Presents an approach to parallel path planning for industrial robot arms with six degrees of freedom in an online given 3D environment. The method is based a best-first search algorithm and needs no essential off-line...
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Presents an approach to parallel path planning for industrial robot arms with six degrees of freedom in an online given 3D environment. The method is based a best-first search algorithm and needs no essential off-line computations. The algorithm works in an implicitly discrete configuration space. Collisions are detected in the Cartesian workspace by hierarchical distance computation based on polyhedral models of the robot and the obstacles. By decomposing the 6D configuration space into hypercubes and cyclically mapping them onto multiple processing units, a good load distribution can be achieved. We have implemented the parallel path planner on a workstation cluster with 9 PCs and tested the planner for several benchmark environments. With optimal discretisation, the new approach usually shows very good speedups. In online provided environments with static obstacles, the parallel planning times are only a few seconds.
作者：
K.-W. JörgUniversity of Kaiserslautern
Robotics and Process Control Research Group Computer Science Department PO Box 3049 67653 Kaiserslautern Germany
In stability-constrained model predictive control (SCMPC) a stability constraint is propagated from stage to stage to limit magnitude of the state vector in a controllable form. For the unconstrained case, a sufficien...
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In stability-constrained model predictive control (SCMPC) a stability constraint is propagated from stage to stage to limit magnitude of the state vector in a controllable form. For the unconstrained case, a sufficient condition that can be easily evaluated guarantees the stability constraint is a feasible contraction mapping. This paper presents sufficient conditions for guaranteed asymptotic stability when SCMPC is applied to nonlinear systems with arbitrary constraints on the control and state.
This paper deals with a neural network application concerning to fixed bed grain dryer. Aim of the study is to set up a relationship between material moisture distribution and physical parameters of drying air, such a...
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This paper deals with a neural network application concerning to fixed bed grain dryer. Aim of the study is to set up a relationship between material moisture distribution and physical parameters of drying air, such as temperature and humidity. Five different neural network structures were studied on two different series input data containing inlet and outlet air temperatures and humidities and air flow. Randomly changed input data was used for training the neural network. The data were taken from a physically based model instead of real measurements. The result show that moisture content of the drying bed can be calculated from air parameters using neural network.
This paper deals with a neural network application concerning to an agricultural fixed bed driers. Aim of the study is to set up a relationship between material moisture distribution and physical parameters of drying ...
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This paper deals with a neural network application concerning to an agricultural fixed bed driers. Aim of the study is to set up a relationship between material moisture distribution and physical parameters of drying air, such as temperature and humidity. Input data was randomly changed, meanwhile output was generated by O'Callaghan's model based on the input. Number of layers in the fixed bed drier for the neural network was also determined using O'Callaghan's model.
This work deals with closed-loop calibration methods where the robot endpoint is constrained to lie on a plane. Previously published calibration approaches are shown to have certain weaknesses. A new solution is given...
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This work deals with closed-loop calibration methods where the robot endpoint is constrained to lie on a plane. Previously published calibration approaches are shown to have certain weaknesses. A new solution is given using DH and Hayati notations and standard nonlinear least squares optimization. The procedure is extended via the implicit loop method, which takes input noise into account. Pose selection is guided by the noise amplification index. Simulation and experimental results are presented for a PUMA 560 industrial manipulator and are compared to those obtained from an open-loop calibration procedure.
The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, derivation of causal relat...
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The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, derivation of causal relationships between inputs and outputs, and analysis of the hidden neuron behavior in classification ANNs. An example of the application of these techniques is given of the faulty LED display benchmark. References of the application of these techniques are given of diverse large scale ANN models of industrial processes.
Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification i...
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Deals with identification of nonlinear processes and model-based fault detection/isolation (FDI). The applicability of the proposed methods is illustrated on a three-tank laboratory setup. The process identification is based on the local linear model tree (LOLIMOT) algorithm and leads to local linear models. The parameters of the local models are used for generation of structured residual equations, similar to the well-known parity space approach. This enables detection and isolation of five different sensor faults of the three-tank process, continously over all ranges of operation.
A new method for nonlinear model predictive control with guaranteed stability is proposed as an extension to the stability constrained model predictive control (SCMPC) for linear time-invariant systems. The method app...
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ISBN:
(纸本)0780341872
A new method for nonlinear model predictive control with guaranteed stability is proposed as an extension to the stability constrained model predictive control (SCMPC) for linear time-invariant systems. The method applies to a class of nonlinear systems that can be transformed to a controllable companion form and depends on the existence of a nonlinear deadbeat control (although this is not necessarily the control that is used). Asymptotic stability is proved for the case when all state variables are measurable.
This article presents an extension to stability-constrained model predictive control (SCMPC) incorporating state estimation. It is proved that asymptotic stability of the closed loop system is guaranteed for SCMPC whe...
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This article presents an extension to stability-constrained model predictive control (SCMPC) incorporating state estimation. It is proved that asymptotic stability of the closed loop system is guaranteed for SCMPC when a stable state estimator is used rather than direct measurement of the full state variables. The results are illustrated with experimental results from a real-time implementation of SCMPC with state estimation for control of a plasma enhanced chemical vapor deposition (PECVD) system.
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