Smart Algorithms to Control a Variable Speed Wind Turbine

In this paper, a robust adaptive fuzzy neural network sliding mode (AFNNSM) control design is proposed to maximize the captured energy for a variable speed wind turbine and to minimize the efforts of the drive shaft. Fuzzy neural network (FNN) is used to improve the mathematical system model, by the prediction of model unknown function, which is used by the Sliding mode control approach (SMC) and enables a lower switching gain to be used despite the presence of large uncertainties. As a result, the used robust control action did not exhibit any chattering behavior. This FNN is trained on-line using the backpropagation algorithm (BP). The particle swarm optimization (PSO) algorithm is used in this study to optimize the learning rate of BP algorithm in order to improve the network performance in term of the speed of convergence. The stability is shown by the Lyapunov theory and the trajectory tracking errors converge to zero without any oscillatory behavior. Simulations illustrate the effectiveness of the designed method

Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm

Online adaptive temperature control by field-programmable gate array (FPGA)-implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is realized according to the structure of Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network. Direct inverse control configuration is used. To design RFC offline, evolutionary fuzzy controller using the hybrid of the Simplex method and particle swarm optimization (SPSO) is proposed. In SPSO, each RFC corresponds to a particle, and all the free parameters in RFC are optimally searched. We use the PSO to find a good solution globally, and the incorporation of the Simplex method helps find a better solution around the local region of the best solution found by PSO so far. Then, online adaptive temperature control with ARFC chip implemented by FPGA is proposed. In the ARFC chip, the consequent parameters of all rules are all tuned online using gradient descent. To verify the performance of the ARK chip, experiments on a water bath temperature system are performed

Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

In this research, the improved mass spring model is presented to simulate the human liver deformation. The underlying MSM is redesigned where fuzzy knowledge-based approaches are implemented to determine the stiffness values. Results show that fuzzy approaches are in very good agreement to the benchmark model. The novelty of this research is that for liver deformation in particular, no specific contributions in the literature exist reporting on real-time knowledge-based fuzzy MSM for liver deformation