Nonlinear Unmanned Aerial Vehicle Research Articles

Optimal control of a small fixed-wing UAV about concatenated trajectories

The paper focuses on the control of a small fixed-wing unmanned aerial vehicle (UAV) along trajectories. The trajectories are generated in real-time from a library of pre-specified motion primitives. Two concatenated primitive trajectories are considered: one formed from seven primitives and exhibiting a figure-8 geometric path and another composed of a Split-S maneuver that settles into a level-turn trim trajectory. Switched control systems stemming from ℓ2-induced norm synthesis approaches are designed for discrete-time linearized models of the nonlinear UAV system. These controllers are analyzed based on simulations in a realistic operational environment and are further implemented on the physical UAV.

AI-based adaptive control and design of autopilot system for nonlinear UAV

The objective of this paper is to design an autopilot system for unmanned aerial vehicle (UAV) to control the speed and altitude using electronic throttle control system (ETCS) and elevator, respectively. A DC servo motor is used for designing of ETCS to control the throttle position for appropriate amount of air mass flow. Artificial Intelligence (AI)-based controllers such as fuzzy logic PD, fuzzy logic PD + I, self-tuning fuzzy logic PID (STF-PID) controller and fuzzy logic-based sliding mode adaptive controller (FLSMAC) are designed for stable autopilot system and are compared with conventional PI controller. The target of throttle, speed and altitude controls are to achieve a wide range of air speed, improved energy efficiency and fuel economy with reduced pollutant emission. The energy efficiency using specific energy rate per velocity of UAV is also presented in this paper.

Robust fault-tolerant control against time-varying actuator faults and saturation

A set invariance condition for a piecewise continuous time-varying system is developed and applied to the fault-tolerant control problem against the actuator effectiveness loss for linear systems subject to system uncertainties and actuator saturation in this study. Linear matrix inequality conditions are proposed to compute invariant ellipsoidal sets. Discussions on system performance optimisation are given including reduction on computational complexity, maximisation of the domain of attraction and disturbance rejection. The proposed design techniques are applied to an unstable multi-input and multi-output system and to the flight control of a non-linear unmanned aerial vehicle model subject to the control effectiveness loss fault in the elevator.

Design of Non-Linear Dynamic Inversion UAV with QFT

Flight control system of UAV (Unmanned Aerial Vehicle) has limitations with non-linear dynamic inversion method. This paper introduces QFT based on non-linear dynamic inversion method, for avoiding model errors during existing non-linear function offset, QFT theory is presented firstly, then UAV model is established using non-linear dynamic inversion method, definituding the range of errors and system performance requirements, flight control system of UAV is designed with QFT, the simulating curves show that system has robustness stability.

Robust PID Control of the Quadrotor Helicopter

In this paper a robust PID control strategy via affine parametrization is designed for an multivariable nonlinear unmanned aerial vehicle. The robustness of the controlled system is assured by using the H∞ norm of the weighted complementary sensitivity function. Simulation results carried out using a complete nonlinear model are shown, wherein the performance achieved with this control strategy is shown.

Fuzzy-Genetic Autopilot Design for Nonminimum Phase and Nonlinear Unmanned Aerial Vehicles

In this paper, the authors tried to design the altitude hold mode autopilot for unmanned aerial vehicles. This case presents an interesting challenge attributable to the nonminimum phase characteristic, nonlinearities, and uncertainties of the altitude to elevator relationship. A fuzzy logic autopilot in a single-loop scheme is proposed for the design of this autopilot. The multiobjective genetic algorithm is used to mechanize the optimal determination of fuzzy logic autopilot parameters on the basis of an efficient cost function that comprises undershoot, overshoot, rise time, settling time, steady state error, and stability. Simulation results show that the proposed strategy not only has a simple structure, but also has desirable performances in time response characteristics, robustness (against the unmodeled nonlinear terms and parametric uncertainties), and the adaptation of itself than the large commands.

Globally solving a nonlinear UAV task assignment problem by stochastic and deterministic optimization approaches

In this paper, we consider a task allocation model that consists of assigning a set of m unmanned aerial vehicles (UAVs) to a set of n tasks in an optimal way. The optimality is quantified by target scores. The mission is to maximize the target score while satisfying capacity constraints of both the UAVs and the tasks. This problem is known to be NP-hard. Existing algorithms are not suitable for the large scale setting. Scalability and robustness are recognized as two main issues. We deal with these issues by two optimization approaches. The first approach is the Cross-Entropy (CE) method, a generic and practical tool of stochastic optimization for solving NP-hard problem. The second one is Branch and Bound algorithm, an efficient classical tool of global deterministic optimization. The numerical results show the efficiency of our approaches, in particular the CE method for very large scale setting.

Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering

The aim of this paper is to present a new INS/GPS sensor fusion scheme, based on state-dependent Riccati equation (SDRE) nonlinear filtering, for unmanned aerial vehicle (UAV) localization problem. SDRE navigation filter is proposed as an alternative to extended Kalman filter (EKF), which has been largely used in the literature. Based on optimal control theory, SDRE filter solves issues linked with EKF filter such as linearization errors, which severely decrease UAV localization performances. Stability proof of SDRE nonlinear filter is also presented and validated on a 3-D UAV flight scenario. Results obtained by SDRE navigation filter were compared to EKF navigation filter results. This comparison shows better UAV localization performance using SDRE filter. The suitability of the SDRE navigation filter over an unscented Kalman navigation filter for highly nonlinear UAV flights is also demonstrated.

Path Planning and State Estimation for Unmanned Aerial Vehicles in Hostile Environments

Path Planning and State Estimation for Unmanned Aerial Vehicles in Hostile Environments