控策略；以某办公建筑VRV空调为研究对象，分别建立该办公建筑空调物理仿真模型以及功耗数学模型，并对模型的准确性进行验证；提出基于不同舒适度和激励电价的VRV空调温度控制档位，构建室内机温度分档调控多目标优化模型，优化目标为调控时期空调实际功率与调控目标值的平均偏差以及负荷聚合商对用户的激励补偿费用同时最小；选取人工蜂鸟算法作为优化算法，针对该算法存在搜索速度慢、寻优精度低、易早熟收敛等缺点，在种群初始化阶段采用Hammersley序列生成更加均匀的初始种群以提高算法的收敛速度与精度，在搜索阶段采用高斯变异算子对蜂鸟位置进行扰动以进一步提升算法的探索能力。运用改进人工蜂鸟算法对模型进行求解，并与人工蜂鸟算法、粒子群算法、灰狼优化算法和鲸鱼优化算法的求解结果进行对比，以证明所提策略的有效性；实验结果表明，应用改进人工蜂鸟算法求解后的结果在保证用户舒适度的条件下最多可将功率调控精度提高83.1%并且将激励费用减小8.36%。     

Comparison of three control strategies for optimization of spray dryer operation

Spray drying is the preferred process to reduce the water content of many chemicals, pharmaceuticals, and foodstuffs. A significant amount of energy is used in spray drying to remove water and produce a free flowing powder product. In this paper, we present and compare the performance of three controllers for operation of a four-stage spray dryer. The three controllers are a proportional-integral (PI) controller that is used in industrial practice for spray dryer operation, a linear model predictive controller with real-time optimization (MPC with RTO, MPC-RTO), and an economically optimizing nonlinear model predictive controller (E-NMPC). The MPC with RTO is based on the same linear state space model in the MPC and the RTO layer. The E-NMPC consists of a single optimization layer that uses a nonlinear system of ordinary differential equations for its predictions. The PI control strategy has a fixed target that is independent of the disturbances, while the MPC-RTO and the E-NMPC adapt the operating point to the disturbances. The goal of spray dryer operation is to optimize the profit of operation in the presence of feed composition and ambient air humidity variations; i.e. to maximize the production rate, while minimizing the energy consumption, keeping the residual moisture content of the powder below a maximum limit, and avoiding that the powder sticks to the chamber walls. We use an industrially recorded disturbance scenario in order to produce realistic simulations and conclusions. The key performance indicators such as the profit of operation, the product flow rate, the specific energy consumption, the energy efficiency, and the residual moisture content of the produced powder are computed and compared for the three controllers. In this simulation study, we find that the economic performance of the MPC with RTO as well as the E-NMPC is considerably improved compared to the PI control strategy used in industrial practice. The MPC with RTO improves the profit of operation by 8.61%, and the E-NMPC improves the profit of operation by 9.66%. The energy efficiency is improved by 6.21% and 5.51%, respectively.     

An ellipsoidal off-line MPC scheme for uncertain polytopic discrete-time systems

An off-line Model Predictive Control (MPC) method based on ellipsoidal calculus and viability theory is described in order to address control problems in the presence of state and input constraints for uncertain polytopic linear plants subject to persistent disturbances. In order to reduce the computational burdens and conservativeness of traditional polytopic MPC schemes, the present approach carries out off-line most of the computations and it makes use of closed-loop predictions to improve the control performance. This is done by recursively pre-computing suitable ellipsoidal inner approximations of the exact controllable sets and solving on-line a simple and numerically low-demanding optimization problem subject to a set-membership constraint. Comparisons with three other recent off-line MPC approaches are also provided in the final example.     

基于神经网络的一类生化反应器状态在线估计

提出一种随机非线性系统状态和参数同时估计的神经网络新方法，并证明了该方法的无偏性和是小方差性，将其用于乙醇间歇发酵器的状态和参数估计，结果表明估计值民实验值相吻合，此方法对噪声特片无特殊要求，对初始状态估值不敏感，对初始参数值具有一定的鲁棒性，可利用有限的状态量测信息在线估计不可测量的状态变量和物理参数。     

动态电源管理的随机切换模型与策略优化

提出一种基于连续时间Markov决策过程的动态电源管理策略优化方法.通过建立动态电源管理系统的随机切换模型,将动态电源管理问题转化为带约束的策略优化问题,并给出一种基于矢量合成的策略梯度优化算法.随机切换模型对动态电源管理系统的描述精确,策略优化算法简便有效,既能离线计算,也适用于在线优化.仿真实验验证了该方法的有效性.     

