Trajectory Control of a Quadrotor Subject to 2D Wind Disturbances

Robust adaptive three-dimensional trajectory tracking control scheme design for small fixed-wing UAVs

This paper aims to tackle the three-dimensional trajectory tracking problems of small fixed-wing unmanned aerial vehicles subject to nonlinearities, uncertainties and wind disturbances via adaptive techniques. The control objective is to efficiently control the thrust and the deflections of control surfaces to ensure the unmanned aerial vehicle arrives at a specified location within a given time frame. However, achieving this goal for small fixed-wing unmanned aerial vehicles can be challenging because the precise dynamic model and several parameters are not accessible, making most existing control strategies unworkable. Motivated by these facts, based on feedback linearization techniques, we derive linear models with equivalent disturbances to describe the translational dynamics without requiring precise aerodynamic force model information. To deal with the dilemmas where the norm bounds of equivalent disturbances depend on control inputs, system states, and unknown disturbances, a novel robust adaptive control strategy is designed for position control. Based on the assumption of two-time separation, the control scheme incorporates two parts, namely, a position controller containing the horizontal-plane and altitude parts and a robust filter-based attitude regulator. Also, to prevent chattering issues, we design a practical and robust adaptive position controller under which the tracking error is ultimately bounded The overall closed-loop stability is theoretically investigated based on the Lyapunov arguments. Hardware-in-loop simulation experiments are performed to testify our developed control scheme.

Adaptive Full-State-Constrained Control of Nonlinear Systems With Deferred Constraints Based on Nonbarrier Lyapunov Function Method

In this article, the problem of tracking control is considered for a class of uncertain strict-feedback nonlinear systems with deferred asymmetric time-varying full-state constraints. A novel adaptive robust full-state-constrained control scheme is developed. First, by introducing a novel shifting function, the original constrained system with any initial values is modified to a new constrained system, and the initial values of the modified constrained system remain 0. Then, to remove the feasibility condition caused by the barrier Lyapunov functions, the modified constrained system is further transformed into a new unconstrained system by a brand new nonlinear transformation. Furthermore, the tracking error system of the unconstrained system is constructed by using a new coordinate transformation, and a novel adaptive full-state-constrained control scheme is designed based on this error system through the backstepping recursion method and first-order filters. Finally, the resulting closed-loop system proves to be stable and numerical simulations are conducted to demonstrate the effectiveness of the developed control strategy.

Sliding Mode Fault Tolerant Control for a Quadrotor with Varying Load and Actuator Fault

In this paper, an adaptive sliding mode fault-tolerant control scheme based on prescribed performance control and neural networks is developed for an Unmanned Aerial Vehicle (UAV) quadrotor carrying a load to deal with actuator faults. First, a nonsingular fast terminal sliding mode (NFTSM) control strategy is presented. In virtue of the proposed strategy, fast convergence and high robustness can be guaranteed without stimulating chattering. Secondly, to obtain correct fault magnitudes and compensate the failures actively, a radial basis function neural network-based fault estimation scheme is proposed. By combining the proposed fault estimation strategy and the NFTSM controller, an active fault-tolerant control algorithm is established. Then, the uncertainties caused by load variation are explicitly considered and compensated by the presented adaptive laws. Moreover, by synthesizing the proposed sliding mode control and prescribed performance control (PPC), an output error transformation is defined to deal with state constraints and provide better tracking performance. From the Lyapunov stability analysis, the overall system is proven to be uniformly asymptotically stable. Finally, numerical simulation based on a quadrotor helicopter is carried out to validate the effectiveness and superiority of the proposed algorithm.

Distributed adaptive containment control of uncertain QUAV multiagents with time-varying payloads and multiple variable constraints

This paper considers the containment control problem for uncertain QUAV (Quadrotor Unmanned Aerial Vehicle) multiagents with time-varying payloads under a fixed topology graph, and a distributed adaptive containment control protocol with multiple variable constraints is proposed. Generally, the control framework is classified into two layers. In the first layer, the desired trajectories are determined for followers by the communication topology and initial values of leaders. For the second layer, the ith QUAV follower is required to track the desired trajectory by employing the information of itself and neighbors. Under the second layer, the system of the ith agent is decoupled into two subsystems: the translational subsystem and the rotational subsystem. For the translational subsystem, the distributed adaptive containment controller is designed via dynamic surface control method to track the desired position trajectory. With such method, the information requirement of ith agent for its neighbors can be reduced significantly. For the rotational subsystem, the adaptive tracking controller is constructed to track the desired attitudes derived from translational subsystem through commonly used attitudes extraction algorithms. In the end, the resulting closed-loop system is proved to be stable in the sense of uniformly ultimate boundness, and the effectiveness of the proposed approach is illustrated by numerical simulations.

Feedback synthesis for underactuated systems using sequential second-order needle variations

This paper derives nonlinear feedback control synthesis for general control affine systems using second-order actions, the second-order needle variations of optimal control, as the basis for choosing each control response to the current state. A second result of this paper is that the method provably exploits the nonlinear controllability of a system by virtue of an explicit dependence of the second-order needle variation on the Lie bracket between vector fields. As a result, each control decision necessarily decreases the objective when the system is nonlinearly controllable using first-order Lie brackets. Simulation results using a differential drive cart, an underactuated kinematic vehicle in three dimensions, and an underactuated dynamic model of an underwater vehicle demonstrate that the method finds control solutions when the first-order analysis is singular. Finally, the underactuated dynamic underwater vehicle model demonstrates convergence even in the presence of a velocity field.