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The purpose of this paper is to report on the global practical uniform h-stabilization for certain classes of nonlinear time-varying systems and its application in a separately excited DC motor circuit.

Based on Lyapunov theory, the practical h-stabilization result is derived to guarantee practical h-stability and applicated in a separately excited DC motor.

A controller is designed and added to the nonlinear time-varying system. The practical h-stability of the nonlinear control systems is guaranteed by applying the appropriate controller based on Lyapunov second method. Another effective controller is also designed for the global practical uniform h-stability on the separately excited DC motor with load. Numerical simulations are demonstrated to verify the effectiveness of the proposed controller scheme.

The introduced approach is interesting for practical h-stabilization of nonlinear time-varying systems and its application in a separately excited DC motor. The original results generalize well-known fundamental result: practical exponential stabilization for nonlinear time-varying systems.

The purpose of this paper is to present a two-loop approach for velocity control of a permanent magnet synchronous motor (PMSM) under mechanical uncertainties.

The inner loop calculates the two-axis stator reference voltages through a feedback linearization method. The outer loop employs an RST control structure to compute the q-axis stator reference current. To increase the robustness of the proposed method, the RST controller parameters are adapted through a fractional order model reference adaptive system (FO-MRAS). The fractional order gradient and Lyapunov methods are utilized as adaptation mechanisms.

The effect of the fractional order derivative in the load disturbance rejection, transient response speed and the robustness is verified through computer simulations. The simulation results show the effectiveness of the proposed method against the external torque and mechanical parameters uncertainties.

The proposed FO-MRAS based on Lyapunov adaptation mechanism is proposed for the first time. Moreover, application of the FO-MRAS for velocity control of PMSM is presented for the first time.

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.

This paper aims to propose a composite form of the robust disturbance fuzzy control scheme. The proposed method uses the benefits of fuzzy system modeling and Lyapunov direct methods.

The effciency of the control technique was demonstrated with assistance of numerical simulation in control problems of unmanned aerial vehicle (UAV) simulation.

To evaluate the control performance, the comparison of the proposed controller was made with conventional control techniques. The Lyapunov stability theorem has been used to testify for asymptotic stability and convergence of the closed loop system.

Implementation of the control law in the real world environment can be easier due to significant reduction in the fuzzy rules, tuning parameters and computation stages.

Simulation results confirm that the proposed control scheme performs remarkably well in terms of the robustness and disturbance attenuation.

The power framework has become a vital part in the day-to-day life and exhibits a rapid development in this current era. Due to the fact of huge power utilization, the power frameworks fall under several power transmission-related concerns. Precisely, frequency deviation has generated a huge impact during power transmission; this in turn highly reduces the power system stability as well as reliability too.

To boost the system’s efficacy, this study proposes a neoteric closed loop feedback controller in which a control algorithm named correlative-elemental-curvature algorithm is introduced with a constant threshold.

With the aim of mitigating frequency deviation, a stability analysis technique called Retrofit Lyapunov’s method is deployed in the controller. This would simultaneously reduce the load disturbances along with tie-line synchronization issues faced with the prior controllers. Optimization is carried out with the aid of duelist optimization algorithm, which tunes the controller parameters thereby mitigating the complexities while designing a loop feedback controller power framework.

The efficacy of the proposed work is assessed with the aid of metrics, such as integral absolute error, accuracy and settling time. Thus, the proposed work enhances the system reliability as well as the stability by mitigating the frequency deviation related issues and guarantees reliable power transmission.

This paper aims to introduce a novel four-dimensional hyper-chaotic system with different hyper-chaotic attractors as certain parameters vary. The typical dynamical behaviors of the new hyper-chaotic system are discussed in detail. The control problem of these hyper-chaotic attractors is also investigated analytically and numerically. Then, two novel electronic circuits of the proposed hyper-chaotic system with different parameters are presented and realized using physical components.

The adaptive control method is derived to achieve chaotic synchronization and anti-synchronization of the novel hyper-chaotic system with unknown parameters by making the synchronization and anti-synchronization error systems asymptotically stable at the origin based on Lyapunov stability theory. Then, two novel electronic circuits of the proposed hyper-chaotic system with different parameters are presented and realized using physical components. Multisim simulations and electronic circuit experiments are consistent with MATLAB simulation results and they verify the existence of these hyper-chaotic attractors.

Comparisons among MATLAB simulations, Multisim simulation results and physical experimental results show that they are consistent with each other and demonstrate that changing attractors of the hyper-chaotic system exist.

The goal of this paper is to construct a new four-dimensional hyper-chaotic system with different attractors as certain parameters vary. The adaptive synchronization and anti-synchronization laws of the novel hyper-chaotic system are established based on Lyapunov stability theory. The corresponding electronic circuits for the novel hyper-chaotic system with different attractors are also implemented to illustrate the accuracy and efficiency of chaotic circuit design.

This study aims to improve the control accuracy and antidisturbance performance of the manipulator with the flexible link, a combined controller, which combines the novel backstepping sliding mode controller based on the extended state observer (ESO) and super-twisting sliding mode controller.