An approach to mitigate the risk of voltage collapse accounting uncertainties using improved particle swarm optimization

This paper describes a new viewpoint for static voltage stability enhancement based on an improved particle swarm optimization technique. The objective function is selected for maximization of reactive power reserve subjected to usual operating constraints at an operating point. Probabilistic risk of voltage collapse has been used for maintaining desired level of voltage stability margin. This risk of voltage collapse is calculated accounting uncertainties in system parameters and control variables. Probabilistic risk of voltage collapse has been obtained by a trained Radial Basis Function network. Developed algorithm has been implemented on 6-bus, 14-bus and 25-bus IEEE test systems. Results have been compared with those obtained using Davidon–Fletcher–Powell's (DFP) method.     

Size optimization of space trusses using Big Bang–Big Crunch algorithm

A Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm is employed for optimal design of truss structures. HBB–BC is compared to Big Bang–Big Crunch (BB–BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB–BC method compared to other heuristic algorithms.     

A Fuzzy Adaptive Differential Evolution Algorithm

The differential evolution algorithm is a floating-point encoded evolutionary algorithm for global optimization over continuous spaces. The algorithm has so far used empirically chosen values for its search parameters that are kept fixed through an optimization process. The objective of this paper is to introduce a new version of the Differential Evolution algorithm with adaptive control parameters – the fuzzy adaptive differential evolution algorithm, which uses fuzzy logic controllers to adapt the search parameters for the mutation operation and crossover operation. The control inputs incorporate the relative objective function values and individuals of the successive generations. The emphasis of this paper is analysis of the dynamics and behavior of the algorithm. Experimental results, provided by the proposed algorithm for a set of standard test functions, outperformed those of the standard differential evolution algorithm for optimization problems with higher dimensionality.     

A new mutation operator for evolutionary airfoil design

A new mutation operator, ℳ _ijn , capable of operating on a set of adjacent bits in one single step, is introduced. Its features are examined and compared against those of the classical bit–flip mutation. A simple Evolutionary Algorithm, ℳ–EA, based only on selection and ℳ _ijn , is described. This algorithm is used for the solution of an industrial problem, the Inverse Airfoil Design optimization, characterized by high search time to achieve satisfying solutions, and its performance is compared against that offered by a classical binary Genetic Algorithm. The experiments show for our algorithm a noticeable reduction in the time needed to reach a solution of acceptable quality, thus they prove the effectiveness of the proposed operator and its superiority to GAs for the problem at hand.     

一种基于免疫机理的故障检测方法

对Castro和T imm is提出的带变异的阴性选择算法进行适当改进,用于故障模式的离线训练。对Cas-tro和Zuben提出的aiN et免疫网络算法进行适当改进,并用于故障的在线检测。实验表明,由于采用了聚类的思想,构造的自我集和非我集能较全面反映系统在正常与不正常两种模式下的全貌,提高了在线检测准确率。     

汽油调合累积过程在线控制与优化

汽油调合具有非连续批次性的特点,并且调合头产品在罐中存在累积过程。已有汽油调合在线配方优化针对的是瞬时调合头产品而非罐中产品,无法解决罐底补偿问题,造成罐产品性质与实际目标性质存在偏差,调合生产经济效益进一步提高受到限制。为解决这一问题,文中采用具有收缩时域的模型预测全局滚动优化方式,兼顾质量过剩最小与经济效益最大化两方面要求,提出累积调合过程在线控制与优化算法。在瞬时产品配方优化层的基础上,通过增加瞬时产品目标值计算层,形成二级递阶结构。应用案例结果:证明该算法能用于累积调合过程,能兼顾罐产品质量和调合经济效益最大化,能够有效地解决罐底补偿问题。     

Control of a nonholomic mobile robot: Backstepping kinematics into dynamics

A dynamical extension that makes possible the integration of a kinematic controller and a torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping, and asymptotic stability is guaranteed by Lyapunov theory. Moreover, this control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. The result is a general structure for controlling a mobile robot that can accommodate different control techniques, ranging from a conventional computed-torque controller, when all dynamics are known, to robust-adaptive controllers if this is not the case. A robust-adaptive controller based on neural networks (NNs) is proposed in this work. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. On-line NN weight tuning algorithms that do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized. © 1997 John Wiley & Sons, Inc.     

Optimal operation of Petlyuk distillation: steady-state behavior

The “Petlyuk” or “dividing-wall” or “fully thermally coupled” distillation column is an interesting alternative to the conventional cascaded binary columns for separation of multi-component mixtures. However, the industrial use has been limited, and difficulties in operation have been reported as one reason. With three product compositions controlled, the system has two degrees of freedom left for on-line optimization. We show that the steady-state optimal solution surface is quite narrow, and depends strongly on disturbances and design parameters. Thus it seems difficult to achieve the potential energy savings compared to conventional approaches without a good control strategy. We discuss candidate variables which may be used as feedback variables in order to keep the column operation close to optimal in a “self-optimizing” control scheme.