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The precision positioning systems generally need a good controller to achieve a fast response, high accuracy and robustness. In addition, ease, simplicity of controller design structure and high motion control performance are very important for practical applications. For satisfying these requirements, nominal characteristic trajectory (NCT) with proportional integral (PI) and notch filter (NF) as a compensator has been proposed as a practical control method for two-mass rotary PTP positioning systems. However, the effect of the actuator saturation cannot be completely compensated due to integrator windup when the object parameter varies. This paper presents a method to further improve nominal characteristic trajectory following (NCTF) controller to overcome the problem of integrator windup by adopting PI anti-windup schemes. The improved NCTF controller is evaluated experimentally using two-mass rotary positioning systems. The effect of the design parameters on the robustness of the improved NCTF with anti-windup integrator controller is evaluated and compared with NCTF without antiwindup integrator and the equivalent PID controller. The results show that the improved NCTF controller is effective to compensate the effect of integrator windup. [ABSTRACT FROM AUTHOR]

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems. [ABSTRACT FROM AUTHOR]