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Resulting from the need for fast and insightful modeling combined with the drawbacks of available modeling environments, provides details of work developed on an automated modelling environment.

An automated modeling environment for serial manipulators has been implemented in Matlab/Simulink.

The manipulator configuration is defined by using a graphical user interface and the corresponding mathematical model is automatically generated. The model is exported to Matlab for analysis and control design, as well as to Simulink for simulation and verification purposes. Friction and stiction phenomena are included in the model. The simulation results can be visualized in standard plots and scopes as well as through virtual reality animations.

The modeling environment has been used in the design of a control system for a seven‐degree‐of‐freedom manipulator in a tunnel‐boring machine.

Information on the implementation of an automated modelling environment to facilitate the simultaneous design of the configuration and the corresponding control system of serial manipulators

The purpose of this paper is to propose a novel ant colony optimization (ACO) approach to optimal control. The standard ACO algorithms have proven to be very powerful optimization metaheuristic for combinatorial optimization problems. They have been demonstrated to work well when applied to various nondeterministic polynomial‐complete problems, such as the travelling salesman problem. In this paper, ACO is reformulated as a model‐free learning algorithm and its properties are discussed.

First, it is described how quantizing the state space of a dynamic system introduces stochasticity in the state transitions and transforms the optimal control problem into a stochastic combinatorial optimization problem, motivating the ACO approach. The algorithm is presented and is applied to the time‐optimal swing‐up and stabilization of an underactuated pendulum. In particular, the effect of different numbers of ants on the performance of the algorithm is studied.

The simulations show that the algorithm finds good control policies reasonably fast. An increasing number of ants results in increasingly better policies. The simulations also show that although the policy converges, the ants keep on exploring the state space thereby capable of adapting to variations in the system dynamics.

This paper introduces a novel ACO approach to optimal control and as such marks the starting point for more research of its properties. In particular, quantization issues must be studied in relation to the performance of the algorithm.

The paper presented is original as it presents the first application of ACO to optimal control problems.

Presents a review on implementing finite element methods on supercomputers, workstations and PCs and gives main trends in hardware and software developments. An appendix included at the end of the paper presents a bibliography on the subjects retrospectively to 1985 and approximately 1,100 references are listed.

The consideration of uncertainties in the numerical computation of electromagnetic fields has recently gained a lot of attention. Most publications focus on the creation of models for the uncertainty quantification, however, neglect the inaccuracy of the applied finite element model itself. Thus, the purpose of this paper is to analyze the influence of mesh quality on stochastic cogging torque calculations.

The presented work consists of three steps. At first, a conventional analysis of the influence of mesh accuracy onto cogging torque is presented. Afterwards, the method is extended to stochastic calculations. Based on a comparison of the convergence behavior of both approaches, a method for more accurate cogging torque predictions with fewer necessary calculations is derived.

An improved method to calculate probability predictions at minimum computational cost is presented and applied.

The presented approach requires the exact knowledge of the system’s stochastic variation boundaries.

A fast method for more accurate stochastic cogging torque calculations is developed.