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Robotic manipulators are widely used in modern industry. In order for robot manipulators to achieve trajectory tracking control, the end-effectors must move precisely along the given trajectories. The nonlinearity, strong coupling, and uncertainty of the system make trajectory tracking very complicated and difficult. An approach for robust feedback control is presented in this paper as a solution to this problem. The design relies on active disturbance rejection control to estimate unknown disturbances affecting the plant. The controlled system is an uncertain two-link manipulator with elastic gear transmissions, described by nonlinear friction and elasticity. It is based on an industrial benchmark problem. The model has two significant uncertainties: parametric uncertainty as well as external disturbances that affect tools and motors. A simulation-based evaluation of the proposed control method and a comparison of its performance, considering specific robustness requirements and trajectory tracking, are presented. The comparison includes the baseline controller, the QFD (Quality Function Deployment) method, and PD-CNN (PD Control Compensation based on the Cascade Neural Network). Computer simulations of the proposed controller are carried out to validate the ABB (Asea Brown Boveri) industrial benchmark. The simulation results demonstrate superiority in meeting stability and target value requirements. The stability and ultimate boundedness of the tracking error of ADRC (Active Disturbance Rejection Control) are demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, a method for real time monitoring and fault diagnosis in photovoltaic systems is proposed. This approach is based on a comparison between the performances of a faulty photovoltaic module, with its accurate model by quantifying the specific differential residue that will be associated with it. The electrical signature of each default will be fixed by considering the deformations induced on the I-V curves. Some faults, such as: interconnection resistance faults and different shading patterns are considered. The proposed technique can be generalized and extended to more types of faults. The fault diagnosis will be determined by fixing a normal and a fault threshold for each fault. These thresholds are calculated based on the Euclidean norm between ideal and normal measurement or between ideal and fault mode measurement. Each threshold is set in a range bounded by the minimum and maximum values of the differential residue obtained for the considered fault. The proposed approach provides identification of faults by calculating their specific threshold ranges. This method allows the instantaneous monitoring of the electrical power delivered by the photovoltaic system. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel fuzzy logic controller, for tracking the maximum power point of boost-based PV system. The fuzzy logic controller is used to improve its dynamic response with sudden irradiations and temperature conditions. This paper deals with the optimal adjustment of scaling factors for fuzzy controllers, these parameters affects the controller performance in term of speed, accuracy and stability. Hence, scaling factors are optimized by using Particle Swarm Optimization algorithm. Numerical simulations are carried out to highlight the tracking control performance of the fuzzy-MPPT using PSO algorithm for tuning the scaling factors. Then the optimized FLC is applied to DC-DC Boost converter. This paper provides reliable information on the performance of the optimized fuzzy-MPPT strategy which can be used by system designers to improve the overall efficiency and reduce the cost of PV system application. [ABSTRACT FROM AUTHOR]

In this work we present a faults detection method for photovoltaic systems (PVS). This method is based on the calculation of sets of parameters of a PV module in different operating conditions, by means of a Neuro-Fuzzy approach. The PV system status is determined by evaluation and comparison of norms based on the aforementioned parameters, with threshold values. This intelligent system developed in Matlab&Simulink environment, consists on the study of the crucial information that the six parameters in normal and faulty condition contain. They are calculated using the I-V curves and synthesized by “hybrid” models. Results show that the diagnosis system is able to discern between normal and faulty operation conditions and with the same defective existence of noise and disturbances. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents, after a comparative review of the most often used algorithms, a control methodology that clarifies the control problems and shows how to design a robust control to track the maximum power point in VSAS. This approach clarifies the rationale behind MPP dynamic tracking problem and shows how to be optimal. This technique called VSAS (Variable Structure Automated Systems) is compared with some techniques existing in literature like PO, HC, InCond and SMC approach. The maximum power is computed online using a Modified Enhanced Perturb and Observe (MEPO) algorithm and a Robust Unified Control Algorithm (RUCA). The proposed MPPT has several advantages: simplicity, high convergence speed, robustness and is independent on PV array characteristics. The algorithm is tested under various operating conditions. The results show that the MPPT is tracked even under sudden change of irradiation level. [Copyright &y& Elsevier]

In this paper, an experimental analysis and validation of a simple reconfigurable photovoltaic (PV) array is carried out. An assessment of a new reconfiguration method based on fuzzy logic (FL) under partial shading conditions is introduced. Furthermore, a recursive least squares based irradiance estimator is proposed aiming to reduce the investment cost of the dynamic PV array. An experimental comparison with other estimators showed the high precision of the proposed estimator. The estimation error has decreased by an average of 10% compared to the first estimator (based on the PV current and voltage measurement) and by 4.28%compared to the second estimator(based on the PV current measurement). On the other hand, the results validated the FL Controller ability to switch to the appropriate configuration under prevailing shading conditions. The method was tested for a simple configuration, however it could be generalized for small-scale configurations as residential house (average power output equal to 5 kWh). To evaluate the performance of the FL method an extended simulation of dynamic PV array of 16 PV modules is also realized. The mismatch loss is mitigated by nearly 50% compared to fixed Total-Cross-Tied and 8% compared to basic Irradiance Equalization techniques. • Experimental assessment of a reconfigurable small-scale photovoltaic array is performed. • Reconfiguration method based on fuzzy logic under partial shading conditions is proposed. • A new irradiance estimator based on recursive least squares is proposed. • Real time comparison showed the high accuracy of the proposed estimator. • Energy loss in the experienced reconfigurable photovoltaic array is significantly reduced. [ABSTRACT FROM AUTHOR]

Maximum Power Point Tracking (MPPT) strategy is necessary to extract the maximum power production of a Photovoltaic (PV) system. Since the PV has nonlinear dynamics, it is more suitable to use a nonlinear MPPT controller to improve the tracking efficiency. The modelling and control of most systems in real-time are not fully precise and introduce some variations such as : the steady state error and ripples in the system outputs. In this paper, a classical sliding mode controller has been designed and applied experimentally to the PV system to achieve the Maximum Power Point (MPP). The results show that the system responses present chattering phenomena with no negligible state error. In order to reduce these drawbacks (chattering and steady-state error), an Integral Backstepping combined with a discontinuous Sliding Mode Controller (IBSMC) is proposed and applied experimentally to the PV system. The obtained experimental results for the two controllers are compared under the same weather conditions, in terms of chattering phenomena and state error. As a result, the proposed hybrid controller (IBSMC) has achieved high dynamic system performances. Moreover, the stability of this system has been proved using Lyapunov stability criteria. • Experimental validation of a new Integral Backstepping Sliding Mode Controller for PV MPP tracking. • Ripples reduction and steady state error minimization successfully achieved by the Integral Backstepping sliding mode controller. • Experimental comparison with a classical Sliding Mode Controller, under the same conditions of weather, in terms of ripples and state error minimization. [ABSTRACT FROM AUTHOR]