This paper proposes a digital twin-based trajectory planning and control strategy for dynamic obstacle avoidance in wheeled mobile robots (WMRs) to enhance operational efficiency and safety. A digital twin system for linking a virtual WMR and a physical one is developed from virtual space, physical space, application service, and data processing. Based on real-time environment and obstacle information in the virtual space, obstacle avoidance planning is completed by combining an improved D-star algorithm with quadratic programming to obtain a reference trajectory. A model predictive controller is designed to track the reference trajectory for the virtual WMR. In the physical space, a backstepping sliding mode controller drives the physical WMR to synchronize its motion with the virtual one. Experiment results demonstrate the effectiveness of the trajectory planning and control strategy for dynamic obstacle avoidance.

Human-robot collaboration is crucial for integrating robots into intelligent manufacturing (IM). However, a significant challenge is rational decision-making for the human-cyber-physical system (HCPS) in IM to enhance cognitive limits of human operators and overcome the potential irrationality. Since humans dominate the collaboration in IM, it is essential to address two critical issues: determining the next task for the robot and deciding whether the human operator should be informed. We propose a risk-aware decision-making framework for task allocation and human-robot interaction (HRI) to achieve a balance between the autonomy level of the human operator and task efficiency. To quantify the efficiency risk, we utilize conditional value-at-risk (CVaR) considering the uncertainty of human operators. We then obtain the optimal task allocation and selection for the robot by minimizing the efficiency risk. We also establish a necessary collection of tasks that must be performed by the human operator. Furthermore, we develop two criteria to quantify the necessity of explicit HRI. Experiments with a real mechanical arm platform demonstrate that our methods can enhance human-robot collaboration (HRC), reduce the need for extensive communication, and grant human operators greater execution freedom. Note to Practitioners —This research is motivated by the fact that the imperfect information sharing between human operators and robots in IM. It results in the inability of human operators to accurately comprehend the robot’s capability constraints and the robot’s limitations in perceiving the current or next action of the human operator. As a result, the effectiveness of HRC then inevitably decreases. Our proposed method introduces the efficiency risk to quantify the need of robot active intervention in HCPS. This approach aids in determining when and what to communicate with the human operator, thereby empirically ensuring efficiency and providing the human operator with a greater degree of freedom. Experimental studies suggest that the efficiency risk can serve as a new metric for balancing the amount of human-robot communication and the improvement of efficiency. Our research can also find utility in human-centric scenarios where communication between the human operator and the robot is constrained or costly.

Distributed consensus control problems of multiagent systems (MASs) subject to total disturbances are studied in this article, where all agents communicate over a fixed topology with undirected or directed connections. First, a novel nonlinear fast convergence function and a novel nonlinear extended state observer (NNESO) are proposed for MASs to realize real-time estimation of total disturbances. Then, an active disturbance rejection control (ADRC) method based a consensus control protocol with disturbance compensation is presented for MASs to reduce the impact of total disturbances on consensus control of MASs. Furthermore, the stability and convergence of MASs are strictly proved by the Lyapunov stability method, which indicates that MASs under constant disturbances can realize consensus without state errors, and MASs under time-varying disturbances can realize consensus with state errors, which can be converged to an arbitrary and assigned neighborhood. Finally, the advantages of the NNESO are demonstrated by comparing the convergence speed of the nonlinear convergence function of the NNESO with that of the conventional ESOs, and the effectiveness of the consensus control method for MASs on the basis of ADRC is verified by numeric simulations and actual experiments.

In this article, a time-optimal turning around method is designed based on model predictive control (MPC) for an autonomous vehicle under steering lag. To address the steering lag issue, a second-order steering model is established for the autonomous vehicle through system identification. A time-optimal turning around trajectory is obtained using a quintic polynomial method and a nonlinear MPC optimization problem. A nonlinear MPC algorithm is then developed to track the time-optimal trajectory with compensation for the steering lag. Asymptotic stability and recursive feasibility are analyzed for the autonomous vehicle based on the nonlinear MPC algorithm. Experimental results demonstrate the feasibility and effectiveness of the time-optimal turning around method in an autonomous electric vehicle with steering lag.

