In this paper, a practically finite-time active disturbance rejection control (PFTADRC) method is proposed for angle tracking on a 3-degree-of-freedom (3-DOF) parallel mechanism of pneumatic muscle actuators (PMAs) with couplings and disturbances. A coupling characterization is presented to determine whether the couplings are beneficial for the system. Based on this, a practically finite-time controller is designed, where detrimental and beneficial couplings are eliminated and remained, respectively. Moreover, when couplings change, the practically finite-time stability of the system is analyzed and guaranteed. Two Lyapunov criteria of practically finite-time stability are proposed to improve convergence rate of the system. Both the bounded region and the settling time are calculated. Finally, the numerical simulation results and experiment results are presented to illustrate the effectiveness of the proposed method.

In this paper, a trajectory tracking control problem is investigated for a wheeled mobile robot (WMR) on uneven road. A mapping relationship is established between the velocity and position to describe the motion of the robot on uneven road. An adaptive kinematic controller (AKC) is designed to improve the trajectory tracking accuracy, where robot parameters and control gains are both estimated. Then, a fixed-time generalized variable gain extended state observer (VGESO) is proposed to estimate the states and disturbance caused by uneven road, where a balance is maintained between disturbance rejection and noise suppression. Next, a dynamic controller is put forward by combining fully actuated system (FAS) approach with practical prescribed time (PPT) control. It is analyzed that the velocity tracking error system is PPT stable. Finally, experimental results demonstrate effectiveness and superiority of the proposed method.

The adaptive event-triggered control problem for a class of uncertain high-order pure feedback nonlinear systems (HOPFNSs) is considered. Different from the traditional backstepping method, a new high-order backstepping method is proposed based on the high-order fully actuated (HOFA) system approaches to design the adaptive event-triggered control law, which has the significant advantages of simple structure, high degree of freedom, and easy to realize. The high-order backstepping method does not need to transform the HOPFNSs into the first-order systems, which is more efficient and significantly reduces design complexity. It is proved that the adaptive event-triggered controller makes all the signals of the system bounded and save the energy in signal transmission. A simulation example is performed to verify the effectiveness of the control strategy.

In this article, an indoor positioning system is investigated for an unmanned ground vehicle based on inertial measurement unit (IMU) and ultrawide band (UWB) integration. Measurement outliers of UWB usually exist due to multipath and non-line-of-sight (NLOS) effects. To describe characteristics of stochastic noises well, heavy-tailed noises modeled by Student’s t distributions are considered. By a NLOS identification method, a local modified fusion estimator is designed based on the Kalman filter framework to fuse the measurements of IMU and UWB. A modified matrix-weighted fusion estimator is proposed for high-accuracy estimation and robustness. Sufficient conditions are given for the fusion estimation error to be exponentially bounded in mean square. Finally, effectiveness of the proposed fusion estimator is indicated by experimental results.

In this article, the game-based backstepping control method is proposed for the high-order nonlinear multi-agent system with unknown dynamic and input saturation. Reinforcement learning (RL) is employed to get the saddle point solution of the tracking game between each agent and the reference signal for achieving robust control. Specifically, the approximate optimal solution of the established Hamilton-Jacobi-Isaacs (HJI) equation is obtained by policy iteration for each subsystem, and the single network adaptive critic (SNAC) architecture is used to reduce the computational burden. In addition, based on the separation operation of the error term from the derivative of the value function, we achieve the different proportions of the two agents in the game to realize the regulation of the final equilibrium point. Different from the general use of the neural network for system identification, the unknown nonlinear dynamic term is approximated based on the state difference obtained by the command filter. Furthermore, a sufficient condition is established to guarantee that the whole system and each subsystem included are uniformly ultimately bounded. Finally, simulation results are given to show the effectiveness of the proposed method.

