Application of Higher Order Derivatives to Helicopter Model Control

Autonomous Landing of UAVs under Unknown Disturbances using NDI Autopilot with L1 Adaptive Augmentation

Adaptive neural network based quadrotor UAV formation control under external disturbances

Adaptive constrained backstepping controller with prescribed performance methodology for carrier-based UAV

The problem of output regulation for nonlinear time-varying control systems under uncertainties is one of particular interest for real-time control system design. There is a broad set of practical problems in the control of aircraft, robotics, mechatronics, chemical industry, electrical and electro-mechanical systems where control systems are designed to provide the following objectives: (i) robust zero steady-state error of the reference input realization; (ii) desired output performance specifications such as overshoot, settling time, and system type of referencemodel for desired output behavior; (iii) insensitivity of the output transient behavior with respect to unknown external disturbances and varying parameters of the system. In spite of considerable advances in the recent control theory, it is common knowledge that PI and PID controllers are most widely and successfully used in industrial applications (Morari & Zafiriou, 1999). A great attention of numerous researchers during the last few decades was devoted to turning rules (Astrom & Hagglund, 1995; O’Dwyer, 2003; Ziegel & Nichols, 1942), identification and adaptation schemes (Li et al., 2006) in order to fetch out the best PI and PID controllers in accordance with the assigned design objectives. The most recent results have concern with the problem of PI and PID controller design for linear systems. However, various design technics of integral controllers for nonlinear systems were discussed as well (Huang & Rugh, 1990; Isidori & Byrnes, 1990; Khalil, 2000; Mahmoud & Khalil, 1996). The main disadvantage of existence design procedures of PI or PID controllers is that the desired transient performances in the closed-loop system can not be guaranteed in the presence of nonlinear plant parameter variations and unknown external disturbances. The lack of clarity with regard to selection of sampling period and parameters of discrete-time counterparts for PI or PID controllers is the other disadvantage of the current state of this question. The output regulation problem under uncertainties can be successfully solved via such advanced technics as control systems with sliding motions (Utkin, 1992; Young & Ozguner, 1999), control systems with high gain in feedback (Meerov, 1965; Young et al., 1977). A set of examples can be found from mechanical applications and robotics where acceleration feedback control is successfully used (Krutko, 1988; 1991; 1995; Lun et al., 1980; Luo et al., 1985; Studenny & Belanger, 1984; 1986). The generalized approach to nonlinear control system design based on control law with output derivatives and high gain in feedback, where integral action can be incorporated in the controller, is developed as well and one is used 7

This paper investigates the problem of the degradation of trajectory tracking performance for an unmanned aerial vehicle under unknown external disturbances such as wind. We propose an adaptive guidance algorithm consisting of two parts: a baseline path-following guidance law with optimal error dynamics and a nonlinear autoregressive with Gaussian process regression (GP-NAR). Firstly, a baseline path-following guidance law is constructed by adopting the guidance kinematics and the optimal error dynamics, assuming that the disturbances are measurable during this step. Then, the GP-NAR that the inputs are past observations of external disturbances is employed to capture and compensate for unknown external time-varying disturbances. Additionally, the partial autocorrelation function (PACF) is utilized to identify the lag value that determines the number of inputs of the GP-NAR model. Numerical simulation results demonstrate the performance of the proposed adaptive guidance algorithm under the unknown time-varying disturbances.KeywordsPath-following guidanceNonlinear autoregressive with Gaussian process regressionTime-varying disturbances

Robust adaptive nonsingular fast terminal sliding-mode tracking control for an uncertain quadrotor UAV subjected to disturbances

For the quadrotor unmanned aerial vehicle (UAV) system in the presence of unknown drag coefficients and unknown external disturbances, a new adaptive sliding mode finite-time tracking control algorithm is presented. The whole control system can be divided into two subsystems, that is, position subsystem and attitude subsystem. And, by the attitude extraction algorithms, the two subsystems are connected each other. The adaptive sliding mode finite-time control methods are designed for both position subsystem and attitude subsystem. In order to estimate the external disturbances of position subsystem, finite-time disturbance observers are designed. At the same time, a new adaptive method is used to deal with the unknown drag coefficients. In addition, the limitation of knowing the boundaries of the external disturbances of attitude subsystem is cancelled by using adaptive technology. The position and attitude tracking errors of quadrotor UAV are proved to be asymptotic stability in finite time by using Lyapunov theory. Finally, simulation results are given to verify the effectiveness of the proposed finite-time control method.

