The topic of this contribution is iterative learning control of a drop-foot stimulator in which a predefined angle profile during the swing phase is realized. Ineffective dorsiflexion is compensated by feedback-controlled stimulation of the muscle tibialis anterior. The ankle joint measurement is based on changes in the bioimpedance (BI) caused by leg movements. A customized four-channel BI measurement system was developed. The suggested control approach and the new measurement method for the joint angle were successfully tested in preliminary experiments with a neurologically intact subject. Reference angle measurements were taken with a marker-based optical system. An almost linear relation between joint angle and BI was found for the angle range applicable during gait. The desired angle trajectory was closely tracked by the iterative learning controller after three gait cycles. The final root mean square tracking error was below 5 degrees.
This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. The proposed FO-INC controls the output voltage of the PV arrays to obtain maximum power point tracking (MPPT). Due to its simplicity and efficiency, the incremental conductance MPPT (INC-MPPT) is one of the most popular algorithms used in the PV scheme. However, owing to the nonlinearity and fractional order (FO) nature of both PV and DC-DC converters, the conventional INC algorithm provides a trade-off between monitoring velocity and tracking precision. Fractional calculus is used to provide an enhanced dynamical model of the PV system to describe nonlinear characteristics. Moreover, three metaheuristic optimization techniques are applied; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and AntLion Optimizer (ALO) are used for tuning the FO parameters of the proposed INC-MPPT. A MATLAB-Simulink-based model of the PV and optimization have been developed and simulated for different INC-MPPT techniques. Different techniques aim to control the boost DC-DC converter towards the MPP. The proposed optimization algorithms are, also, developed and implemented in MATLAB to tune the target parameters. Four performance indices are also introduced in this research to show the reliability of the comparative analysis of the proposed FO-INC with metaheuristic optimization and the conventional INC-MPPT algorithms when applied to a dynamical PV system under rapidly changing weather conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FO-INC as a MPPT control for different climatic conditions with disturbance rejection and robustness analysis.
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