practical implementation

This paper addresses the design and practical implementation of a constrained extended state observer for integration between an inertial navigation system (INS) and a global navigation satellite system (GNSS) navigation system in the presence of model uncertainties and inertial sensor errors. The use of physical constraints in the observer design can achieve an accurate and reliable model for the inertial navigation system during GNSS outages. The proposed method provides an estimate of this variable alongside the state variables of the inertial navigation system by considering the term including model uncertainties and inertial sensor errors as a new state variable. Additionally, the use of motion and environmental constraints in the proposed observer, including non-holonomic constraints and altitude constraints, improves estimation accuracy, reduces error accumulation, and increases the dynamic stability of the estimates. The performance of the proposed observer is evaluated through vehicle tests in a real-world test environment. The results indicate that the model uncertainties and inertial sensor errors in the inertial navigation system can be estimated in real-time. Thus, the designed observer can provide reliable estimates of the state variables by using GNSS information during its availability and utilizing physical constraints during GNSS outages. Furthermore, the proposed algorithm is fast due to its low computational load, making it suitable for practical implementation.

In this paper a neural observer is deigned to control a Quadrotor Drone. Quadrotor is a type of flying robot which can fly vertically and has a simple structure. This robot is one of the best models of flying robot that is considered by many researchers recently. Because of nonlinear dynamics of the system, Stability of the control process has an important role in this robot. In this study first, a PD controller is designed to track the desired state and stabilizing the quadrotor based on particle swarm optimization algorithm. Nonlinear observer is then synthesized in order to estimate the unmeasured states. Then a neural network observer is designed and trained based on data that extract of nonlinear observer. After that, simulation results are also provided in order to illustrate the performances of the proposed controller and observer. Finally In addition to simulation we have practically implemented these control and observer methods on a quadrotor test bench. Practical implementation results demonstrate the effectiveness of the presented method.

Quadrotor is a Flying robot which can fly vertically and has a simple structure. Because of nonlinear dynamics of the system, Stability of the control process has an important role in this robot. In this paper, a neural controller is designed to stabilize the quadrotor. The neural controller is used to stabilize the attitude of the quadrotor. We first designed a PD controller using Ziegler Nichols method, then an online learner neural controller is trained for tuning the parameters of this PD controller. To verify these controllers first a simulation performed in the Simulink environment of the Matlab. In addition to simulation we have practically implemented these control methods on a Quadrotor test bench. Practical implementation results demonstrate the effectiveness of the presented method.