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Future manufacturing systems need to cope with frequent changes and disturbances. As such, their control requires constant adaptation and high flexibility. Holonic manufacturing is a highly distributed control paradigm that promises to handle these problems successfully. It is based on the concept of autonomous cooperating agents, called 'holons'. This paper gives an overview of the holonic reference architecture for manufacturing systems as developed at PMA-KULeuven. This architecture, called PROSA, consists of three types of basic holons: order holons, product holons, and resource holons. They are structured using the object-oriented concepts of aggregation and specialisation. Staff holons can be added to assist the basic holons with expert knowledge. The resulting architecture has a high degree of self-similarity, which reduces the complexity to integrate new components and enables easy reconfiguration of the system. PROSA shows to cover aspects of both hierarchical as well as heterarchical control approaches. As such, it can be regarded as a generalisation of the two former approaches. More importantly, PROSA introduces significant innovations: the system structure is decoupled from the control algorithm, logistical aspects can be decoupled from technical ones, and PROSA opens opportunities to achieve more advanced hybrid control algorithms.

This paper discusses experimental robot identification based on a statistical framework. It presents a new approach toward the design of optimal robot excitation trajectories, and formulates the maximum-likelihood estimation of dynamic robot model parameters. The differences between the new design approach and the existing approaches lie in the parameterization of the excitation trajectory and in the optimization criterion. The excitation trajectory for each joint is a finite Fourier series. This approach guarantees periodic excitation which is advantageous because it allows: 1) time-domain data averaging; 2) estimation of the characteristics of the measurement noise, which is valuable in case of maximum-likelihood parameter estimation. In addition, the use of finite Fourier series allows calculation of the joint velocities and accelerations in an analytic way from the measured position response, and allows specification of the bandwidth of the excitation trajectories. The optimization criterion is the uncertainty on the estimated parameters or a lower bound for it, instead of the often used condition of the parameter estimation problem. Simulations show that this criterion yields parameter estimates with smaller uncertainty bounds than trajectories optimized according to the classical criterion. Experiments on an industrial robot show that the presented trajectory design and maximum-likelihood parameter estimation approaches complement each other to make a practicable robot identification technique which yields accurate robot models.

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