In this paper, a nonlinear control strategy is proposed for a pneumatic manipulator based on an adaptive extended state observer and a backstepping integral sliding mode controller. A single degree of freedom dynamic model is established for the pneumatic manipulator using an Euler-Lagrange dynamic equation. The adaptive extended state observer is designed by an adaptive law to estimate uncertainties and disturbances. The backstepping integral sliding mode controller is proposed using an integral sliding mode surface based on backstepping technique. Comparative experiments verify effectiveness of the proposed nonlinear control strategy for the pneumatic manipulator.

In this article, a digital twin system is investigated to achieve virtual-physical tracking control for a carlike mobile robot (CLMR). The digital twin system consisted of a physical entity, a virtual entity, and a virtual-physical interaction module for the CLMR. The virtual-physical tracking control is used to complete motion mapping from the physical entity to the virtual entity. Although external disturbances acting on the physical entity are different from the virtual entity, only one nonlinear extended state observer is designed in this article, which is suitable in estimating the external disturbances for both the physical and the virtual entities. A backstepping controller and an integral sliding mode controller are designed for both the physical and the virtual entities to realize motion mapping. Experiment results show effectiveness and superiority of the virtual-physical tracking control by digital twin technology for the CLMR.

In this article, a digital twin system is designed for intelligent management of a unit level wheeled mobile robot (WMR). A framework of the digital twin system is arranged from four main parts, i.e., physical WMR, virtual WMR, data processing, and application service. To realize high-fidelity modeling of the physical WMR, a 3D model of the virtual WMR is established in Webots from aspects on geometric structures, kinematic analysis, and actuator control. Based on the digital twin system, a virtual-physical mapping control strategy is proposed for remote motion interaction between the physical WMR and the virtual WMR. Finally, effectiveness of application service is shown in a synchronous-motion experiment with the digital twin system via a Mecanum wheeled mobile robot.

In this paper, we investigate trajectory tracking and obstacle avoidance for a nonholonomic system subject to external disturbances by a nonlinear switched model predictive control (MPC) strategy. In the nonlinear switched MPC strategy, a potential field is introduced in a cost function to guarantee a smooth path for the nonholonomic system. A switched mechanism is designed to ensure switching stability by setting multiple Lyapunov functions in different areas. Different from traditional switched mechanisms, an average dwell time closely related to stability conditions is proposed to balance safety and stability in the whole process. Recursive feasibility is presented for the nonlinear switched MPC strategy in trajectory tracking and obstacle avoidance. Simulation results are provided to show effectiveness and superiority of the nonlinear switched MPC strategy by a two‐wheeled mobile vehicle.

In this article, an adaptive longitudinal control strategy is proposed for a multivehicle cooperative system with actuator saturation. The multivehicle cooperative system is modeled as a longitudinal system. Uncertain disturbances caused by road bumps during vehicle driving are estimated by an adaptive extended state observer based on Silverman canonical transformation and pole placement. An adaptive integral sliding mode control algorithm is designed to accomplish adaptive longitudinal control and handle actuator saturation. With Lyapunov criterion, stability is presented for the multivehicle cooperative system with the adaptive longitudinal control strategy. Simulation results are given to show effectiveness of the adaptive extended state observer and the adaptive integral sliding mode control algorithm.

It is an essential task to guarantee satisfactory tracking performance for a warehouse mobile robot with a detachable load. To this end, a nonlinear extended state observer (ESO)-based tracking control is investigated via a double closed-loop framework in this paper. A kinematics controller is designed in an outer loop to generate desired velocities for the warehouse mobile robot. A nonlinear ESO with an improved error function is proposed in an inner loop to estimate load variations and internal unmodeled dynamics. Then a nonlinear error feedback controller based on estimation values is given to track the desired velocities from the outer loop. Simulation and experiment results illustrate the effectiveness and superiority of the proposed control strategy.

In this paper, lateral control is investigated for an uneven road driving electrical vehicle subject to unmodeled tire dynamics by an extended state observer (ESO). A bicycle model with unmodeled tire dynamics is used as a lateral system of the vehicle for the lateral control. The ESO based on a generalized super-twisting algorithm is designed to estimate the unmodeled tire dynamics. A nonlinear controller is utilized to track a reference trajectory with lateral position errors and heading errors. Stability on the ESO and the nonlinear controller are analyzed for the lateral system by Lyapunov methods. Experimental results are given to show effectiveness of the proposed control method for electrical vehicles.

