A novel Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) is presented for the modeling of renewable energy systems. Not only does this paper present a new architecture of the type-2 fuzzy system (T2FS) for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also, it introduces an exquisite technique to acquire an optimal number of membership functions (MFs) and their corresponding rules. Using nonlinear functions in the “Then” part of fuzzy rules, introducing a new mechanism in structure learning, using an adaptive learning rate and performing convergence analysis of the learning algorithm are the innovations of this paper. Another novel innovation is using optimization techniques (including pruning fuzzy rules, initial adjustment of MFs). Next, a solar photovoltaic cell and a wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge as convincing. The root-mean-square-error (RMSE) is less than 0.006 and the number of fuzzy rules is equal to or less than four rules, which indicates the very good performance of the presented fuzzy neural network. Finally, the obtained model is used for the first time for a geographical area to examine the feasibility of renewable energies.

Uncertainty in parameters of a tunable Micro Electro Mechanical System (MEMS) capacitor, as the main component of AC Voltage Reference Source (VRS), is significant to obtain a precise output voltage unaffected by disturbance and noise. Although attempts are done to craft the capacitor with desired physical specification and improve accuracy of parameters, parametric uncertainties transpire in manufacturing due to micro-machining shortcomings. First off, this paper remarks design of a Robust Adaptive Sliding Mode Controller (RASMC) and its application for the MEMS AC VRS for the first time so that it can produce a stable precise regulated output at presence of uncertainties and disturbance. Secondly, it makes a comparison between the proposed novel controller and our previously designed ASMC. The comparison reassures one of robustness at exacerbating conditions in the tunable capacitor dynamics. The novelty is employment of the dead zone-based ASMC to manage parametric uncertainty and disturbance in the MEMS tunable capacitor. It also contributes to adaptive sliding mode control of the dynamics with stiffness and damping coefficients, which are non-slowly-varying time-dependent parameters, approaching dead-zone method. Uncertainty in the plate mass besides bounded time-variant uncertainty in stiffness and damping are considered. These restrictions were not deemed in the previous design.

The accuracy of physical parameters of a tunable MEMS capacitor, as the major part of MEMS AC voltage reference, is of great importance to achieve an accurate output voltage free of the malfunctioning noise and disturbance. Even though strenuous endeavors are made to fabricate MEMS tunable capacitors with desiderated accurate physical characteristics and ameliorate exactness of physical parameters’ values, parametric uncertainties ineluctably emerge in fabrication process attributable to imperfections in micromachining process. First off, this paper considers applying an adaptive sliding mode controller design in the MEMS AC voltage reference source so that it is capable of giving off a well-regulated output voltage in defiance of jumbling parametric uncertainties in the plant dynamics and also aggravating external disturbance imposed on the system. Secondly, it puts an investigatory comparison with the designed model reference adaptive controller and the pole-placement state feedback one into one’s prospective. Not only does the tuned adaptive sliding mode controller show remarkable robustness against slow parameter variation and external disturbance being compared to the pole-placement state feedback one, but also it immensely gets robust against the external disturbance in comparison with the conventional adaptive controller. The simulation results are promising.

MEMS Tunable Capacitors (TC) are major elements in AC Voltage Reference Sources (VRS). Physical parametric uncertainties, external electrostatic disturbance, and measurement noise malfunction their operation and ruin the preciseness of the VRS output voltage. Our objective problem is the design of a controller to cope with the mentioned parametric uncertainties, noise, and disturbance. Our applied method is the design and employment of a Proportional Integral (PI) Fuzzy Adaptive Sliding Mode Controller (FASMC). Both terms of matched and unmatched uncertainties as well as external disturbance and measurement noise are all addressed in this paper to generate a stable VRS output for the first time. Not only does the paper contribute to the employment of a FASMC to enhance robustness in the drive of the capacitor, but also it benefits the reduction of the chattering effect due to fuzzy regulation in the switching term of the control law. Moreover, the automatic fuzzy adjustment in the controller, which is used for estimation of the coefficients in the sliding surface error dynamical equation, facilitates the specification of those coefficients which can be time-consuming in simulation affairs. Clarifying the importance of the proposed fuzzy adaptive sliding mode controller, this paper reviews some previous controllers for MEMS TC in comparison with the proposed fuzzy controller, demonstrating its enhancement in comparison with previous schemes.