In this paper, we present an integrated UAV waypoint guidance scheme based on vector field guidance law and fillet path geometry switching state machine. A feedback sliding mode controller and a disturbance observer are conducted to enhance the algorithm performance and thin out the chattering effect due to heading and pitch angle rate of change constraints. Besides, a switching state machine based on fillet path geometry is proposed for reducing cross track error while switching between the desired path waypoints configurations and it is also, reduces the path length for path optimality. A simulation platform has been applied for evaluating algorithm performance and robustness for different missions with different flight conditions; we also study the effect of varying the vector field algorithm parameters and their consequence on the algorithm performance.

In the current paper, nonlinear modelling and dynamical analysis of a surface to surface missile with both aerodynamic and thrust vector control is investigated. Aerodynamic force and moment modelling of the presented missile includes the challenging high angle of attack aerodynamics behaviour and the so-called hybrid control, which utilizes both tail fins for stabilization and rudder vanes as control surfaces. Then, concerning relative rotation between bodies, the missile aerodynamic model is built by Leabedev aerodynamic calculations software. After using the generated aerodynamic data obtained the required aerodynamic stability derivatives, the required transfer functions are resolute based on the equations of motion of the missile. The missile dynamic motion in pitch and roll channel are analysed. The guidance and control strategy are developed through an actuation system and autopilot design in the four channels based on the poles assignment technique. The roll autopilot is constructed to regulate the roll angle of the front body of the missile which is the controlled part, for the command signals generated by the guidance laws both pitch and yaw autopilots are designed to realize. The guidance command is in the form of rate of change of flight path angles of the missile with time.

In this paper, attitude angles estimation utilizing low power consumption and efficient approaches will be focused on. The trade-off between power consumption and efficiency is a leading factor in enhancing the overall performance of the navigation system in most of the autonomous system applications, so Complementary and Mahony filters as accelerometer, gyro, and magnetometer data fusion algorithms with high performance and less computation complexity are utilized. A real time implementation for Complementary and Mahony filters was achieved using raw data from Vector NAV VN-100 IMU.Estimated Euler angels using each filter were compared with Euler angels from Vector NAV VN-100 which are estimated using Kalman filter. A comparative study was carried out for analyzing the performance of each algorithm. The study presented that the Complementary filter introduces better efficiency than Mahony filter in case of stationary situations and steady flight. On the other hand, Mahony filter presents better efficiency than Complementary filter in case of motion and high dynamics.

The evaluation of propulsion systems used in UAVs is of paramount importance to enhance the flight endurance, increase the flight control performance, and minimize the power consumption. This evaluation, however, is typically performed experimentally after the preliminary hardware design of the UAV is completed, which tends to be expensive and time-consuming. In this paper, a comprehensive theoretical UAV propulsion system assessment is proposed to assess both static and dynamic performance characteristics via an integrated simulation model. The approach encompasses the electromechanical dynamics of both the motor and its controller. The proposed analytical model estimates the propeller and motor combination performance with the overarching goal of enhancing the overall efficiency of the aircraft propulsion system before expensive costs are incurred. The model embraces an advanced blade element momentum theory underpinned by the development of a novel mechanism to predict the propeller performance under low Reynolds number conditions. The propeller model utilizes XFOIL and various factors, including post-stall effects, 3D correction, Reynolds number fluctuations, and tip loss corrections to predict the corresponding aerodynamic loads. Computational fluid dynamics are used to corroborate the dynamic formulations followed by extensive experimental tests to validate the proposed estimation methodology.

The four-tank system is a common industrial setup used in water treatment, chemical processing, and pharmaceutical applications. This work proposes model predictive control (MPC) for precise controlling of liquid levels of the four tanks considering the flow rate limits. The proposed approach integrates MPC with an ANFIS to improve real-time state estimation by adapting to nonlinear and time-varying dynamics and effectively handling external disturbances and measurement noise. Additionally, adaptive particle swarm optimization is employed to fine-tune the ANFIS network parameters, ensuring accurate and adaptive modeling to ensure accurate state estimation. The research presents a comparative synthesis between linear and nonlinear MPC controllers under different operating conditions, disturbances, and uncertainties. The MPC approaches are implemented using the $$\mu $$ μ aoMPC and ACADO toolkits to accomplish real-time requirements to solve the optimization problem. Furthermore, processor-in-the-loop experiments have been utilized to validate the numerical results, confirming the real-time applicability and computational efficiency. The results demonstrate that the NMPC controller, supplemented by ANFIS, achieves superior tracking accuracy, faster disturbance rejection, and reduced steady-state error.

A hybrid Cuckoo search algorithm and NSGA-II algorithm called NSGA-II-Cuckoo is proposed and applied to find a set of non-dominated solutions for multi-objective supply chain problem. In NSGA-II-Cuckoo, the cuckoo search algorithm is applied to generate the initial population that considers as a good start for another algorithm to reduce the time, avoids the local optima entrapment so its effect on the accuracy of the final solutions obtained by the NSGA-II algorithm. The presented results clarify the enhanced performance for the NSGA-II-Cuckoo algorithm, which provides a sufficient stability and robustness for solving multiobjective supply chain problem.