Manufacturing processes are characterized by both discrete and continuous variable dynamics. The design of innovative and effective control strategies for these processes requires the development of new modeling techn...
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Manufacturing processes are characterized by both discrete and continuous variable dynamics. The design of innovative and effective control strategies for these processes requires the development of new modeling techniques that not only capture the hybrid continuous/discrete dynamic nature of the process, but also address the performance objectives in a systematic way. It is also desirable that the modeling techniques address the controller design issue from a system-theoretic perspective. This paper introduces a new hierarchical approach for modeling of manufacturing systems in which the manufacturing plant is explicitly differentiated from the controller. A key feature of the proposed approach is the use of system-theoretic control concepts that capitalize upon tools from the area of fuzzy logic. An equivalence of this modeling approach with fuzzy Petri nets is described. An example is given that illustrates the design techniques.
A model is presented of the dynamics of many controllers in a distributed system, each controlling one element of a vector and, as a system, trying to optimize a function of the vector. It is assumed that the controll...
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A model is presented of the dynamics of many controllers in a distributed system, each controlling one element of a vector and, as a system, trying to optimize a function of the vector. It is assumed that the controllers apply an adaptive method to achieve the optimal result. The optimization is challenging since the controllers have delayed information concerning the individual elements of the control vector. The potential exists for each element to be viewed with its own unique delay. Linear stability analysis provides an upper bound of the conditions for damped oscillations and a lower bound on the conditions for persistent oscillations. The results indicate that controllers can improve performance with respect to stability by 1) communicating more frequently, 2) interacting in smaller groups, 3) slowing down the adaptive search process, and/or 4) creating more diversity in the delays.
The control of tritium in present and future fusion plants is important for the viability of fusion as a power source. This study presents a generic model which describes the thermal and closed loop control behaviour ...
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The control of tritium in present and future fusion plants is important for the viability of fusion as a power source. This study presents a generic model which describes the thermal and closed loop control behaviour of cold trap assemblies. The model can also be applied to any gas separation or purification in chemical processing, archeometry sample processing and industrial process applications, typically in the range of 80-200 K. It applies to any trap configuration and is used to simulate the dynamic behaviour, the thermal and mass transfer processes and the control parameters in order to optimise performance. In the special case of hydrogen isotope separation, including tritium, as in fusion applications, the model is used to gain an in-depth knowledge of the packed bed control system and operation under tritium gas, and minimise any unnecessary testing and stressing of the cold trap.
作者:
LI, WTSING HUA UNIV
DEPT COMP SCINATL LAB INTELLIGENT TECHNOL & SYSTBEIJING 100084PEOPLES R CHINA
This paper describes neuro-fuzzy systems for intelligent robot navigation and control under uncertainty. First, we present a new neuro-fuzzy system architecture for behavior navigation of a mobile robot in unknown env...
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ISBN:
(纸本)0780324625
This paper describes neuro-fuzzy systems for intelligent robot navigation and control under uncertainty. First, we present a new neuro-fuzzy system architecture for behavior navigation of a mobile robot in unknown environments. In this neuro-fuzzy system, a neural network is used to process range information for understanding distribution of obstacles in local regions, so that a good reference motion direction can be determined;while fuzzy sets and a rule base are used to quantitatively formulate reactive behavior and to coordinate conflicts and competition among multiple types of behavior. Second, based on open-loop responses of a simplified model, we present a new method for designing a neuro-fuzzy controller for a manipulator with nonlinear dynamics or with unknown structure. Because control signals from a fuzzy controller are determined by system response behavior rather than its analytical models, open-loop responses of a set of second-order systems are chosen to describe manipulator characters. Then, the parameters of the fuzzy controller, related to the second-order systems, are off-line optimized, and a neural network is used to train the mapping relationship between the open-loop responses of the second-order systems and the optimized parameters of their corresponding fuzzy controllers. In order to control the manipulator, the neural network perceives manipulator responses and determines the parameters of its fuzzy controller.
A model is presented of the dynamics of many controllers in a distributed system, each controlling one element of a vector and, as a system, trying to optimize a function of the vector. It is assumed that the controll...
