The increase of satellite’s dimensions has caused flexibility and formation of uncertainty in their model. This is because of space missions being more complex and using light moving structures in satellites. Satellites are also encountered with various circumferential disturbance torques. This uncertainty in model and disturbance torques will cause undesirable performance of satellites’ attitude control system. So, for attitude control of these satellites, those methods should be used which are robust to uncertainty of the plant’s model and can reject the effects of disturbances and the measurement noise. One of these methods is the robust control design method. But, because of pole’s place of these satellite’s dynamics equations, the designing procedure of robust control will face difficulties. In this paper, by using an internal feedback as a novel idea, the satellite’s dynamics equations are changed in such a way that the poles will be placed in proper locations. Then, for these new equations, by regarding the effects of flexibility as uncertainty and considering the uncertainty in inertia matrix of satellite, an H∞ controller has been designed and for better performance, a μ-controller has been improved. Afterwards, these two controllers are analyzed and compared for the original dynamic equations, not for the modified ones. Also, for comparison, a classic controller has been also designed for the original plant and eventually all these three controllers are compared with each other.
In this paper, Generalized Predictive Control (GPC) algorithm is implemented to control an earth station antenna. Nonlinear term in motors caused by gearbox or other parts is modeled by a backlash block. Simulation results show the effectiveness of GPC method for robust control in the presence of backlash nonlinearity without a priori knowledge about upper and lower bounds of backlash. Also, adaptation mechanism as a self tuning predictive control is used to conquer environment changing.
This paper considers the problem of tracking the global maximum power point (GMPP) in partially shaded conditions (PSCs) as a multiobjective optimization problem and solves it using a novel multiobjective optimization algorithm on the basis of Bayesian optimization formulation. Bayesian optimization is a metamodel-based global optimization method that is able to balance exploration and exploitation. The Pareto solutions are obtained by using a multiobjective Bayesian optimization algorithm. Also, a new acquisition function is proposed to improve the diversity and convergence of the Pareto solutions. Two objective functions are introduced to remove the large tracking errors and oscillations of the operating point around the GMPP. The suggested method is implemented online for GMPP tracking so that the suggested method monitors any change in environmental conditions and generates the optimal duty cycle for the DC-DC converter for the GMPP tracking (GMPPT) by the PV array. Several multipeak PSC scenarios are implemented and simulated to show efficiency of the suggested approach. The MATLAB/SIMULINK is employed to implement a photovoltaic (PV) system comprising a PV array, a boost converter, and the proposed multiobjective Bayesian optimization algorithm (MOBOA). The simulation results show a very satisfactory performance of the MOBOA in terms of transient state and steady-state oscillations and tracking speed.
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