Miodrag Spasic

Outage capacity of FSO link with pointing errors and link blockage.

In this paper, we analyze the outage capacity performance of free-space optical (FSO) systems. More precisely, taking the stochastic temporary blockage of the laser beam, atmospheric turbulence, misalignment between transmitter laser and receiver photodiode and path loss into account, we derive novel accurate analytical expressions for the outage capacity. The intensity fluctuations of the received signal are modeled by a Gamma-Gamma distribution with parameters directly related to the wide range of atmospheric conditions. The analytical results are validated by Monte Carlo simulations. Furthermore, when the intensity fluctuations are caused only by atmospheric turbulence, derived expressions are reduced to the simpler forms already presented in literature. The numerical and simulation results show that the link blockage causes appearance of the outage floor that is a significant energetic characteristic of an FSO system. The results also show that there exists an optimal value of the laser beam radius at the waist for minimizing outage probability in order to achieve the specified outage capacity. This optimal value depends on atmospheric turbulence strength and standard deviation of pointing errors, but it is also strongly dependent on the probability of link blockage.

An approach to design of sliding mode based generalized predictive control

This paper presents the combination of generalized predictive and sliding mode control techniques in order to improve the system robustness to parameter variation. The proposed control algorithm belongs to the group of chattering-free sliding mode control laws, and it provides the minimum value of the cost function in the presence of parameter perturbations. Digital simulation results are given to verify the sliding mode based generalized predictive controller.

Adaptive PID control based on orthogonal endocrine neural networks

A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances.

Probability estimation of certain properties of the imperfect systems

This paper presents one method for probability estimation of some property of imperfect systems (stability, reliability, oscillations appearance, controllability, sensitivity, etc.). The region of certain system property is approximated in parameter space and relations for probability of defined properties are obtained. Application systems with one nonlinearity: of this approximate method in the real technical system is given for the case of determining the probability of oscillations appearance.

Tube model predictive control based on Laguerre functions with an auxiliary sliding mode controller

This paper deals with Tube Model Predictive Control (MPC) based on Laguerre functions with a Sliding Mode Controller (SMC) as an auxiliary controller. Two types of SMC are implemented: the traditional one and the robust chattering-free discrete-time SMC. It is shown how much the constraints of the nominal Laguerre functions based MPC have to be tightened in order to achieve robust stability and control constraints fulfilment. The proposed approach is verified by experimental results.

ORTHOGONAL HYBRID-FUZZY CONTROLLERS

The main idea of this paper is to present a possibility of application of hybrid-fuzzy controllers in control systems theory. In this paper, we have described a new method оf using orthogonal functions in control of dynamical systems. These functions generate genarilzed quasi-orthogonal filter, which are used in the concluding phase of the fuzzy controllers. Proposed hybrid-fuzzy controllers of Takagi-Sugeno type has been applied to a DC servo drive system and performed experiments have verified efficiency and improvements of a new control method.