Narrow Results

This paper investigates the leader-follower exponential consensus problem of a class of Lipschitz nonlinear multi-agent systems (MASs) with input saturation. Since each agent has nonlinear dynamics, the system is not asymptotically null controllable with bounded controls. Therefore, the widely-used low-gain feedback method for designing consensus protocols of MASs with input saturation can no longer work. Taking advantage of the stability theory of impulsive systems and features of the Laplacian matrix, and combining the properties of convex hull, a distributed impulsive consensus protocol is proposed. Still, the shape reference set is introduced to assess the attraction domain of leader–follower MASs. Finally, a numerical experiment validates the effectiveness of the proposed anti-saturation impulsive consensus algorithm.

This paper is concerned with the problem of constraint control for cooperative adaptive cruise control (CACC) with input saturation and input-additive uncertainties. An integrated longitudinal kinematic model of CACC system including vehicle model and constant time headway is established taking into account input saturation and input-additive uncertainties. According to the system’s robustness requirements under input saturation, the saturation control method is introduced. In order to achieve robust global stabilization of the system, a low-gain state feedback control law is designed by using linear low-gain feedback and gain scheduling. Meanwhile, in order to avoid the saturation of the control system, the low gain parameter $\beta$ is introduced into the controller design. Finally, the simulation of homogeneous and heterogeneous platoons is carried out by MATLAB/Simulink, which verifies the feasibility and effectiveness of the designed controller. Compared with the SMC controller, saturation controller successfully suppresses the acceleration amplification in the process of propagation along the vehicle platoon, avoids actuator saturation and realizes the stability of CACC system.

The impulsive synchronization of coupled neural networks with input saturation and the term of reaction–diffusion via a hybrid control strategy is investigated. In this paper, a hybrid controller is proposed, including impulsive controller with input saturation and intermittent controller. This type of hybrid controller can not only solve the periodic and aperiodic intermittent control, lower the update frequency of the controller, but also avoid the saturation phenomenon of impulsive control. Based on linear matrix inequalities (LMIs), and Jensen’s inequality, under a proposed suitable Lyapunov function, a series of sufficient conditions are established to guarantee the stability of the error system. Compared with the recent relevant impulsive saturation results, the polytopic representation method dealing with actuator saturation may make the synchronization criterion more universal and less restrictive. Finally, a numerical example is provided to verify the correctness and feasibility of the theoretical results.

The dynamic nonlinear system with saturation constraints and unknown information is studied to design a better control system. The controller of the general n-order affine nonlinear system is constructed utilizing the backstepping method and the improved radial basis function (RBF) that can approximate the unknown parameters. Then, the appropriate Lyapunov function is selected. The mature stability judgment theorem can prove that the designed system is semi-globally uniform and ultimately bounded. Finally, the controller is added to the system for the experiment on MATLAB. The experimental results show that the controller is reliable and the system output can approach the ideal value through the limited range of control signal. During the simulation, the reference value can be quickly tracked by the system output, and the tracking error is small only within 0.04. The proposed method can provide a reference for the design of control systems in practical engineering.

In this paper, a novel fuzzy sliding-mode backstepping control approach is put forward to tackle the tracking control issue of fractional-order chaotic nonlinear systems subject to uncertain control coefficient and input saturation. First, a kind of sliding surface with nonlinear term of exponential monotonic attenuation is introduced to facilitate the fast error convergence and the chaos effect suppression. Subsequently, fuzzy logic systems and Nussbaum gains are synthesized to cope with the entire uncertainties including unknown control coefficients. Furthermore, dynamic surface technique is utilized to circumvent the occurrence of “complexity explosion” during the overall backstepping control procedure. By means of the proposed control scheme, system state is guaranteed to maintain close to the sliding surface without any reaching phrase, and whereafter the tracking error converges to a sufficiently small residual set containing the origin. Finally, the comparison between the proposed control scheme and an alternative existing chaos control approach is given through numerical simulation, which further confirms the validity of the obtained results and the superiority of the proposed strategy.