With the increasing demand for unmanned aerial vehicles (UAVs) in both military and civilian applications, critical safety issues need to be specially considered in order to make better and wider use of them. UAVs are usually employed to work in hazardous and complex environments, which may seriously threaten the safety and reliability of UAVs. Therefore, the safety and reliability of UAVs are becoming imperative for development of advanced intelligent control systems. The key challenge now is the lack of fully autonomous and reliable control techniques in face of different operation conditions and sophisticated environments. Further development of unmanned aerial vehicle (UAV) control systems is required to be reliable in the presence of system component faults and to be insensitive to model uncertainties and external environmental disturbances. This thesis research aims to design and develop novel control schemes for UAVs with consideration of all the factors that may threaten their safety and reliability. A novel adaptive sliding mode control (SMC) strategy is proposed to accommodate model uncertainties and actuator faults for an unmanned quadrotor helicopter. Compared with the existing adaptive SMC strategies in the literature, the proposed adaptive scheme can tolerate larger actuator faults without stimulating control chattering due to the use of adaptation parameters in both continuous and discontinuous control parts. Furthermore, a fuzzy logic-based boundary layer and a nonlinear disturbance observer are synthesized to further improve the capability of the designed control scheme for tolerating model uncertainties, actuator faults, and unknown external disturbances while preventing overestimation of the adaptive control parameters and suppressing the control chattering effect. Then, a cost-effective fault estimation scheme with a parallel bank of recurrent neural networks (RNNs) is proposed to accurately estimate actuator fault magnitude and an active fault-tolerant control (FTC) framework is established for a closed-loop quadrotor helicopter system. Finally, a reconfigurable control allocation approach is combined with adaptive SMC to achieve the capability of tolerating complete actuator failures with application to a modified octorotor helicopter. The significance of this proposed control scheme is that the stability of the closed-loop system is theoretically guaranteed in the presence of both single and simultaneous actuator faults.

Fast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing one or more rotors. We present a motor fault detection and isolation (FDI) method for multi-rotor UAVs based on an external wrench estimator and a recurrent neural network composed of long short-term memory nodes. The proposed approach considers the partial or total motor fault as an external disturbance acting on the UAV. Hence, the devised external wrench estimator trains the network to promptly understand whether the estimated wrench comes from a motor fault (also identifying the motor) or from unmodelled dynamics or external effects (i.e., wind, contacts, etc.). Training and testing have been performed in a simulation environment endowed with a physic engine, considering different UAV models operating under unknown external disturbances and unexpected motor faults. To further assess this approach’s effectiveness, we compare our method’s performance with a classical model-based technique. The collected results demonstrate the effectiveness of the proposed FDI approach.

