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Trajectory tracking is an important issue to underactuated unmanned surface vehicles (USVs). However, parametric uncertainties and environmental disturbances bring great challenges to the precise trajectory tracking control of USVs. This paper aims to propose a robust trajectory tracking control algorithm with exponential stability for underactuated USVs with parametric uncertainties and unknown environmental disturbances.

In this method, the backstepping method and sliding mode control method are combined to ensure that the underactuated USV can track and maintain the desired trajectory. In addition, a modified switching-gain adaptation algorithm is adopted to enhance the robustness and reduce chattering. Besides, the global exponential stability of the closed-loop system is proved by Lyapunov’s direct method.

The proposed method in this paper offers a robust trajectory tracking solution to underactuated USVs and it is verified by simulations and experiments. Compared with the traditional proportion-integral-derivative method and several state-of-the-art algorithms, the proposed method has superior performance in simulation and experimental results.

This paper proposes a robust trajectory tracking control algorithm with exponential stability for underactuated USVs. The proposed method achieves exponential stability with better robustness and transient performance.

The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties.

The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem.

The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed.

The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation.

Automatic guided vehicles (AGVs) are widely used in industrial fields. But most control strategies merely take the lateral force into consideration. This will reduce the accuracy, stability and robustness and will pay additional costs. Therefore, this paper aims to design a control strategy that initially considers lateral force. Thereby, it will improve the accuracy, stability and robustness and reduce the overall cost of AGV.

To achieve the goal of comprehensively improving AGV operating performance, this paper presents a new scheme, combining the dual-wheeled chassis model (DCM) using proportional–integral–differential (PID) control and a supporting quick response (QR) code navigation technology. DCM is the core, which analyzes the deviation caused by lateral force. Then, DCM with PID control by the control law is combined to suppress the errors. Meanwhile, QR code navigation technology provides effective data support for the control strategy.

Most AGV experiments are carried out in a standard environment. However, this study prepares unfavorable scenarios and operating conditions for the experiments that generate detailed data to demonstrate this study’s strategy, which can make an accurate, stable and robust operation process of AGV under various adverse environmental and mechanical factors.

This study proposed DCM, fully considering lateral force and converting the force into velocity. Subsequently, PID controls the speed of two wheels to reduce the error. QR code provides an efficient and low – cost way to obtain information. The three are cleverly combined as a novel industrial AGV control strategy, which can comprehensively improve the operating performance while reducing overall costs.

This paper aims to deal with the trade-off of the stability and the accuracy in learning human control strategy from demonstrations. With the stability conditions and the estimated stability region, this paper aims to conveniently get rid of the unstable controller or controller with relatively small stability region. With this evaluation, the learning human strategy controller becomes much more robust to perturbations.

In this paper, the criterion to verify the stability and a method to estimate the domain of attraction are provided for the learning controllers trained with support vector machines (SVMs). Conditions are formulated based on the discrete-time system Lyapunov theory to ensure that a closed-form of the learning control system is strongly stable under perturbations (SSUP). Then a Chebychev point based approach is proposed to estimate its domain of attraction.

Some of such learning controllers have been implemented in the vertical balance control of a dynamically stable, statically unstable wheel mobile robot.

The purpose of this paper is to develop some interesting results in the field of chaotic synchronization with a new finite-time controller to reduce the time of convergence.

This article proposes a finite-time controller for the synchronization of hyper(chaotic) systems in a given time. The chaotic systems are perturbed by the model uncertainties and external disturbances. The designed controller achieves finite-time synchronization convergence to the steady-state error without oscillation and elimination of the nonlinear terms from the closed-loop system. The finite-time synchronization convergence reduces the hacking duration and recovers the embedded message in chaotic signals within a given preassigned limited time. The free oscillation convergence keeps the energy consumption low and alleviates failure chances of the actuator. The proposed finite-time controller is a combination of linear and nonlinear parts. The linear part keeps the stability of the closed-loop, the nonlinear part increases the rate of convergence to the origin. A generalized form of analytical stability proof is derived for the synchronization of chaotic and hyper-chaotic systems. The simulation results provide the validation of the accomplish synchronization for the Lu chaotic and hyper-chaotic systems.

The designed controller not only reduces the time of convergence without oscillation of the trajectories which can run the system for a given time domain.

This work is originally written by the author.

This paper aims to propose output feedback-based control algorithms for the flight control system of a scaled, un-crewed helicopter in its hover flight mode.

The proposed control schemes are based on H_∞ control and composite nonlinear control. The gains of the output feedback controllers are obtained as the solution of a set of linear matrix inequalities (LMIs).

In the proposed schemes, the finite-time convergence of system states to trim condition is achieved with minimum deviation from the steady-state. As the proposed composite nonlinear output feedback design improves the transient response, it is well suited for a scaled helicopter flight. The use of measured output vector instead of the state vector or its estimate for feedback provides a simple control structure and eliminates the need for an observer in real-time application. The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints.

The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints. They also have significance in applications where the number of measurement quantities needs to be minimized such as in a fully functional rotor-craft unmanned aerial vehicle.

The developed output feedback control algorithms can be used in small-scale helicopters for numerous civilian and military applications.

This work addresses the LMI-based formulation and solution of an output feedback controller for a hovering un-crewed helicopter. The stability and robustness of the closed-loop system are proved mathematically and the performance of the proposed schemes is compared with an existing strategy via simulation studies.

Considering the external disturbances and dynamic uncertainties during the process of the trajectory tracking, this paper aims to address the problem of the welding robot trajectory tracking with guaranteed accuracy.

The controller consists sliding mode control, fuzzy control and low pass filter. The controller adopts low-pass filter to reduce the high frequency chattering control signal in sliding mode control. The fuzzy control model is used to simulate the external disturbance signal and the dynamic uncertainty signal, so that the controller can effectively restrain the chattering caused by the sliding mode control algorithm, realizing the track of the welding robot effectively and improving the robustness of the robot.

An innovative experiment device was adopted to realize the performance of the proposed controller. Considering the kinematic and dynamic uncertainty during the process of robot tracking, the tracking accuracy was realized within 0.3 mm.

This paper uses Lyapunov stability theory and Barbalat theorem to analyze the stability of the proposed controller.

Sensorless online measurements, application of variable speed drives has been given a great attention, especially over the past few years. In most of the previous literates dealing with permanent magnet synchronous motor (PMSM) drives, the combination of inter-sampled behavior with high gain design approach has not been discussed yet. This paper aims to discuss this feature in-depth.

The study contains a different approach for an observer running with surface-mounted permanent magnet synchronous machine drives to implement sensorless control. Design of sampled data observer methodology for one kind of AC machine having non-linear model and backed by an elegant formal stability convergence analysis using the tools of Lyapunov stability techniques was highly recommended in scientific contributions, and it is yet needed to be solved.

In this study, a solution to observation problem is covered and developed by combining ideas from the high-gain design approach and inter-sample predictor based on stator voltage measurements. The output state currents are accessible only at the sampling instant to solve the problem of states observation at continuous-time mode. This allows to reducing the usage of online appliances, improving reliability of control design and saving costs.

The proposed observer is capable of guaranteeing an acceptable closed loop dynamic response over a wide range of operation region and industrial process for random initial conditions.

The output state predictor has been interred in constructing the innovation correct term to prove the robustness of the proposed observer against attenuated sampling interval. To validate the theoretical results introduced by the main fundamental theorem and prove the observer stability convergence, the proposed observer is demonstrated through a sample study application to variable speed permanent magnet synchronous machine drive.