The control system of industrial robots is often model-based, and the quality of the model of high importance. Therefore, a fast and easy-to-use process for finding the model parameters from a combination of prior knowledge and measurement data is required. It has been shown that the experiment design can be improved in terms of short experiment times and an accurate parameter estimate if the robot configurations for the identification experiments are selected carefully. Estimates of the information matrix can be generated based on simulations for a number of candidate configurations, and an optimization problem can be solved for finding the optimal configurations. This work shows that the proposed method for improved experiment design works with a real manipulator, i.e. it is demonstrated that the experiment time is reduced significantly and the accuracy of the parameter estimate can be maintained or reduced if experiments are conducted only in the optimal manipulator configurations. It is also shown that the model improvement is relevant for realizing accurate control. Finally, the experimental data reveals that, in order to further improve the model accuracy, a more advanced model structure is needed for taking into account the commonly present nonlinear transmission stiffness of the robotic joints.

In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the system dynamics. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantize the disturbance present in the measured variable as a function of the sampling period and we propose a measurement disturbance suppressive control structure. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability and adaptation.

The intricacy of real-time data service management increases mainly due to the emergence of applications operating in open and unpredictable environments, increases in software complexity, and need for performance guarantees. In this paper we propose an approach for managing the quality of service of real-time databases that provide imprecise and differentiated services, and that operate in unpredictable environments. Transactions are classified into service classes according to their level of importance. Transactions within each service class are further classified into subclasses based on their quality of service requirements. In this way transactions are explicitly differentiated according to their importance and quality of service requests. The performance evaluation shows that during overloads the most important transactions are guaranteed to meet their deadlines and that reliable quality of service is provided even in the face of varying load and execution time estimation errors.

Several applications, such as Web services and e-commerce, are operating in open environments where the workload characteristics, such as the load applied on the system and the worst-case execution times, are inaccurate or even not known in advance. This implies that transactions submitted to a real-time database cannot be subject to exact schedulability analysis given the lack of a priori knowledge of the workload. In this paper we propose an approach, based on feedback control, for managing the quality of service of real-time databases that provide imprecise and differentiated services, given inaccurate workload characteristics. For each service class, the database operator specifies the quality of service requirements by explicitly declaring the precision requirements of the data and the results of the transactions. The performance evaluation shows that our approach provides reliable quality of service even in the face of varying load and inaccurate execution time estimates.

This paper gives an overview of classical iterative learning control algorithms. The presented algorithms are also evaluated on a commercial industrial robot from ABB. The paper covers implicit to explicit model-based algorithms. The result from the evaluation of the algorithms is that performance can be achieved by having more system knowledge.

A study is made of three recursive identification algorithms with respect to their tracking capability and disturbance sensitivity. The algorithms are applied to the identification of time-varying systems with finite impulse response. The performance of the algorithms are measured in terms of the mean square error of the estimated transfer function. Using asymptotic methods, explicit expressions for the mean square error are derived, and these expressions are used to evaluate the performance of the algorithms when applied to systems with different behavior.>