详细信息
A model is presented of the dynamics of many controllers in a distributed system, each controlling one element of a vector and, as a system, trying to optimize a function of the vector. It is assumed that the controllers apply an adaptive method to achieve the optimal result. The optimization is challenging since the controllers have delayed information concerning the individual elements of the control vector. The potential exists for each element to be viewed with its own unique delay. Linear stability analysis provides an upper bound of the conditions for damped oscillations and a lower bound on the conditions for persistent oscillations. The results indicate that controllers can improve performance with respect to stability by: 1) communicating more frequently, 2) interacting in smaller groups, 3) slowing down the adaptive search process, and 4) creating more diversity in the delays.
Manufacturing processes are characterized by both discrete and continuous variable dynamics. The design of innovative and effective control strategies for these processes requires the development of new modeling techn...
详细信息
Manufacturing processes are characterized by both discrete and continuous variable dynamics. The design of innovative and effective control strategies for these processes requires the development of new modeling techniques that not only capture the hybrid continuous/discrete dynamic nature of the process, but also address the performance objectives in a systematic way. It is also desirable that the modeling techniques address the controller design issue from a system-theoretic perspective. This paper introduces a new hierarchical approach for modeling of manufacturing systems in which the manufacturing plant is explicitly differentiated from the controller. A key feature of the proposed approach is the use of system-theoretic control concepts that capitalize upon tools from the area of fuzzy logic. An equivalence of this modeling approach with fuzzy Petri nets is described. An example is given that illustrates the design techniques.
In this paper, a constrained discrete-time nonlinear state space model is proposed for modeling the dynamics of general manufacturing system. The model is an extension/modification of the one proposed by Rovithakis an...
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In this paper, a constrained discrete-time nonlinear state space model is proposed for modeling the dynamics of general manufacturing system. The model is an extension/modification of the one proposed by Rovithakis and Christodoulou. The proposed system possesses certain advantages over the existing ones, such as generality and ability to solve optimal policy tasks using optimal control methods as well as ability to model both deterministic and stochastic processing times; moreover its stability and robustness properties can be easily identified. The optimal control of such a system is shown to be equivalent to the solution of a system of static nonlinear equations. Despite the fact that no close form solution is obtained, it is believed that the resulted control policy obtained from the solution of the aforementioned system of equations is locally optimal. Simulations performed on a very simple manufacturing process are also presented.
This paper deals with vision-based fuzzy closed-loop control schemes for collision avoidance as well as maintenance of clearance in a-priori unknown textured environments. These control schemes employ a visual motion ...
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This paper deals with vision-based fuzzy closed-loop control schemes for collision avoidance as well as maintenance of clearance in a-priori unknown textured environments. These control schemes employ a visual motion cue, we call the visual threat cue (VTC) that provides some measure for a relative change in range as well as clearance between 3D surface and a fixated observer in motion. It is a collective measure obtained directly from the raw data of gray level images, is independent of the type of 3D surface texture. This motion cue is scale-independent, rotation independent, needs no 3D reconstruction and is measured in [time/sup -1/] units. Fuzzy control is closer in spirit to human thinking and can implement linguistically-expressed heuristic control policies directly without any knowledge about the dynamics of the complex process. The fuzzy controllers were implemented in real-time using a 486-based personal computer and a camera capable of undergoing 6-DOF motion. Results are highly encouraging.
This paper analyzes the properties of a proportional machine allocation scheme in a reentrant production line of one product and one machine group. The scheme allocates machines to each production stage proportional t...
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ISBN:
(纸本)0780325354
This paper analyzes the properties of a proportional machine allocation scheme in a reentrant production line of one product and one machine group. The scheme allocates machines to each production stage proportional to the workload of the stage. It is proved that given initial work-in-process (WIP) distribution and a constant work release rate in the capacity of the line, there exists a unique, full machine capacity allocation for the line; when the proportional machine allocation scheme is repetitively applied over time, the whole reentrant line is eventually balanced. These results obtained under such a simplified scenario also provide some insights to the dynamics and resource allocation problem of a reentrant line.
This paper considers the use of neural networks (NNs) in controlling a nonlinear, stochastic system with unknown process equations. The approach here is based on using the output error of the system to train the NN co...
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This paper considers the use of neural networks (NNs) in controlling a nonlinear, stochastic system with unknown process equations. The approach here is based on using the output error of the system to train the NN controller without the need to assume or construct a separate model (NN or other type) for the unknown processdynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown processdynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations. The approach will be illustrated on a simulated wastewater treatment system with stochastic effects and nonstationary dynamics.
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