基于SQP算法的7000m载人潜水器有约束非线性控制分配研究

以7000m载人潜水器为研究对象,分析了潜水器的推进系统,并给出了6自由度推力转换模型,重点讨论了载人潜水器控制分配的优化问题.结合7000m载人潜水器的推进器布置和推进器特点,设计了优化准则代价函数,采用序列二次规划(sequential quadratic programm ing,SQP)算法求解了载人潜水器的非线性控制分配问题,通过半物理仿真平台实验验证了本文提出的控制分配算法的正确性和有效性.yh

Investigation of methods for increasing the energy efficiency on unmanned aerial vehicles (UAVS)

In recent years, development of Unmanned Aerial Vehicles (UAV) has become a significant growing segment of the global aviation industry. These vehicles are developed with the intention of operating in regions where the presence of onboard human pilots is either too risky or unnecessary. Their popularity with both the military and civilian sectors have seen the use of UAVs in a diverse range of applications, from reconnaissance and surveillance tasks for the military, to civilian uses such as aid relief and monitoring tasks. Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. This thesis presents an investigation of methods for increasing the energy efficiency on UAVs. One method is via the development of a Mission Waypoint Optimisation (MWO) procedure for a small fixed-wing UAV, focusing on improving the onboard fuel economy. MWO deals with a pre-specified set of waypoints by modifying the given waypoints within certain limits to achieve its optimisation objectives of minimising/maximising specific parameters. A simulation model of a UAV was developed in the MATLAB Simulink environment, utilising the AeroSim Blockset and the in-built Aerosonde UAV block and its parameters. This simulation model was separately integrated with a multi-objective Evolutionary Algorithm (MOEA) optimiser and a Sequential Quadratic Programming (SQP) solver to perform single-objective and multi-objective optimisation procedures of a set of real-world waypoints in order to minimise the onboard fuel consumption. The results of both procedures show potential in reducing fuel consumption on a UAV in a ight mission. Additionally, a parallel Hybrid-Electric Propulsion System (HEPS) on a small fixedwing UAV incorporating an Ideal Operating Line (IOL) control strategy was developed. An IOL analysis of an Aerosonde engine was performed, and the most efficient (i.e. provides greatest torque output at the least fuel consumption) points of operation for this engine was determined. Simulation models of the components in a HEPS were designed and constructed in the MATLAB Simulink environment. It was demonstrated through simulation that an UAV with the current HEPS configuration was capable of achieving a fuel saving of 6.5%, compared to the ICE-only configuration. These components form the basis for the development of a complete simulation model of a Hybrid-Electric UAV (HEUAV).

The alpha method for solving differential algebraic inequality (DAI) systems

This paper describes an algorithm for ``direct numerical integration'' of the initial value Differential-Algebraic Inequalities (DAI) in a time stepping fashion using a sequential quadratic programming (SQP) method solver for detecting and satisfying active path constraints at each time step. The activation of a path constraint generally increases the condition number of the active discretized differential algebraic equation's (DAE) Jacobian and this difficulty is addressed by a regularization property of the alpha method. The algorithm is locally stable when index 1 and index 2 active path constraints and bounds are active. Subject to available regularization it is seen to be stable for active index 3 active path constraints in the numerical examples. For the high index active path constraints, the algorithm uses a user-selectable parameter to perturb the smaller singular values of the Jacobian with a view to reducing the condition number so that the simulation can proceed. The algorithm can be used as a relatively cheaper estimation tool for trajectory and control planning and in the context of model predictive control solutions. It can also be used to generate initial guess values of optimization variables used as input to inequality path constrained dynamic optimization problems. The method is illustrated with examples from space vehicle trajectory and robot path planning.

