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The purpose of this paper is to find a solution for the unmanned aerial vehicle (UAV) rendezvous problem, which should be feasible, optimal and not time consuming. In the existing literatures, the UAV rendezvous problem is always presented as a matter of simultaneous arrival. They focus only on the time consistency. However, the arrival time of UAVs can vary according to the rendezvous position. The authors should determine the best rendezvous position with considering UAVs’ maneuver constraint, so that UAVs can construct a formation in a short time.

The authors present a decentralized method in which UAVs negotiate with each other for the best rendezvous positions by using Nash bargain. The authors analyzed the constraints of the rendezvous time and the UAV maneuver, and proposed an objective function that allows UAVs to get to their rendezvous positions as fast as possible. Bezier curve is adopted to generate smooth and feasible flight trajectories. During the rendezvous process, UAVs adjust their speed so that they can arrive at the rendezvous positions simultaneously.

The effectiveness of the proposed method is verified by simulation experiments. The proposed method can successfully and efficiently solve the UAV rendezvous problem.

As far as the authors know, it is the first time Nash bargain is used in the UAV rendezvous problem. The authors modified the Nash bargain method and make it distributed, so that it can be computed easily. The proposed method is much less consuming than ordinary Nash bargain method and ordinary swarm intelligence based methods. It also considers the UAV maneuver constraint, and can be applied online for its fast calculation speed. Simulations demonstrate the effectiveness of the proposed method.

The purpose of this paper is to probe the recursive identification of piecewise affine Hammerstein models directly by using input-output data. To explain the identification process of a parametric piecewise affine nonlinear function, the authors prove that the inverse function corresponding to the given piecewise affine nonlinear function is also an equivalent piecewise affine form. Based on this equivalent property, during the detailed identification process with respect to piecewise affine function and linear dynamical system, three recursive least squares methods are proposed to identify those unknown parameters under the probabilistic description or bounded property of noise.

First, the basic recursive least squares method is used to identify those unknown parameters under the probabilistic description of noise. Second, multi-innovation recursive least squares method is proposed to improve the efficiency lacked in basic recursive least squares method. Third, to relax the strict probabilistic description on noise, the authors provide a projection algorithm with a dead zone in the presence of bounded noise and analyze its two properties.

Based on complex mathematical derivation, the inverse function of a given piecewise affine nonlinear function is also an equivalent piecewise affine form. As the least squares method is suited under one condition that the considered noise may be a zero mean random signal, a projection algorithm with a dead zone in the presence of bounded noise can enhance the robustness in the parameter update equation.

To the best knowledge of the authors, this is the first attempt at identifying piecewise affine Hammerstein models, which combine a piecewise affine function and a linear dynamical system. In the presence of bounded noise, the modified recursive least squares methods are efficient in identifying two kinds of unknown parameters, so that the common set membership method can be replaced by the proposed methods.

This paper aims to present a control strategy that eliminates the longitudinal and lateral drifting movements of the coaxial ducted fan unmanned helicopter (UH) during autonomous take-off and landing and reduce the coupling characteristics between channels of the coaxial UH for its special model structure.

Unidirectional auxiliary surfaces (UAS) for terminal sliding mode controller (TSMC) are designed for the flight control system of the coaxial UH, and a hierarchical flight control strategy is proposed to improve the decoupling ability of the coaxial UH.

It is demonstrated that the proposed height control strategy can solve the longitudinal and lateral movements during autonomous take-off and landing phase. The proposed hierarchical controller can decouple vertical and heading coupling problem which exists in coaxial UH. Furthermore, the confronted UAS-TSMC method can guarantee finite-time convergence and meet the quick flight trim requirements during take-off and landing.

The designed flight control strategy has not implemented in real flight test yet, as all the tests are conducted in the numerical simulation and simulation with a hardware-in-the-loop (HIL) platform.

The designed flight control strategy can solve the common problem of coupling characteristics between channels for coaxial UH, and it has important theoretical basis and reference value for engineering application; the control strategy can meet the demands of engineering practice.

In consideration of the TSMC approach, which can increase the convergence speed of the system state effectively, and the high level of response speed requirements to UH flight trim, the UAS-TSMC method is first applied to the coaxial ducted fan UH flight control. The proposed control strategy is implemented on the UH flight control system, and the HIL simulation clearly demonstrates that a much better performance could be achieved.

To facilitate the nonlinear controller design, dynamic model of a novel coaxial unmanned helicopter (UH) is established and its coupling analysis is presented.

The chattering-free sliding mode controller (SMC) with unidirectional auxiliary surfaces (UASs) is designed and implemented for the coaxial ducted fan UH.

The coupling analysis based on the established model show severe coupling between channels. For coaxial UH’s special model structure, UAS-SMC controller is proposed to reduce the coupling characteristics between channels of the UH by setting controllers’ output calculation sequence.

The flight control law and control logic are successfully tested in numerical simulation and hardware in the loop (HIL) simulation. The results show best hovering performances without chattering problem, even under the bounded internal dynamics and external disturbances.

Attitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.

Among the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their performance balance; moreover, an evolution mechanism of the best frog is introduced to make the algorithm jump out the local optimum. The modified SFLA is applied to the tuning of the proportional coefficients of pitching and rolling channels of UAV flight control system.

