In this paper, trajectory tracking for a quadrotor unmanned aerial vehicle (UAV) is considered in the presence of a cable-suspended payload and wind as unknown disturbances. It is assumed that the wind disturbance is slowly time varying and affects quadrotor position and orientation independently. Nonlinear robust strategies, such as backstepping and sliding mode control could be used for trajectory tracking; however, they fail to stabilize the system in the presence of payload or wind, or both together. We have proposed a combined backstepping and super-twisting integral sliding mode strategy to stabilize the system. Conventional sliding mode control suggests discontinuous control signals and suffers from the chattering phenomenon whereas its super-twisting integral version suggests continuous control signals, which makes it implementable and chattering free.

Disastrous and hazardous events (eg, natural disasters and cyber‐physical attacks) have significantly increased in power systems, which drastically affect their performance. Different research works have been recently introduced in the literature, aiming at thoroughly evaluating power system resilience against various types of disastrous and hazardous events. In this article, a review of these research works is presented. Moreover, the main differences between the concept of resiliency and the concepts of accessibility, durability, flexibility, hardening, maintainability, reliability, stability, survivability, sustainability, and vulnerability are described. Additionally, various resiliency indices are presented, and different techniques to increase power system resilience against disastrous and hazardous events are reviewed. Furthermore, uncertainty handling approaches used in the literature to analyze power system resiliency are discussed. Some concluding remarks are also presented.

This paper investigates on the development and implementation of a high integrity navigation system based on the combined use of the Global Positioning System (GPS) and an inertial measurement unit (IMU) for land vehicle applications. The complementary properties of the GPS and the INS have motivated several works dealing with their fusion by using a Kalman Filter. The conventional kalman filter has a fix error covariance matrix in all times of processing. Multi-sensor based navigation system that is implemented in this paper is called data synchronization. Also, multi-rate operations that are compared with conventional Kalman filtering has fix error covariance matrix. Therefore, when GPS outage occurred we have improper treat by kalman filter. In this paper we present an Adaptive method instead of conventional methods. It is shown that proposed method has a better performance rather than conventional method. Experimental results show the effectiveness of the GPS/INS integrated system.

In this paper, the robust control problem is tackled by employing the state-dependent Riccati equation (SDRE) for uncertain systems with unmeasurable states subject to mismatched time-varying disturbances. The proposed observer-based robust (OBR) controller is applied to two highly nonlinear, coupled and large robotic systems: namely a manipulator presenting joint flexibility due to deformation of the power transmission elements between the actuator and the robot known as flexible-joint robot (FJR) and also an FJR incorporating geared permanent magnet DC motor dynamics in its dynamic model called electrical flexible-joint robot (EFJR). A novel state-dependent coefficient (SDC) form is introduced for uncertain EFJRs. Rather than coping with the OBR control problem for such complex uncertain robotic systems, the main idea is to solve an equivalent nonlinear optimal control problem where the uncertainty and disturbance bounds are incorporated in the performance index. The stability proof is presented. Solving the complicated robust control problem for FJRs and EFJRs subject to uncertainty and disturbances via a simple and flexible nonlinear optimal approach and no need of state measurement are the main advantages of the proposed control method. Finally, simulation results are included to verify the efficiency and superiority of the control scheme.

The aim of this research is to solve the Hamilton–Jacobi–Bellman equation (HJB) arising in nonlinear optimal problem using Adomian decomposition method. First Riccati equation with matrix variable coefficients, arising in linear optimal and robust control approach, is considered. By using the Adomian method, we consider an analytical approximation of the solution of nonlinear differential Riccati equation. An application in optimal control is presented. The solution in different order of approximations and different methods of approximation will be compared with respect to accuracy. Then the HJB equation, obtained in nonlinear optimal approach, is considered and an analytical approximation of the solution of it, using Adomian method, is presented.