A novel robust proportional-integral (PI) adaptive observer design for chaos synchronization

In this paper, chaos synchronization in the presence of parameter uncertainty, observer gain perturbation and exogenous input disturbance is considered. A nonlinear non-fragile proportional-integral （PI） adaptive observer is designed for the synchronization of chaotic systems; its stability conditions based on the Lyapunov technique are derived. The observer proportional and integral gains, by converting the conditions into linear matrix inequality （LMI）, are optimally selected from solutions that satisfy the observer stability conditions such that the effect of disturbance on the synchronization error becomes minimized. To show the effectiveness of the proposed method, simulation results for the synchronization of a Lorenz chaotic system with unknown parameters in the presence of an exogenous input disturbance and abrupt gain perturbation are reported.

Robust pre-specified time synchronization of chaotic systems by employing time-varying switching surfaces in the sliding mode control scheme

In the conventional chaos synchronization methods, the time at which two chaotic systems are synchronized, is usually unknown and depends on initial conditions. In this work based on Lyapunov stability theory a sliding mode controller with time-varying switching surfaces is proposed to achieve chaos synchronization at a pre-specified time for the first time. The proposed controller is able to synchronize chaotic systems precisely at any time when we want. Moreover, by choosing the time-varying switching surfaces in a way that the reaching phase is eliminated, the synchronization becomes robust to uncertainties and exogenous disturbances. Simulation results are presented to show the effectiveness of the proposed method of stabilizing and synchronizing chaotic systems with complete robustness to uncertainty and disturbances exactly at a pre-specified time.

Multi-criteria prioritization of the renewable power plants in Australia using the fuzzy logic in decision-making method(FMCDM)

The presented study focused on developing an innovative decision-making framework to select the best renewable-power-plant technologies,considering comprehensive techno-economic and environmental variables.Due to the favourable conditions,Australia was selected as the case study.A fuzzy-logic method and analytical hierarchy process were applied to prioritize different renewable-energy power plants.The techno-economic factors included levelized cost of energy,initial cost,simple payback time,and operation and maintenance costs along with environmental factors including carbon payback time,energy payback time and greenhouse-gas emissions were used to rank the power plants.The results showed that the capital cost and simple payback time had the highest priority from an economic point of view.In comparison,greenhouse-gas emissions and carbon payback time were the dominant environmental factors.The analysis results provided economic and environmental priority tables for developing different power plants in the current state and a future scenario by 2030.The fuzzy results and pairwise composite matrix of alternatives indicated that the onshore wind,offshore wind,single-axis tracker polycrystalline photovoltaic,single-axis tracker monocrystalline photovoltaic,fix-tilted polycrystalline photovoltaic and fix-tilted monocrystalline photovoltaic scored the highest in the current state.In contrast,by 2030,the single-axis tracker photovoltaic power plants will be the best choice in the future scenario in Australia.Finally,the results were used and analysed to recommend and suggest several policy implementations and future research studies.