Tracking control for boundary controlled parabolic PDEs with varying parameters: Combining backstepping and differential flatness

The combination of backstepping-based state-feedback control and flatness-based trajectory planning and feedforward control is considered for the design of an exponentially stabilizing tracking controller for a linear diffusion-convection-reaction system with spatially and temporally varying parameters and nonlinear boundary input. For this, in a first step the backstepping transformation is utilized to determine a state-feedback controller, which transforms the original distributed-parameter system into an appropriately chosen exponentially stable distributed-parameter target system of a significantly simpler structure. In a second step, the flatness property of the target system is exploited in order to determine the feedforward controller, which allows us to realize the tracking of suitably prescribed trajectories for the system output. This results in a systematic procedure for the design of an exponentially stabilizing tracking controller for the considered general linear diffusion-convection-reaction system with varying parameters, whose applicability and tracking performance is evaluated in simulation studies.     

Planning and execution through variable resolution planning

Generating sequences of actions–plans–for robots using Automated Planning in stochastic and dynamic environments has been shown to be a difficult task with high computational complexity. These plans are composed of actions whose execution might fail due to different reasons. In many cases, if the execution of an action fails, it prevents the execution of some (or all) of the remainder actions in the plan. Therefore, in most real-world scenarios computing a complete and sound (valid) plan at each (re-)planning step is not worth the computational resources and time required to generate the plan. This is specially true given the high probability of plan execution failure. Besides, in many real-world environments, plans must be generated fast, both at the start of the execution and after every execution failure. In this paper, we present Variable Resolution Planning which uses Automated Planning to quickly compute a reasonable (not necessarily sound) plan. Our approach computes an abstract representation–removing some information from the planning task–which is used once a search depth of $k$ steps has been reached. Thus, our approach generates a plan where the first $k$ actions are applicable if the domain is stationary and deterministic, while the rest of the plan might not be necessarily applicable. The advantages of this approach are that it: is faster than regular full-fledged planning (both in the probabilistic or deterministic settings); does not spend much time on the far future actions that probably will not be executed, since in most cases it will need to replan before executing the end of the plan; and takes into account some information of the far future, as an improvement over pure reactive systems. We present experimental results on different robotics domains that simulate tasks on stochastic environments.     

ARP预算管理信息系统应用探索

随着国家建设和信息产业的不断发展,研究所在发展的同时业务范围也在逐步扩张和多元化,如何在这一过程中实现合理、高效的预算管理和监控,成为研究所预算管理的核心.本项目基于ARP综合财务和科研项目系统,根据研究所以课题为成本中心的核算体制模式,建立ARP预算管理系统,关联科研项目预算与实际支出,通过课题预算录入、报销预算控制及实时预算查询等业务流程,实现事前预算编制、事中预算控制和事后预算分析的预算管理模式,为研究所管理者和科研人员提供预算执行情况、收支明细查询等综合信息服务.     

Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains

In the real-world manufacturing/distribution planning decision (MDPD) integration problems in supply chains, the environmental coefficients and parameters are normally imprecise due to incomplete and/or unavailable information. This work presents a fuzzy linear programming approach based on the possibility theory. It applies this approach to solve multi-product and multi-time period MDPD problems with imprecise goals and forecast demand by considering the time value of money of related operating cost categories. The proposed approach attempts to minimize the total manufacturing and distribution costs by considering the levels of inventory, subcontracting and backordering, the available machine capacity and labor levels at each source, forecast demand and available warehouse space at each destination. This study utilizes an industrial case study to demonstrate the feasibility of applying the proposed approach to practical MDPD problems. The primary contribution of this paper is a fuzzy mathematical programming methodology for solving the MDPD integration problems in uncertain environments.     

Path-planning and navigation of a mobile robot as discrete optimization problems

There is huge diversity among navigation and path-planning problems in the real world because of the enormous number and great variety of assumptions about the environments, constraints, and tasks imposed on a robot. To deal with this diversity, we propose a new solution to the path-planning and navigation of a mobile robot. In our approach, we formulated the following two problems at each time-step as discrete optimization problems: (1) estimation of a robot's location, and (2) action decision. For the first problem, we minimize an objective function that includes a data term, a constraint term, and a prediction term. This approach is an approximation of Markov localization. For the second problem, we define and minimize another objective function that includes a goal term, a smoothness term, and a collision term. Simulation results show the effectiveness of our approach. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Robot Motion Planning: A Game-Theoretic Foundation

Analysis techniques and algorithms for basic path planning have become quite valuable in a variety of applications such as robotics, virtual prototyping, computer graphics, and computational biology. Yet, basic path planning represents a very restricted version of general motion planning problems often encountered in robotics. Many problems can involve complications such as sensing and model uncertainties, nonholonomy, dynamics, multiple robots and goals, optimality criteria, unpredictability, and nonstationarity, in addition to standard geometric workspace constraints. This paper proposes a unified, game-theoretic mathematical foundation upon which analysis and algorithms can be developed for this broader class of problems, and is inspired by the similar benefits that were obtained by using unified configuration-space concepts for basic path planning. By taking this approach, a general algorithm has been obtained for computing approximate optimal solutions to a broad class of motion planning problems, including those involving uncertainty in sensing and control, environment uncertainties, and the coordination of multiple robots. Received November 11, 1996; revised March 13, 1998.     

An approach to analyzing and planning post-merger IS integration: Insights from two field studies

Information systems (IS) integration is among the most challenging tasks in corporate mergers and acquisitions (M&As). In this paper we propose and illustrate the use of an approach to analyzing and planning post-merger IS integration that extends general theory for IS planning, considering the specific characteristics of the M&A context. We highlight that planning effectiveness is possible to achieve in fundamentally different ways, dependent on the characteristics of the merger or acquisition at hand.

论溪落渡水电站的战略地位

溪落渡水电站是金沙江上最大的一座巨型电站,装机容量1200~1500万kW,除发电外兼有防洪、拦沙、改善下游航运等综合利用效益。通过对金沙江水电基地在我国经济建设中的地位和作用;溪落渡水电站在金沙江水电基地开发中的地位和作用、在“西电东送(包括南送)”中的地位和作用以及在长江上游水电基地中的地位和作用等论述,希望引起有关领导和决策部门的重视,加快溪落水电站前期工作的进程和促进金沙江水电基地的开发。