This article focuses on hybrid dynamic event-triggered security tracking control of wheeled mobile robots (WMRs) against round-trip stochastic denial of service (DoS) attacks. To characterize the random properties, DoS attacks are modulated as Markov switched processes. A hybrid dynamic model is characterized and a hybrid dynamic event-triggering mechanism (ETM) is proposed for the WMR, which achieve larger triggering intervals than the static one. To actively defend against the DoS attacks and achieve the trajectory tracking control, a hybrid dynamic controller is designed with a dynamic parameter that reflects the DoS attacks in real time. The uniformly globally asymptotically stability (UGAS) condition of the addressed hybrid dynamic WMR system is obtained. Practical experiments are provided for illustration of the provided theoretical results.

This article addresses the problem of stabilizing a continuous-time switched system affected by a completely unknown disturbance, data quantization, and time-varying delay. In the sense of combined dwell-time and average dwell-time, it is assumed that the switching is slow enough. Suppose that the bound of delay is known but the one of disturbance is unknown. An estimation for the bound of disturbance is used to counteract the unknown disturbance. By extending the approach of the delay-free case, a communication and control strategy is developed by introducing a virtual system. On this basis, the exponential decay and practical stability of the closed-loop system are guaranteed by using a Lyapunov function. Two examples are illustrated to show the usefulness of the proposed framework for stability analysis of some classes of switched systems.

In this article, event-triggered distributed fusion is investigated for a multirate multisensor system subject to heavy-tailed noises under packet dropout. The heavy-tailed noises are modeled by a multivariate$t$-distribution for the multirate multisensor system. An event-triggered local estimator is designed to gain local estimates with packet dropout in communication networks. An event-triggered distributed fusion algorithm is proposed by sequential fast covariance intersection (SFCI) fusion technology. Sufficient conditions on the boundedness of the estimation error scale matrices are given for the event-triggered local estimator and distributed fusion. Finally, numerical simulation is put forward to indicate validity of the presented event-triggered local estimator and distributed fusion.

This article is concerned with a distributed state estimation problem over sensor networks. The communication links of the sensor networks are subject to bounded time-varying transmission delays. A distributed Kalman filtering algorithm is designed to estimate the state based on a Kalman consensus filtering algorithm. Moreover, sufficient conditions are derived for convergence on estimation errors and the boundedness of error covariances, respectively. Finally, the effectiveness of the designed algorithm is validated by a simulation example.

This article is concerned with the distributed covariance intersection (CI) fusion estimation for cyber-physical systems (CPSs) with delayed measurements and unknown inputs. The measurement transmission is subject to random delays described by a set of independent Bernoulli processes. Based on the provided finite-length buffers, the delayed measurements are retrieved within the corresponding buffer length. By modeling the unknown inputs with a noninformative prior distribution, a local minimum mean square error (MMSE) estimator is derived in the Bayesian framework. Then this result is extended to the multiple sensor scenario, where the sequential CI fusion approach is applied to design a recursively distributed fusion estimator. It is proved that the distributed sequential CI fusion estimator is consistent and performs better than each local estimator in state estimation. An illustrative example is provided to demonstrate the effectiveness of the proposed technique.

This paper concentrates on the remote state estimation problem for nonlinear systems over a communication-limited wireless sensor network. Because of the non-Gaussian property caused by nonlinear transformation, the unscented transformation technique is exploited to obtain approximate Gaussian probability distributions of state and measurement. To reduce excessive data transmission, uncontrollable and controllable stochastic event-triggered scheduling schemes are developed to decide whether the current measurement should be transmitted. Compared with some existing deterministic event-triggered scheduling schemes, the newly developed ones possess a potential superiority in maintaining Gaussian property of innovation process. Under the proposed schemes, two nonlinear state estimators are designed based on the unscented Kalman filter. Stability and convergence conditions of these two estimators are established by analyzing behaviors of estimation error and error covariance. It is shown that an expected compromise between communication rate and estimation quality can be achieved by properly tuning event-triggered parameter matrix. Numerical examples are provided to testify the validity of the proposed results.