The robotic airship can provide a promising aerostatic platform for many potential applications. These applications require a precise autonomous trajectory tracking control for airship. Airship has a nonlinear and uncertain dynamics. It is prone to wind disturbances that offer a challenge for a trajectory tracking control design. This paper addresses the airship trajectory tracking problem having time varying reference path. A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed parameters. It uses extended Kalman filter (EKF) for uncertainty and disturbance estimation. The estimated parameters are used by sliding mode controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, EKF based SMC (ESMC), is used as a robust solution to track airship trajectory. The proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model inaccuracies. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. The simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC under model uncertainties and wind disturbances.

An airship is a lighter than air, aerial vehicle whose model is based on dynamic, aerodynamic, aerostatic and propulsion forces and torques. Apart from other, aerodynamic forces and toques are difficult to measure. In this work, an estimation scheme for aerodynamic forces and torques based on the Extended Kalman Filter (EKF) is presented. It is assumed that the airship attitude and position estimates are available. EKF estimates the airship body axes linear and angular velocities and aerodynamic forces and torques. As the method measures a complete aerodynamic model instead of measuring its individual parameters by utilizing minimum auxiliary state variables, it is computationally non-intensive and can provide online aerodynamic model information that can be used in controller implementation in a real-time environment. Nonlinear simulation environment is developed for the experimental airship and EKF performance is evaluated. For validating the estimator’s performance, 3-σ uncertainty bounds and error analysis, estimator convergence analysis and it’s closed-loop simulations with Sliding Mode Controller have been performed. The simulation results show that EKF successfully estimates the airship states and aerodynamic forces and torques with minimum estimation error enhancing the model-based nonlinear controller performance.

An airship is a lighter-than-air vehicle that offers static lift without consuming much power. This property makes it a potential candidate for many commercial applications. The target applications include rescue operations, surveillance, communication, a data collection platform for research activities and payload delivery that requires hovering capabilities, etc. To successfully apply airships in these applications and many others, airship autonomous control development is of paramount importance. To accomplish this goal, the initial step is to model airship dynamics that cover the complete flight envelope accurately. The goal is to develop a flight simulator that can test the advanced autonomous control algorithms. In the proposed work, first, the nonlinear six-degree-of-freedom equations of motion are developed using Newtonian mechanics. These equations are used to develop a flight simulator for the University of Engineering and Technology Taxila (UETT) airship. Airship responses to different control inputs are investigated, and the results are validated with the available data in the literature for other airship projects. Also, the obtained longitudinal and lateral eigenmodes show good agreement with the experimental flight data of the UETT airship. The extensive simulation results favour the dynamic analysis of the airship.

This study develops a gain-scheduled Linear Quadratic Regulator (LQR) for the final landing burn of a reusable booster. Using thrust vector control (TVC) and thrust modulation as the primary braking and adjustment force, the vehicle booster is made to land with minimal velocity at the correct coordinates. The Falcon-9-inspired descent trajectory is used as a reference point for the landing. The nonlinear six-degree-of-freedom system is linearized at selected operating points along the reference trajectory, which includes different initial velocity, vehicle wet mass, and starting altitude. At each point, a controllable ten-state reduced model is used to compute a local LQR gain, and altitude interpolation is used to allow the controller to adjust the gain, reference state, and feedforward command. The controller is tested in eight nonlinear cases, including nominal descent, initial lateral and altitude offsets, a sustained thrust command disturbance, gimbal bias disturbance, and combined actuator–thrust and actuator–thrust–lateral disturbances. The vehicle makes soft landings for all the cases with vertical velocities between −0.713 m/s and −0.844 m/s, and a maximum final lateral error of 1.257 m, a maximum tilt of 5.22 deg, and gimbal demand within the imposed ±8 deg limit. In the lateral offset case, an initial 22.36 m horizontal error is reduced to about 0.005 m. A 2000-case Monte Carlo study of initial-state and mass dispersions gave a 100% pass rate, with touchdown velocities from −0.783 m/s to −0.654 m/s and a maximum final lateral error of 0.250 m. Within the assumptions of the present simulation model, the results show that LQR can be deployed as a practical baseline controller for reusable-booster landing burn studies.

Accurate prediction of store trajectories at the point of release from an unmanned/manned aircraft is an essential requirement for safety and precision. Captive Trajectory System (CTS) is a well-known feature of wind-tunnel testing to simulate the dynamics of store separation. To accurately replicate real-world aerodynamic conditions based on measured forces and moments, it utilizes a six-degree-of-freedom (6-DOF) robotic arm controlled by a closed-loop control system that solves the store’s equations of motion. In this study, a wing–pylon–store configuration is used as a sample case, and published experimental trajectories are used as input. A 6-DOF robotic arm named ROBO-S is designed to follow these trajectories in a CTS setup. The kinematic analysis of ROBO-S is performed in this study. The Denavit–Hartenberg (DH) method is used for the calculation of forward kinematics, whereas geometric techniques are used for inverse kinematics calculations. A simulation of kinematic analysis is performed in MATLAB R2021a. The mechanical design of ROBO-S is carried out in PTC CREO 9.0. MATLAB simulations confirm that the robotic arm can follow the trajectory obtained from published experimental results. To demonstrate the feasibility of the design, the robotic arm is fabricated using 3D printing. The results demonstrate the potential of the developed system in accurately following trajectories for wind-tunnel testing applications.