In this article, a fuzzy adaptive finite time (FT) output feedback tracking fault-tolerant control (FTC) scheme is designed for stochastic non-strict feedback nonlinear systems (NSFNSs) with actuator failure, input saturation, and unknown control direction. The system studied in this article is more complex than previous results and more in line with the actual situation. The advantages of this study are as follows: (i) A fuzzy state observer of high gain is established to deal with the states unmeasured problem of the system. (ii) The Nussbaum function is employed to address unknown control direction and actuator failure problems, and the Smooth non-affine function is utilized to dispose of the input saturation. (iii) The dynamic surface technique introduces errors that are eliminated by using the error compensation technology, and fuzzy logic systems (FLSs) are applied to appropriate unknown nonlinear functions in the system. The controller developed in this study guarantees that tracking errors can converge to a small neighborhood around the origin within FT and overall signals in the stochastic system are semi-global finite-time stable in probability (SGFSP). Finally, three simulation examples are introduced to verify the effectiveness of the suggested control strategy.

A synchronization control scheme is proposed for uncertain coronary artery system (CAS) with input saturation. In order to deal with the input saturation, linear matrix inequalities (LMIS), adequate conditions are obtained based on the local sector condition. Furthermore, by constructing Lyapunov–Krasovskii functional (LKF), we design a state feedback controller to achieve synchronization for chaos system with input saturation. Moreover, the improved Jensen inequality, convex analysis, delay-partitioning approach and Moon et al.’s inequality are utilized to get the less conservative. Finally, the simulation result is given to explain the effectiveness of the proposed synchronization control scheme.

This paper addresses the coverage control problem for a network of mobile sensors with first-order discrete-time dynamics, where the goal is to minimize a coverage cost function which is defined as the largest distance between any point on a circle and its nearest sensor. Since in practice sensors’ moving speed is always upper bounded, there exist saturation constraints on the control inputs of the mobile sensors. In this paper, distributed coverage control laws with input saturation are developed to drive the sensors to the final configuration such that the coverage cost function is minimized. It is also shown that the spatial order of the mobile sensors is preserved throughout the network’s evolution. As a result, collision avoidance between mobile sensors is always guaranteed.

This paper proposes robust controllers for a class of unmanned tracked ground vehicles (UTGVs), which are built to autonomously clean carryback or spillage from the conveyor belts used in the mining industry. The UTGV, a nonholonomic system in its nature, needs to follow a given path in a harsh environment with large uncertainties due to the time-varying mass and inertia when the UTGV loads and unloads as well as unknown frictions and flatness of the ground. Moreover, the input constraints coming from motors do exist. It is usually hard to design robust controllers for such complex systems. By utilizing the available autonomous driving system, which is designed to be compatible with the existing remote motion controller in unmanned systems to generate autonomous ability, this paper uses the off-the-shelf motion planner to calculate desired linear and angular velocities based on the given path and sensor perceptions. Consequently, the control design can be simplified as two decoupled linear time-invariant scalar dynamic systems with uncertainties, making the active disturbance rejection controller (ADRC) applicable. By carefully designing the parameters of ADRC with the help of an extended state observer (ESO), it is shown that the proposed ADRC and ESO can achieve good tracking performance in the presence of input saturation and can handle nonsmooth disturbances. The proposed simulation results and experimental results support the theoretical findings.

Machine learning-based data-driven approaches have greatly improved system identification capabilities and facilitated the application of model-based control algorithms. However, techniques such as neural networks require significant amounts of training data and have limited generalization capabilities. To overcome this problem, we employ the sparse identification of nonlinear dynamics with control (SINDYc) for system identification, which considers both system states and control inputs. Based on the identified system, we design the controller using the backstepping control method. In order to make the algorithm more practical in real-world scenarios, we introduce an input saturation compensation system into the controller design. Additionally, we apply a command filter into the method to avoid deriving a virtual control signal and reduce the computational complexity of the controller. Through stability analysis, the proposed control algorithm ensures that the tracking error in the system is bounded. Finally, we verify the effectiveness of the proposed SINDYc-Backstepping framework by conducting simulations using a single-link robot arm.