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The purpose of this paper is to improve the tracking performance of the tracking loops under high dynamic and severe jamming conditions.

First, as the two dominant measurement error sources of the tracking loops, the thermal noise jitter and the dynamic stress error are thoroughly analyzed. Second, a scheme of adaptive tracking loops, which could adaptively adjust the order and the bandwidth of tracking loops, is proposed. Third, real-time detections of the vehicle dynamics and the carrier-to-noise density ratio, and the adaptive bandwidth of the carrier loop are presented, respectively. Finally, simulations are operated to validate the excellent tracking performance of the adaptive tracking loops.

Based on the principle of minimizing the measurement errors, the loop order and bandwidth are adaptively adjusted in the proposed scheme. Thus, the anti-jamming capability and dynamic tracking performance of the tracking loops could be effectively enhanced.

This paper provides further study on the method of improving the tracking capability under complexly applied conditions of high dynamics and severe jamming.

The detections of carrier-to-noise density ratio and vehicle dynamics are used to adaptively adjusting the loop order and bandwidth, which could not only improve the measurement accuracy but also ensure the stable operation of tracking loops.

The purpose of this paper is to analyze and compare the performance of several different UAV trajectory tracking algorithms in normal and abnormal flight conditions to investigate the fault‐tolerant capabilities of a novel immunity‐based adaptive mechanism.

The evaluation of these algorithms is performed using the West Virginia University (WVU) UAV simulation environment. Three types of fixed‐parameter algorithms are considered as well as their adaptive versions obtained by adding an immunity‐based mechanism. The types of control laws investigated are: position proportional, integral, and derivative control, outer‐loop nonlinear dynamic inversion (NLDI), and extended NLDI. Actuator failures on the three channels and increased turbulence conditions are considered for several different flight paths. Specific and global performance metrics are defined based on trajectory tracking errors and control surface activity.

The performance of all of the adaptive controllers proves to be better than their fixed parameter counterparts during the presence of a failure in all cases considered.

The immunity inspired adaptation mechanism has promising potential to enhance the fault‐tolerant capabilities of autonomous flight control algorithms and the extension of its use at all levels within the control laws considered and in conjunction with other control architectures is worth investigating.

The WVU UAV simulation environment has been proved to be a valuable tool for autonomous flight algorithm development, testing, and evaluation in normal and abnormal flight conditions.

A novel adaptation mechanism is investigated for UAV control algorithms with fault‐tolerant capabilities. The issue of fault tolerance of UAV control laws has only been addressed in a limited manner in the literature, although it becomes critical in the context of imminent integration of UAVs within the commercial airspace.

The purpose of this paper is to enhance the quadrotor’s capability of short-distance delivery to satisfy the large demand for quadrotor, which is used for goods distribution in huge warehouses, under time-varying payload and external wind disturbance.

A trajectory tracking controller design based on the combination of an adaptive sliding mode control (ASMC) method and the active disturbance rejection control (ADRC) technique is proposed. Besides, an inner–outer loop control system structure is adopted.

Simulation results of different trajectory tracking verify the effectiveness and robustness of the proposed tracking control method under various conditions, including parameter uncertainty and external wind disturbance. The proposed control strategy ensures that quadrotor UAV is capable of tracking linear and spiral trajectory well whether it loads or unloads goods in the presence of the external wind disturbance.

The proposed method of designing a trajectory tracking controller is based on an integral ADRC and ASMC scheme so as to deal with the trajectory tracking problem for a quadrotor with payload variation.

The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

A chaotic system is a nonlinear deterministic system that displays complex, noisy‐like and unpredictable behavior. The interest in chaotic systems lies mostly upon their complex, unpredictable behavior, and extreme sensitivity to initial conditions as well as parameter variations. Based on wavelet neural network's (WNN) online approximation ability, the purpose of this paper is to propose an adaptive Gaussian wavelet neural control (AGWNC) system to control a chaotic system.

The proposed AGWNC system is composed of a wavelet neural controller and a compensation tangent controller. The wavelet neural controller utilizes a Gaussian WNN to mimic an ideal controller, and the compensation tangent controller is designed to compensate the approximation error between the ideal and the wavelet neural controllers. The controller parameters of the proposed AGWNC can online tune in the Lyapunov sense, thus the uniformly ultimately bounded stability of closed‐loop system can be guaranteed.

The proposed AGWNC system is applied to a chaotic system. Simulation results are used to demonstrate the effectiveness and performance of the proposed AGWNC scheme. Simulation results show that not only the favorable control performance can be achieved but also the control efforts without any chattering phenomena. Moreover, all controller parameters can be online tuning by the derived adaptive laws based on the Lyapunov function.

The proposed AGWNC approach is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the overall closed‐loop control system is globally stable in uniform ultimate boundedness; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the AGWNC system can achieve favorable tracking performance.

The purpose of this paper is to develop a new inverse controller for servo‐system position tracking control based on neural network (NN) and model reference adaptive control (MRAC).

First, the model of general servo‐systems is analyzed. Then, a MRAC based on neural network control (NNC) is proposed with mathematical prove of stability. In addition, several simulation cases and experiments are listed to verify the usability of the control scheme.

