Simultaneous optimization of slab permutation scheduling and heat controlling for a reheating furnace

In this report, for a reheating furnace, which is employed in one of the processes for producing steel sheets from slabs, we propose a modelling method that simultaneously optimizes both the permutation scheduling of slabs and the heat controlling of the furnace. The proposed modelling scheme is based on a hybrid model composed of a nonlinear advection equation that expresses the behavior of the slab temperature and a discrete model for feeding slabs. The model predictive control problem of this model, which will be reduced to a mixed integer programming problem, is formulated by discretizing the advection equation in time and space by means of the method of characteristics and spatially piecewise-linearizing the nonlinear term. It is shown by numerical simulations that the proposed model predictive control method is very effective from the viewpoint of the control performance and the computational burden.     

高温力学试样机监控系统的设计

设计了高温力学试样机的计算机监控系统，介绍了其组成、结构、功能与特点，并详细介绍了其软件设计，实现了对高温力学试样机的实时监控和数据管理。     

竖炉焙烧过程运行优化控制系统的开发及实验研究

为解决具有综合复杂动态特性的竖炉焙烧(shaft furnace roasting,SFR)过程运行优化控制问题,基于研制的流程工业一体化运行控制平台,开发了具有可伸缩、可扩展、可复用以及模块化、组态化特点的SFR过程运行优化控制(operational optimization control,OOC)系统.该系统支持MATLAB、动态链接库等多种算法实现方式,并采用非编译融合算法方式实现算法与系统功能模块的解耦.基于开发的OOC系统,建立了具有运行控制层、过程控制层以及虚拟对象层的半实物仿真实验平台,开展了相应的仿真实验研究.结果表明OOC系统在正常工况和局部故障工况下均能取得满意的控制性能.     

Path-based approach to integrated planning and control for robotic systems: A path-based approach provides an analytical integration of robot planning and control. As a result, the task level control can be achieved, and the system is capable of coping with unexpected or uncertain events

Our newly developed event-based planning and control theory is applied to robotic systems. It Introduces a suitable action or motion reference variable other than time, but directly related to the desired and measurable systems output, called event. Here the event is the length of the path tracked by a robot. It enables the construction of an integrated planning and control system where planning becomes a real-time closed-loop process. The path-based integration planning and control scheme is exemplified by a single-arm tracking problem. Time and energy optimal motion plans combined with nonlinear feedback control are derived in closed form. To the best of our knowledge, this closed-form solution was not obtained before. The equivalence of path-based and time-based representations of nonlinear feedback control is shown, and an overall system stability criterion has also been obtained. The application of event-based integrated planning and control provides the robotic systems the capability to cope with unexpected and uncertain events in real time, without the need for replanning. The theoretical results are illustrated and verified by experiments.     

A harmonic potential field approach for joint planning and control of a rigid,separable nonholonomic,mobile robot

The main objective of this paper is to provide a tool for performing path planning at the servo-level of a mobile robot. The ability to perform, in a provably-correct manner, such a complex task at the servo-level can lead to a large increase in the speed of operation, low energy consumption and high quality of response. Planning has been traditionally limited to the high level controller of a robot. The guidance velocity signal from this stage is usually converted to a control signal using what is known as an electronic speed controller (ESC). This paper demonstrates the ability of the harmonic potential field (HPF) approach to generate a provably-correct, constrained, well-behaved trajectory and control signal for a rigid, nonholonomic robot in a stationary, cluttered environment. It is shown that the HPF-based, servo-level planner can address a large number of challenges facing planning in a realistic situation. The suggested approach migrates the rich and provably-correct properties of the solution trajectories from an HPF planner to those of the robot. This is achieved using a synchronizing control signal whose aim is to align the velocity of the robot in its local coordinates, with that of the gradient of the HPF. The link between the two is made possible by representing the robot using what the paper terms “separable form”. The context-sensitive and goal-oriented control signal used to steer the robot is demonstrated to be well-behaved and robust in the presence of actuator noise, saturation and uncertainty in the parameters. The approach is developed, proofs of correctness are provided and the capabilities of the scheme are demonstrated using simulation results.     

A self-organized model for the control,planning and learning of nonlinear multi-dimensional systems using a sensory feedback

A new approach is presented to deal with the problem of modelling and simulating the control mechanisms underlying planned-arm-movements. We adopt a synergetic view in which we assume that the movement patterns are not explicitly programmed but rather are emergent properties of a dynamic system constrained by physical laws in space and time. The model automatically translates a high-level command specification into a complete movement trajectory. This is an inverse problem, since the dynamic variables controlling the current state of the system have to be calculated from movement outcomes such as the position of the arm endpoint. The proposed method is based on an optimization strategy: the dynamic system evolves towards a stable equilibrium position according to the minimization of a potential function. This system, which could well be described as a feedback control loop, obeys a set of non-linear differential equations. The gradient descent provides a solution to the problem which proves to be both numerically stable and computationally efficient. Moreover, the addition into the control loop of elements whose structure and parameters have a pertinent biological meaning allows for the synthesis of gestural signals whose global patterns keep the main invariants of human gestures. The model can be exploited to handle more complex gestures involving planning strategies of movement. Finally, the extension of the approach to the learning and control of non-linear biological systems is discussed.