Simulation of a UAV control system in which the nonlinear model is obtained by the wind tunnel experiment show the rapid dynamic response and high control precision by using the modified SFLA optimized attitude controller, which is better than that of the original SFLA and particle swarm optimization method.

A modification scheme is presented to improve the global searching capability of SFLA. The modified SFLA based intelligent determination method of the UAV flight controller parameters is proposed, in order to improve the attitude control performance of UAV.

The purpose of this paper considers optimal input signal design for flutter model parameters identification, as input signal is the first step during the whole identification process. According to the constructed flutter stochastic model with observed noises, separable least squares identification and set membership identification are proposed to identify those unknown model parameters for statistical noise and unknown but bounded noise, respectively. The common trace operation with respect to the asymptotic variance matrix is minimized to solve the power spectral for the optimal input signal in the framework of statistical noise. Moreover, for the unknown bout bounded noise, the radius of information, corresponding to the established parameter uncertainty interval, is minimized to give the optimal input signal.

First, model identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter statistical model with statistical noise, separable least squares identification is proposed to identify the unknown model parameters, then the optimal input signal is designed to satisfy one given performance function. Third, for aircraft flutter model with unknown but bounded noise, set membership identification is proposed to solve the parameter set for each unknown model parameter. Then, the optimal input signal is designed by applying the idea of the radius of information with unknown but bounded noise.

This aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Then identification strategy and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise and unknown but bounded noise, respectively.

To the best knowledge of the authors, this problem of the model parameter identification for aircraft flutter was proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes two novel identification strategies and opens a new subject about optimal input signal design for statistical noise and unknown noise, respectively.

The aim of this paper is to design a nonlinear disturbance observer-based control (DOBC) method obtained by patching a control method developed using a robust adaptive technique and a DO.

For designing a DOBC, initially a class of nonlinear system is considered with an external disturbance. First, a DO is designed to estimate the external disturbances. This estimate is combined with the controller to reject the disturbances and obtain the desired control objective. For designing a controller, the robust sliding mode control theory is used. Furthermore, instead of using a constant switching gain, an adaptive gain tuning criterion is designed using Lyapunov candidate function. To investigate the stability and effectiveness of the developed DOBC, stability analysis and simulation study are presented.

The major findings of this paper include the criteria of designing the robust adaptive control parameters and investigating the disturbance rejection when robust adaptive control based DOBC is developed.

In practice, the flight of quadrotor is affected by different kind of external disturbances, thus leading to the change in dynamics. Hence, it is necessary to design DOBCs based on robust adaptive controllers such that the quadrotor model adapts to the change in dynamics, as well as nullify the effect of disturbances.

Designing DOBCs based on robust control method is a common practice; however, the robust adaptive control method is rarely developed. This paper contributes in the domain of DOBC based on robust adaptive control methods such that the behavior of controller varies with the change in dynamics occurring due to external disturbances.

In this research paper, an adaptive output-feedback robust active disturbance rejection control (RADRC) is designed for the multiple input multiple output (MIMO) quadrotor attitude model subject to unwanted uncertainties and disturbances (UUDs).

In order to achieve the desired control objectives in the presence of UUDs, the low pass filter (LPF) and extended high gain observer (EHGO) methods are used for the estimation of matched and mismatched UUDs, respectively. Furthermore, for solving the chattering incurred in the standard sliding mode control (SMC), a multilayer sliding mode surface is constructed. For formulating the adaptive output-feedback RADRC algorithm, the EHGO, LPF and SMC schemes are combined using the separation principle.

The findings of this research work include the design of an adaptive output-feedback RADRC with the ability to negate the UUDs as well as estimate the unknown states of the quadrotor attitude model. In addition, the chattering problem is addressed by designing a modified SMC scheme based on the multilayer sliding mode surface obtained by utilizing the estimated state variables. This sliding mode surface is also used to obtain the adaptive criteria for the switching design gain parameters involved in the SMC. Moreover, the requirement of high design gain parameters in the EHGO is solved by combining it with the LPF.

Designing the flight control techniques while assuming that the state variables are available is a common practice. In addition, to obtain robustness, the SMC technique is widely used. However, in practice, the state variables might not be available due to unknown parameters and uncertainties, as well as the chattering due to SMC reduces the performances of the actuators. Hence, in this paper, an adaptive output-feedback RADRC technique is designed to solve the problems of UUDs and chattering.

The purpose of this study is to provide the full-state mathematical model and devise a nonlinear controller for a balloon-supported unmanned aerial vehicle (BUAV).

Newtonian mechanics is used to establish the nonlinear mathematical model of the proposed vehicle assembly which incorporates the dynamics of both balloon and quadrotor UAV. A controllable form of the nine degrees of freedom model is derived. Backstepping control is designed for the proposed model and simulations are performed to assess the tracking performance of the proposed control.

The results show that the proposed methodology works well for smooth trajectories in presence of wind gusts. Moreover, the final mathematical model is affine and various nonlinear control techniques can be used in the future for improved system performance.

Multi-rotor unmanned aerial vehicles (MUAVs) are equipped with controllers but are constrained by smaller flight endurance and payload carrying capability. On the contrary, lighter than air (LTA) aerial vehicles have longer flight times but have poor control performance for outdoor operations. One of the solutions to achieve better flight endurance and payload carrying capability is to augment the LTA balloon to MUAV. The novelty of this research lies in full-order mathematical modeling along with transformation to controllable form for the BUAV assembly.