This scheme consists of an MRAC, an online NN controller and a robust controller in velocity‐loop. For reducing influence which arose from modeling error, unknown model dynamics, parameter variation, and load changes, the NN controller is introduced to counteract the various influence mentioned above dynamically. MRAC, NNC, and robust controller adjust system to track the approximate velocity‐loop reference model. In this way, the position‐loop is not sensitive to the disturbance on velocity‐loop, and the whole velocity‐loop can be treated as a simple linear model when designing the other parts of the system. In addition, a novel inverse control method based on linear velocity signal filter is introduced to this scheme. In this case, the MRAC, NNC, and robust controller perform as an adaptive inverse controller, which keeps the velocity signal tracking the position loop controller output.

The paper presents a new inverse controller with NNC and MRAC which is practical and flexible.

The aim of this paper is the implementation and validation of control and guidance algorithms for unmanned aerial vehicle (UAV) autopilots.

The path-following control of the UAV can be separated into different layers: inner loop for pitch and roll attitude control, outer loop on heading, altitude and airspeed control for the waypoints tracking and waypoint navigation. Two control laws are defined: one based on proportional integrative derivative (PID) controllers both for inner and outer loops and one based on the combination of PIDs and an adaptive controller.

Good results can be obtained in terms of trajectory tracking (based on waypoints) and of parameter variations. The adaptive control law guarantees smoothing responses and less oscillations and glitches on the control deflections.

The proposed controllers are easily implementable on-board and are computationally efficient.

The algorithm validation via hardware in the loop simulations can be used to reduce the platform set-up time and the risk of losing the prototype during the flight tests.

The purpose of this paper is to present an adaptive neuro‐sliding mode control scheme for uncertain nonlinear systems with Lyapunov approach.

The paper focuses on neural network (NN) adaptive control for nonlinear systems in the presence of parametric uncertainties. The plant model structure is represented by a NNs system. The essential idea of the online parametric estimation of the plant model is based on a comparison of the measured state with the estimated one. The proposed adaptive neural controller takes advantages of both the sliding mode control and proportional integral (PI) control. The chattering phenomenon is attenuated and robust performances are ensured. Based on Lyapunov stability theorem, the proposed adaptive neural control system can guarantee the stability of the whole closed‐loop system and obtain good‐tracking performances. Adaptive laws are proposed to adjust the free parameters of the neural models.

Simulation results show that the adaptive neuro‐sliding mode control approach works satisfactorily for nonlinear systems in the presence of parametric uncertainties.

The proposed adaptive neuro‐sliding mode control approach is a mixture of classical neural controller with a supervisory controller. The PI controller is used to attenuate the chattering phenomena. Based on the Lyapunov stability theorem, it is rigorously proved that the stability of the whole closed‐loop system is ensured and the tracking performance is achieved.

The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA).

The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible control laws design environment is developed, which can be used to easily optimize the gains for a variety of unmanned aerial vehicle (UAV) control laws architectures.

The performance of the aircraft trajectory-tracking controllers was shown to improve significantly through the GA optimization. Additionally, the novel normalization modification was shown to encourage more rapid convergence to an optimal solution.

The GA paradigm shows much promise in the optimization of highly non-linear aircraft trajectory-tracking controllers. The proposed optimization tool facilitates the investigation of novel control architectures regardless of complexity and dimensionality.

The addition of the evolutionary optimization to the WVU UAV simulation environment enhances significantly its capabilities for autonomous flight algorithm development, testing, and evaluation. The normalization methodology proposed in this paper has been shown to appreciably speed up the convergence of GAs.

The paper provides a flexible generalized framework for UAV control system evolutionary optimization. It includes specific novel structural elements and mechanisms for improved convergence as well as a comprehensive PI for trajectory tracking.

This paper aims to propose an innovative adaptive control method for lower-limb rehabilitation robots.

Despite carrying out various studies on the subject of rehabilitation robots, the flexibility and stability of the closed-loop control system is still a challenging problem. In the proposed method, surface electromyography (sEMG) and human force-based dual closed-loop control strategy is designed to adaptively control the rehabilitation robots. A motion analysis of human lower limbs is performed by using a wavelet neural network (WNN) to obtain the desired trajectory of patients. In the outer loop, the reference trajectory of the robot is modified by a variable impedance controller (VIC) on the basis of the sEMG and human force. Thenceforward, in the inner loop, a model reference adaptive controller with parameter updating laws based on the Lyapunov stability theory forces the rehabilitation robot to track the reference trajectory.

The experiment results confirm that the trajectory tracking error is efficiently decreased by the VIC and adaptively correct the reference trajectory synchronizing with the patients’ motion intention; the model reference controller is able to outstandingly force the rehabilitation robot to track the reference trajectory. The method proposed in this paper can better the functioning of the rehabilitation robot system and is expandable to other applications of the rehabilitation field.

The proposed approach is interesting for the design of an intelligent control of rehabilitation robots. The main contributions of this paper are: using a WNN to obtain the desired trajectory of patients based on sEMG signal, modifying the reference trajectory by the VIC and using model reference control to force rehabilitation robot to track the reference trajectory.