This paper investigates the event-triggered predefined time output feedback control design problem for nonlinear interconnected systems with non-strict feedback control structures. Compared with the existing event-triggered output feedback control design schemes, the most significant contribution of this paper is that the system stability time can be preset directly. Fuzzy logic systems (FLSs) and FLSs-based state observer deal with unknown nonlinear dynamics and unmeasured states. Combining with dynamic surface control technology and event-triggered mechanism based on switching threshold strategy, an event-triggered predefined time decentralized output feedback control method is proposed, in which a predefined time filter is designed to solve the computational complexity problem. In addition, the algebraic loop problem and control singular problem are also solved respectively by applying the property of fuzzy basis function and L’Hospital’s rule. Utilizing predefined time Lyapunov stability theory, the system stability analysis is given. Finally, the effectiveness and practicability of the proposed control method are verified by practical system simulation cases.

This paper proposes an adaptive fuzzy output feedback control approach for quadrotor unmanned aerial vehicles (QUAV) with stochastic disturbances, besides which we consider the unmeasurable states and unknown nonlinear functions. The QUAV system contains stochastic terms which are not bounded and not differentiable. By combining the It $\hat{\rm \textbf{o}}$ differential equation with finite-time theory, a novel stochastic finite-time stability controller for QUAV is raised for the first time. The fuzzy logic system (FLS) is utilized to approximate the unknown nonlinear functions in the model. Dynamic surface control technology is introduced to reduce the complexity of differential. Furthermore, an observer with FLS is designed through the adaptive backstepping technique to estimate the immeasurable states. It is proved that this control approach can ensure that all states of the closed-loop QUAV system are semi-global and finite-time stable in probability. Meanwhile, the errors of system outputs and observer converge to a small neighborhood of origin. The simulation results show the effectiveness of the proposed method. Note to Practitioners —In practical applications, many systems are often affected by internal and external disturbances, and not all of them can be described by mathematical models, so they are called stochastic disturbances. Taking the QUAV as an example, when the system is disturbed by internal parameters, external environment, system input, sensor error, and other stochastic factors, the deterministic system can no longer accurately describe the controlled object. In this paper, the QUAV is regarded as a stochastic system for the first time, and a novel controller is designed to solve the above problems. In addition, the QUAV needs fast action in some applications, such as emergency collision avoidance, which has high requirements for the convergence performance of the controller. Therefore, studying the finite-time control for QUAV systems with stochastic disturbances is necessary. At the same time, this paper also considers the problem that the state is not measurable and the system contains unknown nonlinear equations.

This paper studies the predefined time control design issue for uncertain nonlinear systems with full-state error constraints and unmeasurable states for the first time. Compared with existing works, this study enables the controlled system to stabilize within a predetermined time and ensures that the full-state tracking errors converge to a desired control accuracy range, even in the absence of measurable state information. Fuzzy logic systems (FLSs) are applied to handle unknown nonlinear dynamics, and FLSs-based nonlinear state observer is constructed to estimate unmeasurable states. With the universal barrier Lyapunov function and the dynamic surface control (DSC) technique, an output-feedback-based full-state error constrained control strategy with predefined time stability is proposed, in which a non-singular predefined time filter is constructed to avoid high computational complexity. Under this control strategy, the predefined time stability of the closed-loop system is achieved and the full-state error constraints are satisfied. Finally, a series of simulation results confirm the effectiveness and superiority of the proposed control strategy.

This paper researches the adaptive fuzzy tracking control problem for uncertain nonlinear systems with multiple actuators and sensors faults. Compared with previous studies, all states of the system can not be measured accurately in this paper due to the existence of multiple sensors faults, and it brings significant difficulties to the design of the control scheme. Moreover, multiple actuators faults and external disturbance can also bring challenges to controller design. To solve these problems, we design different adaptive update laws to relieve the effects of unknown actuators faults, sensors faults, and external disturbance, respectively. Furthermore, the actual states can be estimated by combining sensors outputs with adaptive parameters. On this basis, the unknown nonlinear functions are approximated through the combining of fuzzy logic systems (FLSs) and the estimation of states. Then, a novel adaptive fuzzy tracking control algorithm is completed by the backstepping method. By employing the Lyapunov function, the presented novel fault-tolerant control algorithm can guarantee all signals of the system are bounded in spite of the occurrence of multiple faults. Finally, we verify the availability of the novel algorithm by comparing the control performance of the two algorithms.

In this article, we propose the dynamic coverage control method based on K -means. In the traditional coverage control, Voronoi partition method is used to assign the coverage positions for intelligent units. However, the Voronoi partition method requires that the space to be covered is compact and convex, and it is difficult to realize the coverage control of high-dimensional space. Therefore, in this article, we propose a dynamic coverage planning algorithm based on K -means, which relaxes the requirements on the coverage objects and can calculate the optimal coverage positions of the intelligent units. In addition, we also design a dynamic coverage control law based on discrete sliding mode control to drive the intelligent units to the optimal coverage positions. Through the combination of planning algorithm and control law, the optimal coverage control performance can be achieved for the specified targets and the specified area, which can be discrete, nonconvex, and high dimensional. The stability of the planning algorithm and the control law are proved by theoretical deduction. The effectiveness and superiority of the dynamic coverage control method are verified by two examples.

This article proposes the fuzzy swarm control based on sliding-mode strategy with self-organized omnidirectional mobile robots (OMRs) system. In previous work, swarm control is realized by using sliding-mode strategy. However, chattering in the OMRs system is very serious. To overcome this drawback, a new fuzzy swarm control for OMRs system is designed, which introduces the fuzzy logic to replace switching control in traditional sliding-mode control (SMC). The fuzzy SMC (FSMC) can weaken chattering for sliding-mode strategy in the OMRs system. However, it is difficult to design swarm control law for OMRs because of model uncertainties, external disturbance and the introduction of fuzzy logic. The FSMC is proposed in this article and its stability is analyzed. Simulation and experiment results show that the fuzzy swarm control based on sliding-mode strategy with self-organized OMRs system can realize stability asymptotically and chattering is weakened.