Application of particle swarm optimization and robust net present value for BOT-type contracts

Major infrastructure construction projects contracted to private companies by governments are important for maximizing profitability. This paper extends an existing build–operate–transfer (BOT) concession model (BOTCcM) for identifying the reasonable concession period which would be profitable both to the government and to the private sector. There are some major limitations with BOTCcM – for example, the total investment cost is pre-given and the impact of uncertainty of parameters affecting the concession period were not considered. In this research, the total investment cost is assumed as variable which should be optimally determined and the uncertainty of net cash flows is considered. Further, the proposed model is implemented to calculate the robust concession period and required capital for the construction period, using the obtained values and particle swarm optimization method.     

船舶自适应操舵仪新算法的研究

该文根据传统ＰＩＤ自动操舵仪的不足，提出二种应用辨识与自适应滤波技术、采用一步最优控制律、实现最优操舵的自适应操舵新算法。该算法立足于获取较精确的船舶航向运动数学模型，因此，可望取得较好的控制鲁棒性。     

Dynamic control of a remotely operated vehicle using a neural network

The control of a remotely-operated underwater vehicle to maintain a prescribed depth in shallow water under irregular surface waves is realized through the application of the Robust Adaptive Neuro Controller, a composite control system incorporating—together with the conventional control algorithm—a neural network controller. This network bestows a learning capability on the system, allowing it to deal with unanticipated disturbances that would otherwise cause erroneous behavior of the vehicle. The effectiveness of this application is verified through mathematical simulation of a model vehicle's behavior, through experiment in a model basin, and through simulation of the behavior of an actual remotely operated vehicle in shallow water under irregular surface waves. Graphic data representing the learning process undergone by the neural network distinctly indicate the rising output from the network with the progression of learning, and the vehicle's depth variation traced in terms of the mean square error vividly show the diminution of deviation from the prescribed depth obtained with application of the neural network. Thus controlled to maintain constant depth, under-water vehicles with power supplied externally through a tether for propulsion and for heavy-duty operations should consolidate their advantage for such activities as maintenance of submarine structures and surveys in deep or hazardous water.     

大型浮船坞浮态鲁棒控制分析

以某30000 t举力浮船坞为研究对象,为更好地掌握其横摇运动的特性,分析浮态变化规律。根据浮船坞自身的特点,利用鲁棒控制原理,对其横摇减摇系统进行设计。并且基于灵敏度极小化的思想,对浮船坞横摇减摇控制系统进行仿真。通过分析得到,减摇控制系统对海浪干扰的抑制能力能够达到设定的干扰抑制要求。     

Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control

We propose a two-stage, on-line signal control strategy for dynamic networks using a linear decision rule (LDR) approach and a distributionally robust optimization (DRO) technique. The first (off-line) stage formulates a LDR that maps real-time traffic data to optimal signal control policies. A DRO problem is solved to optimize the on-line performance of the LDR in the presence of uncertainties associated with the observed traffic states and ambiguity in their underlying distribution functions. We employ a data-driven calibration of the uncertainty set, which takes into account historical traffic data. The second (on-line) stage implements a very efficient linear decision rule whose performance is guaranteed by the off-line computation. We test the proposed signal control procedure in a simulation environment that is informed by actual traffic data obtained in Glasgow, and demonstrate its full potential in on-line operation and deployability on realistic networks, as well as its effectiveness in improving traffic.     

Short-term work team scheduling models for effective road repair and management

Abstract

In this study, we focus on the development of work team routing/scheduling models incorporating stochastic travel and repair times. Robust and expected optimization concepts, combined with a time–space network technique, are used to develop the models. We perform numerical tests based on operational data for Taoyuan County in Taiwan. The test results show the good performance of the models.     

考虑时滞作用下的车辆主动座椅悬架鲁棒H∞控制

利用鲁棒H∞控制理论为车辆座椅悬架模型设计输出反馈控制器,并且考虑了模型的不确定性和控制输入的时滞问题.建立了车辆座椅的三自由度运动微分方程,并转化为包括参数变化和控制延迟的状态方程和包括控制指标在内的控制模型.分析了系统可通过输出反馈鲁棒镇定的充分条件,并将该充分条件转化为线性矩阵不等式（LMI）问题,通过Matlab求解出用LMI构造的输出反馈控制律.建立仿真模型并以白噪声作为路面激励与被动控制、PID控制对比验证了在路面输入扰动作用下鲁棒控制器的有效性.     

Application of H∞ control in rudder/flap vector robust control of a ship''s course

Given the uncertainty of parameters and the random nature of disturbances that effect a ships course, a robust course controller should be designed on the basis of rudder/flap vector control. This paper analyzes system uncertainty, and the choice of weighting functions is also discussed. When sea waves operate on a ship, the energy-concentrating frequency varies with the angle of encounter. For different angles of encounter, different weighting functions are designed. For the pole of a nominal model existing in an imaginary axis, the bilinear-transform method is used. The "2-Riccati" equation is adopted to solve the H∞ controller. A system simulation is given, and the results show that, compared with a PID controller, this system has higher course precision and more robust performance. This research has significant engineering value.     

Application of H~∞ control in rudder/flap vector robust control of a ship's course

Given the uncertainty of parameters and the random nature of disturbances that effect a ships course, a robust course controller should be designed on the basis of rudder/flap vector control. This paper analyzes system uncertainty, and the choice of weighting functions is also discussed. When sea waves operate on a ship, the energy-concentrating frequency varies with the angle of encounter. For different angles of encounter, different weighting functions are designed. For the pole of a nominal model existing in an imaginary axis, the bilinear-transform method is used. The "2-Riccati" equation is adopted to solve the H∞ controller. A system simulation is given, and the results show that, compared with a PID controller, this system has higher course precision and more robust performance. This research has significant engineering value.     

Linear matrix inequality based control of vehicle active suspension system

Linear matrix inequality (LMI) methods, novel techniques in solving optimisation problems, were introduced as a unified approach for vehicle's active suspension system controller design. LMI methods were used to provide improved and computationally efficient controller design techniques. The active suspension problem was formulated as a standard convex optimisation problem involving LMI constraints that can be solved efficiently using recently developed interior point optimisation methods. An LMI based controller for a vehicle system was developed. The controller design process involved setting up an optimisation problem with matrix inequality constraints. These LMI constraints were derived for a vehicle suspension system. The resulting LMI controller was then tested on a quarter-car model using computer simulations. The LMI controller results were compared with an optimal PID controller design solution. The LMI controller was further tested by incorporating a nonlinear term in the vehicle's suspension model; the LMI's controller degraded response was enhanced by using gain-scheduling techniques. The LMI controller with gain-scheduling gave good results in spite of the unmodelled dynamics in the suspension system, which was triggered by large deflections due to off-road driving.