Adaptive sliding-mode-backstepping trajectory tracking control of underactuated airships

The problem of trajectory tracking control for an underactuated stratospheric airship with model parameter uncertainties and wind disturbances is addressed in the paper. An adaptive backstepping sliding-mode controller is designed from the airship nonlinear dynamics model. The proposed controller has a two-level structure for trajectory guidance, tracking and stability, and the developed controller, based on nonlinear adaptive sliding-mode backstepping method, provides airship attitude and velocity control for the entire flight process. Furthermore, an active set based weighted least square algorithm is applied to find the optimal control surface inputs and the thruster commands under constraints of actuator saturation. The closed-loop system of trajectory tracking control plant is proved to be globally asymptotically stable by using Lyapunov theory. By comparing with traditional backstepping control and PID design, the results obtained demonstrate the capacity of the airship to execute a realistic trajectory tracking mission under two cases of lateral- and roll- underactuations, even in the presence of aerodynamic coefficient uncertainties, and wind disturbances

Estimation of airship states and model uncertainties using nonlinear estimators

This Airships are lighter than air vehicles and due to their growing number of applications, they are becoming attractive for the research community. Most of the applications require an airship autonomous flight controller which needs an accurate model and state information. Usually, airship states are affected by noise and states information can be lost in the case of sensor's faults, while airship model is affected by model inaccuracies and model uncertainties. This paper presents the application of nonlinear and Bayesian estimators for estimating the states and model uncertainties of neutrally buoyant airship. It is considered that minimum sensor measurements are available, and data is corrupted with process and measurement noise. A novel lumped model uncertainty estimation approach is formulated where airship model is augmented with six extra state variables capturing the model uncertainty of the airship. The designed estimator estimates the airship model uncertainty along with its states. Nonlinear estimators, Extended Kalman Filter and Unscented Kalman Filter are designed for estimating airship attitude, linear velocities, angular velocities and model uncertainties. While Particle filter is designed for the estimation of airship attitude, linear velocities and angular velocities. Simulations have been performed using nonlinear 6-DOF simulation model of experimental airship for assessing the estimator performances. 1− uncertainty bound and error analysis have been performed for the validation. A comparative study of the estimator's performances is also carried out

Classifying intelligence in machines : a taxonomy of intelligent control

The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications. Looking at the spread of classifications based on this taxonomy can help researchers identify where control systems can be made more intelligent

RBF-based supervisor path following control for ASV with time-varying ocean disturbance

1028-1036A robust model-free path following controller is developed for autonomous surface vehicle (ASV) with time-varying ocean disturbance. First, the geometrical relationship between ASV and virtual tracking point on the reference path is investigated. The differentiations of tracking errors are described with the relative motion method, which greatly simplified the direct differential of tracking errors. Furthermore, the control law for the desired angular velocity of the vehicle and virtual tracking point are built based on the Lyapunov theory. Second, the traditional proportional-integral-derivative (PID) controller is developed based on the desired velocities and state feedback. The radial basic function (RBF) neural network taking as inputs the desired surge velocity and yaw angular velocity is developed as the supervisor to PID controller. Besides, RBF controller tunes weights according to the output errors between the PID controller and supervisor controller, based on the gradient descent method. Hence, PID controller and RBF supervisor controller act as feedback and feed forward control of the system, respectively. Finally, comparative path following simulation for straight path and sine path illustrate the performance of the proposed supervisor control system. The PID controller term reports loss of control even in the unknown disturbance

Multi-Layered Optimal Navigation System For Quadrotors UAV

Purpose This paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV). Design/methodology/approach The proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS). Findings All the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system. Practical implications The proposed controllers are easily implementable on-board and are computationally efficient. Originality/value The originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly

Adaptive backstepping nonsingular terminal sliding-mode attitude control of flexible airships with actuator faults

This paper studies the attitude tracking control of a flexible airship subjected to wind disturbances, actuator saturation and control surface faults. Efficient flexible airship models, including elastic deformation, rigid body motions, and their coupling, are established via Lagrange theory. A fast-nonsingular terminal sliding-mode (NTSM) combined with a backstepping control is proposed for the problem. The benefits of this approach are NTSM merits of high robustness, fast transient response, and finite time convergence, as well as the backstepping control in terms of globally asymptotic stability. However, the major limitation of the backstepping NTSM is that its design procedure is dependent on the prior knowledge of the bound values of the disturbance and faults. To overcome this limitation, a wind observer is designed to compensate for the effect of the wind disturbances, an anti-windup compensator is designed to compensate for actuator saturation, and an adaptive fault estimator is designed to estimate the faults of the control surfaces. Globally exponential stability of the closed-loop control system is guaranteed by using the Lyapunov stability theory. Finally, simulation results demonstrate effectiveness and advantages of the proposed control for the Skyship-500 flexible airship, even in the presence of unknown wind disturbances, control surface faults, and different stiffness variants

Predictive Sliding Mode Control for Attitude Tracking of Hypersonic Vehicles Using Fuzzy Disturbance Observer

We propose a predictive sliding mode control (PSMC) scheme for attitude control of hypersonic vehicle (HV) with system uncertainties and external disturbances based on an improved fuzzy disturbance observer (IFDO). First, for a class of uncertain affine nonlinear systems with system uncertainties and external disturbances, we propose a predictive sliding mode control based on fuzzy disturbance observer (FDO-PSMC), which is used to estimate the composite disturbances containing system uncertainties and external disturbances. Afterward, to enhance the composite disturbances rejection performance, an improved FDO-PSMC (IFDO-PSMC) is proposed by incorporating a hyperbolic tangent function with FDO to compensate for the approximate error of FDO. Finally, considering the actuator dynamics, the proposed IFDO-PSMC is applied to attitude control system design for HV to track the guidance commands with high precision and strong robustness. Simulation results demonstrate the effectiveness and robustness of the proposed attitude control scheme