Comparative study of decay ratios of disturbance-rejection magnitude optimum method for PI controllers

One of the key time-domain closed-loop performance requirements is the closed-loop response decay ratio. In this paper, the decay ratios of the disturbance-rejection magnitude optimum (DRMO) tuning method [Vranci? D, Strmcnik S, Kocijan J. Improving disturbance rejection of PI controllers by means of the magnitude optimum method. ISA Trans 2004; 43: 73-84; Vranci? D, Strmcnik S. Achieving optimal disturbance rejection by using the magnitude optimum method. In: Pre-prints of the CSCC'99 conference. 1999. p. 3401-6] are analyzed and compared to decay ratios of two other modern tuning methods, i.e. the Kappa-Tau tuning method (based on time-domain step-response characteristics) [Astr?m KJ, H?gglund T. PID controllers: Theory, design, and tuning. 2nd ed. Instrument Society of America; 1995] and the non-convex optimization tuning method (based on frequency response) [Panagopoulos H, Astr?m KJ, H?gglund T. Design of PI controllers based on non-convex optimization. Automatica 1998; 34: 585-601; Panagopoulos H, Astr?m KJ, H?gglund T. Design of PID controllers based on constrained optimisation. IEE Proc Control Theory Appl 2002; 149 (1): 32-40]. It is shown that the DRMO method results in such a closed-loop response that the decay ratio is within a relatively narrow interval when compared to the other two methods.     

Modeling the influence of network delay on the user experience in distributed home-automation networks

Today’s modern home-automation systems and services (HASS) frequently communicate over public telecommunications networks, such as the Internet. Unfortunately, these communication networks do not usually provide sufficient quality (i.e., a predictable delay), which is generally assured in fieldbus HASS networks. Consequently, the user-perceived quality of experience (QoE) cannot be maintained at a satisfactory level when using different HASS devices communicating over an IP-based network. The data transferred over the Internet can experience a non-negligible delay that can have a considerable influence on the QoE. For this reason, the main goal of our research was to measure the influence of the network delay on a subjective QoE assessment, while interacting with some frequently used HASS tasks. The results show that users are satisfied if the delay is kept below 0.8?s, and that they can tolerate delays of over 2?s (depending on the level of the HASS task interactivity). Since such a user-perceived subjective QoE assessment is both time-consuming and expensive we also propose objective QoE assessment models to represent the influence of network delay on a subjective QoE assessment for various HASS tasks.