Shape optimization of RC flexural members

A natural velocity field method for shape optimization of reinforced concrete (RC) flexural members has been demonstrated. The possibility of shape optimization by modifying the shape of an initially rectangular section, in addition to variation of breadth and depth along the length, has been explored. Necessary shape changes have been computed using the sequential quadratic programming (SQP) technique. Genetic algorithm (Goldberg and Samtani 1986) has been used to optimize the diameter and number of main reinforcement bars. A limit-state design approach has been adopted for the nonprismatic RC sections. Such relevant issues as formulation of optimization problem, finite-element modeling, and solution procedure have been described. Three design examples-a simply supported beam, a cantilever beam, and a two-span continuous beam, all under uniformly distributed loads-have been optimized. The results show a significant savings (40-56%) in material and cost and also result in aesthetically pleasing structures. This procedure will lead to considerable cost saving, particularly in cases of mass-produced precast members and a heavy cast-in-place member such as a bridge girder.

Enolic Schiff-base aluminum complexes and their application in lactide polymerization

A series of NNOO-tetradentate enolic Schiff-base ligands were prepared where ligand L-1 = bis(benzoylacetone)propane-1,2-diimine, L-2 = bis(acetylacetone)-propane-1,2-diimine, L-3 = bis-(acetylacetone)cyclohexane-1,2-diimine. Their further reaction with aluminum tris(ethyl) formed complexes LAlEt (1a, 2a and 3a). The solid structure of complexes la, 2a and 3a confirmed by X-ray single crystal analysis manifested that these complexes were all monomeric and five-coordinated with an aluminum atom in the center. The configurations of these complexes varied from trigonal bipyramidal geometry (tbp) to square pyramidal geometry (sqp) due to their different auxiliary ligand architectures. H-1 NMR spectra indicated that all these complexes retained their configuration in solution states. Their catalytic properties to polymerize racemic-lacticle (rac-LA) in the presence of 2-propanol were also studied. The diimine bridging parts as well as the diketone segment substituents had very close relationship with their performance upon the polymerization process. All these complexes gave moderately isotactic polylactides with controlled molecular weight and very narrow molecular weight distributions.

Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.

7000米载人潜水器动力定位系统研究

深海载人潜水器可以运载科学家和工程技术人员，以及各种仪器设备进入深海现场，进行各种海洋资源调查、科学考察和海底调查等作业任务。近十五年来，深海载人潜水器为促进海洋资源调查、海洋生物基因、海洋地质勘探等与海洋相关研究领域的发展作出了显著贡献。未来，深海载人潜水器还将在与海洋相关研究领域，以及海洋资源开发与争夺过程中发挥更加重要的作用。随着海洋高技术的发展，人类对海洋的研究更加深入和精细，在众多作业任务中，将逐渐要求深海载人潜水器具有动力定位能力。精确的动力定位能力，可以有效提高载人潜水器的作业能力和效率，降低操作人员的作业强度。影响深海载人潜水器动力定位系统性能的因素有很多，主要包括状态感知系统的测量精度、作业系统作业过程中产生的扰动、复杂海洋环境产生的复杂外力干扰、以及推进子系统的控制分配和故障容错能力等。 本文以“十五”期间国家863计划重大专项“7000米载人潜水器”为研究背景，结合7000米载人潜水器动力定位能力的实际应用需求，深入研究了7000米载人潜水器动力定位系统，实现高精度、高稳定性的动力定位。重点研究能有效提高7000米载人潜水器水平位置和线速度测量精度的导航方法；研究适合存在复杂外界干扰和自身不确定性条件下，多输入多输出的载人潜水器智能控制方法；研究具有推进器故障容错能力的动态控制分配策略。本文研究内容主要包括： （1） 根据7000米载人潜水器的传感器配置情况，研究基于多普勒速度计程仪和光纤罗经的载人潜水器导航问题。提出基于“当前”加速度统计模型和潜水器运动方程的Kalman Filter导航方法，这种导航方法可以在线准确地估计出多普勒速度计程仪的速度测量偏差比例因子，以及多普勒速度计程仪和光纤罗经间的航向角安装偏差。通过仿真实验和湖上试验数据验证实验，验证了该导航方法的正确性和有效性。 （2） 基于潜水器6自由度空间动力学模型，设计了基于径向基神经网络直接自适应控制策略的7000米载人潜水器动力定位控制器。基于Lyapunov稳定性理论，证明了存在有界外界干扰和有界神经网络逼近误差条件下，7000米载人潜水器控制系统的闭环稳定性。通过7000米载人潜水器动力学模型的仿真实验和水下机器人实验平台的水池实验，进一步验证了该控制系统的正确性、有效性和稳定性。 （3） 提出了采用动态模糊神经网络在线学习算法，解决基于径向基神经网络载人潜水器直接自适应控制方法的网络结构设计困难问题。改进了用于机械手运动控制的动态模糊神经网络在线学习算法的结构学习部分，使其更适用于载人潜水器的控制。基于Lyapunov稳定性理论，证明了基于动态模糊神经网络直接自适应控制策略的载人潜水器控制系统的闭环稳定性，并进行了仿真实验。 （4） 基于推进器配置情况，建立了7000米载人潜水器的动态控制分配模型，设计了推进器故障容错处理策略。针对完全固定推进器配置的潜水器系统，提出了基于伪逆矩阵分配和定点分配策略相结合的混合动态控制分配算法。针对存在可动态配置的回转推进器，提出基于SQP算法的动态非线性优化控制分配策略，设计了优化算法的代价函数。通过仿真实验和7000米载人潜水器半物理仿真平台实验，验证了本文研究的潜水器动态控制分配算法的正确性和有效性。

