Showing 4 results for Uncertainty
S. Nasrollahi Boroujeni, M. Fathi, A. Ashrafifar,
Volume 35, Issue 1 (9-2016)
Abstract
In this paper, a robust control law is proposed, based on Lyapunov’s theory and sliding mode control theory, in
order to track the angle of attack in nonlinear longitudinal dynamics of a missile. It is assumed that there are unmatched
uncertainties in the nonlinear systems. In the proposed algorithm, the controller gains are optimized by Particle Swarm
Optimization (PSO) algorithm. For this purpose, a cost function is extracted from the output tracking error. Simulation results
show that the proposed algorithm has better performance than conventional Proportional-Integral-Derivative (PID) controller in
the presence of unmatched uncertainties.
M. Rabbani, E. Asgaari, A. Ghavamifar, H. Farrokhi-Asl,
Volume 37, Issue 2 (3-2019)
Abstract
In the recent decades, raw materials and resources have been remarkable issues for researchers; in other words, they play an important role in manufacturing industries or service organizations. On the other hand, the population is increasing every day. An increase in the population means the increased demand for goods or services. Therefore, more resources are needed to deliver services or goods. For this reason, government agencies and environmental agencies have developed and enforced stringent laws against producers and service providers who have exceeded the permissible limits for the environment; in some cases, the use of resources has been even restricted. In the meantime, the supply chain has become one of the major issues that can greatly influence this issue. In this research, the supply chain of the closed loop has been modeled due to uncertainty, disturbances and cost of production. The purpose of this problem has been to minimize the cost of the system in question based on the location decisions, and flow rates between levels and sales. The Lagrangian liberation solution method is used to solve this NP-hard problem. In the end, a numerical example has been employed to test the model and the proposed solution method. The results show that the time of implementation of the large-scale problem with GAMS is higher than that of the proposed method.
N. Cheraghi, M. Miri, M. Rashki,
Volume 39, Issue 1 (8-2020)
Abstract
This paper presents a probabilistic assessment on the free vibration analysis of functionally graded material plates, including layers with magneto-electro-elastic properties, using the 3D solution and surrogate models. The plate is located on an elastic foundation and the intra-layer slipping effect is also considered in the analysis by employing the generalized intra-layer spring model. Due to the high computational cost of the 3D solution in calculating the free vibration frequency of the plate, surrogate models are used. The meta models including kriging method, radial fundamental function method and polynomial response surface method are used to construct the surrogate model. For surrogate models training, the results of the three-dimensional solving method are used. The elastic foundation hardness, the intra-layer slipping effect, the material properties index, and the layer densities are considered as the variables with uncertainty. The three-dimensional solution method is validated through a comparison with other available reference. The results obtained through the surrogate models have been compared to those of the 3D solution formulation, showing a good agreement. The effects of some parameters including the elastic foundation hardness, the intra-layer slipping effect, the density of each layer, and the material properties index on the fundamental frequency of functionally graded material plates are investigated. By using three-dimensional solution method and Kriging Surrogate Model, it is shown that the shear and transverse components of elastic foundation hardness and the density of each layer have the greatest effect on the fundamental frequency of the functionally graded material plates.
R. Zardashti, S. A. Saadatdar Arani , S. M. Hosseini,
Volume 41, Issue 1 (9-2022)
Abstract
In this paper, a robust optimization method is developed to solve the Satellite Launch Vehicle (SLV) trajectory design problem in the presence of uncertainties using a powerful Particle Swarm Optimization (PSO) algorithm. Given the uncertainties such as uncertainties in the actual values of aerodynamic coefficients, engine thrust, and mass in the ascent phase of a SLV, it is important to achieve an optimal trajectory that is robust to these uncertainties; because it improves the flight performance, reduces the workload of the guidance-control system, and increases the reliability of the satellite. For this purpose, first the optimization problem is considered by using the criterion of minimizing the flight time of the SLV as a cost function, and three-dimensional equations of motion as constraints governing the problem. Then, by adding the mean parameters and the standard deviation of uncertainties in the cost function, a robust optimizer model is developed and the algorithm is used to numerically optimize the model. Monte Carlo's perspective has also been used to analyze the results of uncertainties and their continuous feedback to the optimization model. Finally, the optimal trajectory is obtained that is robust to the uncertainties. The resulting simulation results show the accuracy of this claim.