This paper investigates an adaptive fault‐tolerant control scheme for an unmanned aerial vehicle (UAV) attitude control system in the presence of multiple faults, input saturation, and external disturbances. The attitude control system of a UAV is divided into an outer loop consisting of attitude angles and an inner loop consisting of attitude angle velocities. The nonlinear dynamic inversion (NDI) method based on sliding mode surface is used to construct the outer loop controller to ensure fast response and accurate attitude tracking. The treatment of the inner loop with multiple faults, input saturation, and external disturbances is more complex. First, input saturation and sensor faults are converted successively for ease of analysis. After the conversion and reorganization of such uncertainties including actuator faults, sensor faults, input saturation, and external disturbances, a nonlinear uncertain system model is developed. Second, a L1 neural network adaptive fault‐tolerant controller is designed to deal with uncertainties, where radial basis function neural networks (RBFNNs) are applied to approximate the nonlinear function in the system model. The L1 adaptive fault‐tolerant controller includes three parts: state predictor, adaptive law, and control law. Adaptive laws based on the projection operator estimate and update the unknown parameters in the system and transfer them to the control law and the state predictor to compensate for the effects of uncertainties. The control law with a low‐pass filter also eliminates the high‐frequency oscillations caused by a high adaptive gain in conventional adaptive algorithms, which guarantee the robustness and fast self‐adaptation. Finally, simulation results are given to verify the effectiveness and feasibility of the proposed controller.

Time-varying formation tracking problem for unmanned aerial vehicle (UAV) swarm systems subject to unknown nonlinear dynamics, external disturbances, and switching topologies is studied. The states of the following UAV s are required to form a predefined time-varying formation while tracking the state of the leading UAV. For each following UAV, the unknown nonlinear dynamics and external disturbance are regarded as an extended state of the UAV, and then an extended state observer (ESO) is correspondingly designed. Based on the output of the ESO, a novel time-varying formation tracking protocol is proposed. Rigorous theoretical analysis is given to show that, with the application of the proposed protocol, the ESO estimation error and the time-varying formation tracking error can be made arbitrarily small. A numerical simulation demonstrates the effectiveness of the proposed approach.

In this paper, an adaptive neural fault-tolerant control scheme is proposed and analyzed for a class of uncertain nonlinear large-scale systems with unknown dead zone and external disturbances. To tackle the unknown nonlinear interaction functions in the large-scale system, the radial basis function neural network (RBFNN) is employed to approximate them. To further handle the unknown approximation errors and the effects of the unknown dead zone and external disturbances, integrated as the compounded disturbances, the corresponding disturbance observers are developed for their estimations. Based on the outputs of the RBFNN and the disturbance observer, the adaptive neural fault-tolerant control scheme is designed for uncertain nonlinear large-scale systems by using a decentralized backstepping technique. The closed-loop stability of the adaptive control system is rigorously proved via Lyapunov analysis and the satisfactory tracking performance is achieved under the integrated effects of unknown dead zone, actuator fault, and unknown external disturbances. Simulation results of a mass-spring-damper system are given to illustrate the effectiveness of the proposed adaptive neural fault-tolerant control scheme for uncertain nonlinear large-scale systems.

In this paper, the accurate dynamics of three-rotor unmanned aerial vehicle (UAV) are detailedly analyzed and a nonlinear robust tracking control strategy is proposed considering unknown time-varying external disturbances. Aiming at the tracking control of the typical underactuated system, the dynamic model of the three-rotor UAV is divided into outer-loop position subsystem and inner-loop attitude subsystem. The feedback linearization algorithm is employed to design the outer-loop controller for the trajectory tracking of the UAV. For the inner-loop control of the UAV, the robust integral of the signum of the error (RISE) method is utilized to formulate the robust attitude controller to deal with the external disturbances. The stability of the closed loop system and the asymptotical tracking of the desired trajectory are proved via Lyapunov based stability analysis. Real-time experiments are implemented to validate the performance of the proposed control strategy.

Robust output regulation of a class of uncertain nonlinear minimum phase systems perturbed by unknown external disturbances

This paper presents a novel neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller for consensus of nonlinear multi-agent systems in the presence of unknown external disturbance. N-DNDI is a blending of neural network and distributed nonlinear dynamic inversion (DNDI), a new consensus control technique that inherits the features of Nonlinear Dynamic Inversion (NDI) and is capable of handling the unknown external disturbance. The implementation of NDI based consensus control along with neural networks is unique in the context of multi-agent consensus. The mathematical details provided in this paper show the solid theoretical base, and simulation results prove the effectiveness of the proposed scheme.