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In this paper, an adaptive fuzzy-based global sliding mode control strategy is proposed for quadrotor unmanned aerial vehicles (UAVs) in robust trajectory tracking against parameter uncertainties and external disturbances. Compared with the conventional sliding mode control, the reaching phase and the control chattering are eliminated in the control scheme, and requirements of the upper bound of the uncertainties are removed. More specifically, in order to counteract the disturbances, the fuzzy system with multiple-input variables and continuous membership functions is adopted rather than the switching term, which softens the control signals greatly. Besides, the use of specific singletons membership functions for the multiple-output fuzzy sets simplifies the defuzzification and reduces the computation burden significantly. Additionally, an adaptive tuner is employed, and the optimal control efforts can be achieved. Finally, comparative flight performances under different controllers for the quadrotor UAVs are demonstrated to verify the effectiveness and superiority of the proposed control approach.

This study presents a strategy for an intelligent vehicle trajectory tracking system that employs an adaptive robust non-singular fast terminal sliding mode control (ARNFTSMC) approach to address the challenges of uncertain nonlinear dynamics. Initially, a path tracking error system based on mapping error is established, along with a speed tracking error system. Subsequently, a novel ARNFTSMC strategy is introduced to tackle the uncertainties and external perturbations encountered during actual vehicle operation. The adaptive laws established for the longitudinal demand force and the front-wheel steering angle do not require prior understanding of the upper limit of the lumped uncertainty, while successfully avoiding singularities and eliminating chattering. By applying Lyapunov’s stability theorem, it is shown that the control system for trajectory tracking can reach the equilibrium point within a finite time. Following this, a torque optimization distribution control strategy is developed. Ultimately, numerical simulations are used to validate both the effectiveness of the proposed approach and its robustness across different conditions.

The piezoelectric smart structures, which can be labeled as the cream of the crop of smart structures without overstatement, are strongly impacted by a large number of uncertainties and disturbances during operation. The present paper reviews active disturbance rejection control (ADRC) technologies developed for application in piezoelectric smart structures, focusing on measurement, analysis, estimation, and attenuation of uncertainties/disturbances in systems. It first explained vast categories of uncertainties/disturbances with their adverse influences. Then, after a brief introduction to the application of basic ADRC in smart structures, a thorough review of recently modified forms of ADRC is analyzed and classified in terms of their improvement objectives and structural characteristics. The universal advantages of ADRC in dealing with uncertainties and its improvement on the particularity of smart structures show its broad application prospects. These improved ADRC methods are reviewed by classifying them as modified ADRC for specific problems, modified ADRC by nonlinear functions, composite control based on ADRC, and ADRC based on other models. In addition, the application of other types of active anti-disturbances technologies in smart structures is reviewed to expand horizons. The main features of this review paper are summarized as follows: (1) it can provide profound understanding and flexible approaches for researchers and practitioners in designing ADRC in the field and (2) light up future directions and unsolved problems.

This paper proposes a prescribed-time formation control scheme with low complexity and performance guarantees for second-order nonlinear multi-agent systems with a directed graph. A continuous finite-time control based on the barrier Lyapunov function and a novel performance function is proposed. Within our scheme, the unmodeled dynamics and external disturbances of the system are handled by the combination of a function approximator and a disturbance observer based on neural network and sliding mode control, respectively. Different from many existing finite-time control results, the proposed scheme can set the settling time of the closed-loop system in advance, and there is no discontinuous control term and chattering phenomenon. Finally, it is proved that all signals in the closed-loop system are uniformly ultimately bounded. At the same time, a set of numerical comparisons intuitively illustrate the effectiveness and superiority of the proposed controller.

A large input delay, parametric uncertainties, matched disturbances and mismatched disturbances exist extensively in variable air volume air-conditioning systems, which can deteriorate the control performance of the room temperature and even destabilize the system. To address this problem, an adaptive-gain command filter control framework for the room temperature of variable air volume air-conditioning systems is exploited. Through skillfully designing an auxiliary system, both the filtered error and the input delay can be compensated concurrently, which can attenuate the effect of the filtered error and the input delay on the control performance of the room temperature. Then, a smooth nonlinear term with an adjusted gain is introduced into the control framework to compensate for parametric uncertainties, matched disturbances and mismatched disturbances, which relieves the conservatism of the controller gain selection. With the help of the Lyapunov theory, both the boundedness of all the system signals and the asymptotic tracking performance for the room temperature can be assured with the presented controller. Finally, the contrastive simulation results demonstrate the validity of the developed method.

