The paper puts forward and implements a method of designing and creating a modelling simulation environment for extensive and complete analysis of economical lighting on highways. From a general design viewpoint, the proposed solution explores the concept of a network description language (SMOL), which has been designed to describe the necessary network functions, mechanisms, and devices for the purpose of their computer simulation and verification. The presented results of the performed research confirm the usability of intelligent lighting on highways, both in the sense of the design concept and in the aspect of saving energy.

The paper puts forward a method of designing and creating a complete computer system for monitoring and diagnosis of business and industrial facilities, as well as for control purposes. The proposed solution represents a computer-network system being a practical tool for communication, control and management of modern plants and enterprises. The applied concept of communication, based on the Service Oriented Architecture (SOA), makes a new attempt to solve certain performance problems met when using a (previously developed) Networked Object Monitor (NOM). The principal idea of increasing the performance of NOM lies in employing a common data bus, refereed to as a Diagnostic Service Bus (DSB), in the NOM monitor. The paper also describes a preliminary concept of a network description language (SMOL), which is designed to describe the functions, mechanisms, and network devices and to be a basis for simulation and verification of the NOMmonitor function.

The paper uses specific parameter estimation methods to identify the coefficients of continuous-time models represented by linear and non-linear ordinary differential equations. The necessary approximation of such systems in discrete time in the form of utility models is achieved by the use of properly tuned ‘integrating filters’ of the FIR type. The resulting discrete-time descriptions retain the original continuous parameterization and can be identified, for example, by the classical least squares procedure. Since in the presence of correlated noise, the estimated parameter values are burdened with an unavoidable systematic error (manifested by asymptotic bias of the estimates), in order to significantly improve the identification consistency, the method of instrumental variables is used here. In our research we use an estimation algorithm based on the least absolute values (LA) criterion of the least sum of absolute values, which is optimal in identifying linear and non-linear systems in the case of sporadic measurement errors. In the paper, we propose a procedure for determining the instrumental variable for a continuous model with non-linearity (related to the Wienerian system) in order to remove the evaluation bias, and a recursive sub-optimal version of the LA estmator. This algorithm is given in a simple (LA) version and in an instrumental variable version (IV-LA), which is robust to outliers, removes evaluation bias, and is suited to the task of identifying processes with non-linear dynamics (semi-Wienerian/NLID). In conclusion, the effectiveness of the proposed algorithmic solutions has been demonstrated by numerical simulations of the mechanical system, which is an essential part of the suspension system of a wheeled vehicle.