Performance and improvement of flight efficiency at various velocities for flight systems, in particular, rotorcrafts, with specific complexities in motion and its nonlinear equations are always of particular interest to researchers in the aerial and control domains. In this research, a new control algorithm is addressed based on the complete nonlinear Unmanned Rotorcraft (UR) model and its four main inputs. Exploiting state feedback and Polytopic Linear Parameter Varying (PLPV) modeling and using Linear Matrix Inequality (LMI), the velocity control problem is investigated. The trim points of the system are produced under different velocity control conditions. State feedback control gain matrix which plays a main role in producing the ultimate control signal, is computed by solving a set of LMIs under various conditions. Finally, instead of using a Nonlinear model, a Polytopic model is used for controller synthesis. With this goal, different scenarios for the proposed flight velocity control (in different dynamic ranges, minimum velocity to maximum velocity) are implemented. The simulation results demonstrate a very good performance of the proposed controller in the basis of PLPV modelling. It can be concluded that the proposed manner is useful to overcome the disruptions imposed on the flight system due to the changes in the equilibrium points and the uncertainties of the parameters and/or possible errors due to the unwanted possibilities in the system. Manuscript profile

In this paper, a new approach to the use of genetic algorithms and the predictive control method, for goal tracking is presented. A hypothetical rocket is modelled for the analyses. Rocket guidance algorithm is developed to achieve a desired mission goal according to some performance criteria and the imposed constraints. Given that goals can be fixed or moving, we have focused and expanded on this issue in this study and also the dynamic modelling of flying objects with six-degrees-of-freedom (DOF) is used to make the design more similar to the actual model. The predictive control method is used to predict the next step of rocket and aim movement. At each step of the problem, the rocket distance to the aim is obtained, and a trajectory is predicted to move the rocket towards the purpose. The objective function of this problem, in addition to the distance from the rocket position to the target, are also parameters of the dynamic model of the rocket. Therefore, these parameters are optimized at each step of the problem solving. Ultimately, the rocket strikes the intended aim by following this optimal path. Finally, for the validation of the model, numerical results are obtained for both Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Simulation results demonstrate the effectiveness and feasibility of the proposed optimization technique. Manuscript profile

This paper presents a new method for modeling and Attitude Control of Autonomous Helicopters (A.H.) based on a polytopic linear parameter varying approach using parameter set mapping with the Principal Component Analysis (PCA). The polytopic LPV model is extracted based on angular velocities and Euler angles, that is influenced by flopping angles, by generating a set of data over the different trim points. Because of the high volume of trim data, parameter set mapping based on (PCA) is used to reduce the parameter set dimension. State feedback control law is proposed to stabilize the system by introducing a Linear Matrix Inequality (LMI) set over the vertices models. The proposed controller is performed for an Autonomous Helicopter in different scenarios. All the scenarios are investigated with the PCA algorithm as a technique for reducing the computational volume and increasing the speed of solving the LMI set. The simulation results of implementing the planned controller on the nonlinear model of an autonomous helicopter in different scenarios show the effectiveness of the proposed scheme. Manuscript profile