First, the dynamic of the system is constructed by Lagrange method and assumed mode method, and then the dynamic is decoupled by the singular perturbation theory to obtain the slow-varying subsystem and fast-varying subsystem. For the slow-varying subsystem, the novel backstepping sliding mode controller based on ESO is used to achieve joint tracking. For the fast-varying subsystem, the super-twisting sliding mode controller is used for vibration suppression. At the same time, to suppress chattering, the tanh function is used to replace the sign function in the reaching law.

The simulation results show that the combined control has better trajectory tracking performance, antiinterference performance and vibration suppression performance than traditional sliding mode control (SMC).

A novel backstepping sliding mode controller based on ESO is designed to guarantee the performance of the tracking trajectory. The new controller improves the converge rate. A super-twisting sliding mode controller, which can stabilize the fast-varying subsystem, is used to suppress the vibration of flexible link.

Nonlinear systems identification from experimental data without any prior knowledge of the system parameters is a challenge in control and process diagnostic. It determines mathematical model parameters that are able to reproduce the dynamic behavior of a system. This paper aims to combine two fundamental research areas: MIMO state space system identification and nonlinear control system. This combination produces a technique that leads to robust stabilization of a nonlinear Takagi–Sugeno fuzzy system (T-S).

The first part of this paper describes the identification based on the Numerical algorithm for Subspace State Space System IDentification (N4SID). The second part, from the identified models of first part, explains how we use the interpolation of linear time invariants models to build a nonlinear multiple model system, T-S model. For demonstration purposes, conditions on stability and stabilization of discrete time, T-S model were discussed.

Stability analysis based on the quadratic Lyapunov function to simplify implementation was explained in this paper. The linear matrix inequalities technique obtained from the linearization of the bilinear matrix inequalities was computed. The suggested N4SID2 algorithm had the smallest error value compared to other algorithms for all estimated system matrices.

The stabilization of the closed-loop discrete time T-S system, using the improved parallel distributed compensation control law, was discussed to reconstruct the state from nonlinear Luenberger observers.

This paper aims to tele-operate the movement of an unmanned aerial vehicle (UAV) in the obstructed environment with asymmetric time-varying delays. A simple passive proportional velocity errors plus damping injection (P-like) controller is proposed to deal with the asymmetric time-varying delays in the aerial teleoperation system.

This paper presents both theoretical and real-time experimental results of the bilateral teleoperation system of a UAV for collision avoidance over the wireless network. First, a position-velocity workspace mapping is used to solve the master-slave kinematic/dynamic dissimilarity. Second, a P-like controller is proposed to ensure the stability of the time-delayed bilateral teleoperation system with asymmetric time-varying delays. The stability is analyzed by the Lyapunov–Krasovskii function and the delay-dependent stability criteria are obtained under linear-matrix-inequalities conditions. Third, a vision-based localization is presented to calibrate the UAV’s pose and provide the relative distance for obstacle avoidance with a high accuracy. Finally, the performance of the teleoperation scheme is evaluated by both human-in-the-loop simulations and real-time experiments where a single UAV flies through the obstructed environment.

Experimental results demonstrate that the teleoperation system can maintain passivity and collision avoidance can be achieved with a high accuracy for asymmetric time-varying delays. Moreover, the operator could tele-sense the force reflection to improve the maneuverability in the aerial teleoperation.

A real-time bilateral teleoperation system of a UAV for collision avoidance is performed in the laboratory. A force and visual interface is designed to provide force and visual feedback of the slave environment to the operator.

Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics. However, these predictive controllers do not perform robustly in the presence of physics-based model mismatches and uncertainties. Unmodeled dynamics and external disturbances are unpredictable and unsteady, which can dramatically degrade predictive controllers’ performance. To address this limitation, the purpose of this paper is to propose a new systematic approach using frequency-dependent weighting matrices.

In this framework, frequency-dependent weighting matrices jointly minimize closed-loop sensitivity functions. This work presents the first practical implementation where the frequency content information of uncertainty and disturbances is used to provide a significant degree of robustness for a time-domain nonlinear predictive controller. The merit of the proposed method is successfully verified through the design, coding, and numerical implementation of a robust nonlinear model predictive controller.

The proposed controller commanded and controlled a large unmanned aerial system (UAS) with unsteady and nonlinear dynamics in the presence of environmental disturbances, measurement bias or noise, and model uncertainties; the proposed controller robustly performed disturbance rejection and accurate trajectory tracking. Stability, performance, and robustness are attained in the NMPC framework for a complex system.

The theoretical results are supported by the numerical simulations that illustrate the success of the presented technique. It is expected to offer a feasible robust nonlinear control design technique for any type of systems, as long as computational power is available, allowing a much larger operational range while keeping a helpful level of robustness. Robust control design can be more easily expanded from the usual linear framework, allowing meaningful new experimentation with better control systems.

Such algorithms allows unstable and unsteady UASs to perform reliably in the presence of disturbances and modeling mismatches.