In this paper, a non-negative adaptive mechanism based on an adaptive nonsingular fast terminal sliding mode control strategy is proposed to have finite time and high-speed trajectory tracking for parallel manipulators with the existence of unknown bounded complex uncertainties and external disturbances. The proposed approach is a hybrid scheme of the online non-negative adaptive mechanism, tracking differentiator, and nonsingular fast terminal sliding mode control (NFTSMC). Based on the online non-negative adaptive mechanism, the proposed control can remove the assumption that the uncertainties and disturbances must be bounded for the NFTSMC controllers. The proposed controller has several advantages such as simple structure, easy implementation, rapid response, chattering-free, high precision, robustness, singularity avoidance, and finite-time convergence. Since all control parameters are online updated via tracking differentiator and non-negative adaptive law, the tracking control performance at high-speed motions can be better in real-time requirement and disturbance rejection ability. Finally, simulation results validate the effectiveness of the proposed method.

Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a linearized expression method of platoon error dynamics based on state measurement, the state feedback mechanism combined with a nonsmooth controller for a truck platoon is proposed in the development of the distributed control method. The state-feedback controller can drive the nominal platoon system to the state of second-order consensus, and the nonsmooth controller counterparts the uncertainties and disturbances. The convergence and string stability of the proposed control algorithm are demonstrated both theoretically and experimentally, and the effectiveness and robustness are also verified by simulation tests.

This paper proposes an adaptive neural-network-based nonsingular fast terminal sliding mode (NN-NFTSMC) approach to address the trajectory tracking control problem of a quadrotor in the presence of model uncertainties and external disturbances. First, the dynamic model of the quadrotor with uncertainty is derived. Then, a control scheme using nonsingular fast terminal sliding mode control (NFTSMC) is proposed to guarantee the finite-time convergence of the quadrotor to its desired trajectory. NFTSMC is firstly formulated for the case that the upper bound of the lumped uncertainty is known in advance. Under this framework, a disturbance observer by using the hyperbolic tangent nonlinear tracking differentiator (TANH-NTD) is designed to estimate the external interference, and a neural network (NN) approximator is used to develop an online estimate of the model uncertainty. Subsequently, adaptive algorithms are designed to compensate the approximation error and update the NN weight matrix. An NN-NFTSMC algorithm is formulated to provide the system with robustness to the model uncertainty and external disturbance. Moreover, Lyapunov-based approach is employed to prove the global stability of the closed-loop system and the finite-time convergence of the trajectory tracking errors. The results of a comparative simulation study with other recent methods illustrate the proposed control method reduces the chattering effectively and has remarkable performance.

I The purpose of this paper is to propose a fast adaptive terminal sliding mode control method for quadrotor unmanned aerial vehicles (UAVs) under dynamic uncertainties and external interference. Firstly, a complete mathematical dynamics model of a quadrotor UAV is presented. Secondly, a robust dual-loop nonlinear control law for the quadrotor velocity and attitude tracking is designed. Through the introduction of equivalent control inputs, the attitude control channels can be decoupled and attitude stabilized controllers can be designed separately. For velocity tracking and altitude tracking control, coupled controllers are designed to ensure velocity tracking based on the stability of altitude tracking control. The attitude loop adopts RBF neural network sliding mode control, to compensate for model disturbance uncertainty, adjust its gain in real time, and suppress chattering problems. Finally, the experimental results show that the designed controller can not only suppress external interference, but also achieve accurate tracking of the desired flight command.

In this paper, fractional-order chaotic synchronization of multiple drive and response systems in secure communications is studied and analyzed. First, dual combination–combination synchronization (DCCS) of fractional-order chaotic systems (FOSs) problem consisting of four drive systems and four response systems is discussed. Then, robust controllers and adaptive parameter update laws are designed under the effects of model uncertainties and external disturbances, and sufficient conditions are achieved by using Mittag-Leffler stability theory. Finally, DCCS of FOSs is applied to the secure communications, and the numerical simulation verifies the effectiveness of the synchronization plan and the feasibility of the secure communications scheme.