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.

Adaptive Control of Nonlinear MIMO System With Orthogonal Endocrine Intelligent Controller.

In this article, a new intelligent hybrid controller is proposed. The controller is based on the combination of the orthogonal endocrine neural network (OENN) and orthogonal endocrine ANFIS (OEANFIS). The orthogonal part of the controller consists of Chebyshev orthogonal functions, which are used because of their recursive property, computational simplicity, and accuracy in nonlinear approximations. Artificial endocrine influence on the controller is achieved by introducing excitatory and inhibitory glands to the OENN part of the structure, in the form of postsynaptic potentials. These potentials provide a network with the capability of additional self-regulation in the presence of external disturbances. The intelligent structure is trained using a developed learning algorithm, which consists of both offline and online learning procedures: online learning for fitting OENN substructure and offline learning for adjusting OEANFIS parameters. The learning process is expanded by introducing the learning rate adaptation algorithm, which bases its calculations on the sign of the error difference. Finally, the proposed intelligent controller was experimentally tested for control of a nonlinear multiple-input-multiple-output two rotor aerodynamical system. During the test phase, an additional four related intelligent control logics and default PID-based controllers were used, and tracking performance comparisons were performed. The proposed controller showed notably better online results in comparison to other control algorithms. The major deficiencies of the structure are complexity and noticeably large training computation time, but these drawbacks can be neglected if tracking performances of a dynamical system are of the highest importance.

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.