A study of user requirements for COTS: computer off the shelf software solutions

[This abstract is based on the authors' abstract.]Three new standards to be applied when adopting commercial computer off-the-shelf (COTS) software solutions are discussed. The first standard is for a COTS software life cycle, the second for a software solution user requirements life cycle, and the third is a checklist to help in completing the requirements. The standards are based on recent major COTS software solution implementations.

Day-Ahead Optimal Charging/Discharging Scheduling for Electric Vehicles in Micro-Grids

A micro-grid is an autonomous system which can be operated and connected to an external system or isolated with the help of energy storage systems (ESSs). While the daily output of distributed generators (DGs) strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the imbalance between the daily load and generation curves. In this paper, a statistical model is presented to describe daily EV charging/discharging behaviour. An optimisation problem is proposed to obtain economic operation for the micro-grid based on this model. In day-ahead scheduling, with estimated information of power generation and load demand, optimal charging/discharging of EVs during 24 hours is obtained. A series of numerical optimization solutions in different scenarios is achieved by serial quadratic programming. The results show that optimal charging/discharging of EVs, a daily load curve can better track the generation curve and the network loss and required ESS capacity are both decreased. The paper also demonstrates cost benefits for EVs and operators.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.

Numerical optimization experiments using the hyperbolic smoothing strategy to solve MPCC

In this work we solve Mathematical Programs with Complementarity Constraints using the hyperbolic smoothing strategy. Under this approach, the complementarity condition is relaxed through the use of the hyperbolic smoothing function, involving a positive parameter that can be decreased to zero. An iterative algorithm is implemented in MATLAB language and a set of AMPL problems from MacMPEC database were tested.

Numerical experiments with a modified regularization scheme for mathematical programs with complementarity constraints

On this paper we present a modified regularization scheme for Mathematical Programs with Complementarity Constraints. In the regularized formulations the complementarity condition is replaced by a constraint involving a positive parameter that can be decreased to zero. In our approach both the complementarity condition and the nonnegativity constraints are relaxed. An iterative algorithm is implemented in MATLAB language and a set of AMPL problems from MacMPEC database were tested.

Optimization Strategies Based on Sequential Quadratic Programming Applied for a Fermentation Process for Butanol Production

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)