This paper investigates the tracking control problem of second-order multi-agent systems (MASs) in the presence of unmatched disturbances and completely unknown dynamics. The extended state observer (ESO) and neural networks (NNs) are utilized to estimate and compensated the unmatched disturbances and unknown dynamics, respectively. By constructed a novel integral sliding-mode manifold incorporated with ESO output, a neural-network-based control algorithm is developed. Meanwhile, by Lyapunov theoretical analysis, the UUB stability of the tracking errors as well as within a sufficiently small region is guaranteed by the appropriate choice of the parameters. Simulation results show that the proposed method exhibits much better control performances than the traditional I-SMC method, such as great robustness, reduced chattering and more accurate.

In this paper, the attitude control problem of rigid body is addressed with considering inertia uncertainty, bounded time-varying disturbances, angular velocity-free measurement, and unknown non-symmetric saturation input. Using a mathematical transformation, the effects of bounded time-varying disturbances, uncertain inertia, and saturation input are combined as total disturbances. A novel finite-time observer is designed to estimate the unknown angular velocity and the total disturbances. For attitude control, an observer-based sliding-mode control protocol is proposed to force the system state convergence to the desired sliding-mode surface; the finite-time stability is guaranteed via Lyapunov theory analysis. Finally, a numerical simulation is presented to illustrate the effective performance of the proposed sliding-mode control protocol.

This note proposes a notion of scaled cluster consensus, wherein the final consensus states within different clusters converge to prescribed ratios. Unlike most results in existing literature on cluster consensus, no constraints are imposed on the system topologies under the designed protocol, i.e. the agents are not required to possess any cluster affiliation information of others. For the delay-free case, an explicit scaled cluster consensus function is provided by exploring the characteristics of stochastic matrices. Diverse input delays and asymmetric communication delays are both considered, and sufficient condition for scaled cluster consensus is derived based on frequency domain analysis. Finally, numerical examples are given to illustrate the effectiveness of the presented results.

This paper investigates the cluster consensus problems of generic linear multi-agent systems with switching topologies. Sufficient criteria for cluster consensus, which generalise the results in existing literatures, are derived for both state feedback and observer-based control schemes. By using an averaging method, it is shown that cluster consensus can be achieved when the union of the acyclic topologies contains a directed spanning tree within each cluster frequently enough. We also provide a principle to construct digraphs with inter-cluster cyclic couplings that promote cluster consensus regardless of the magnitude of inter-agent coupling weights. Finally, numerical examples are given to demonstrate the effectiveness of the proposed approaches.

This study investigates the consensus problem of second-order multi-agent systems subject to time-varying interval-like delays. The notion of consensus is extended to networks containing antagonistic interactions modeled by negative weights on the communication graph. A unified framework is established to address both the stationary and dynamic consensus issues in sampled-data settings. Using the reciprocally convex approach, a sufficient condition for consensus is derived in terms of matrix inequalities. Numerical examples are provided to illustrate the effectiveness of the proposed result.

This paper addresses the stabilization and optimization problem of networked control systems (NCSs) with long time delays and parameter scheduling. According to the actual network conditions, the network time delay is divided into the fixed time delay and the random time delay. Then, the stabilization controller and the gain scheduling controller are constructed, where the stabilizing control parameters are obtained with cone complementary linearization (CCL) approach and the optimizing control parameters are solved with estimation of distribution algorithm (EDA). Simulation results demonstrate the effectiveness of the proposed methods.

The purpose of this paper is to study positioning control performance of the one-DOF manipulator driven by pneumatic artificial muscles using active disturbance rejection controller. Owing to the pneumatic artificial muscle's highly nonlinear and time-varying behavior, it is difficult to achieve good positioning performance. In this paper, the nonlinear and time-varying behavior of pneumatic artificial muscle is considered as disturbance to be estimated by extended state observer. Tracking differentiator is designed to get corresponding smooth signal and differential signal of reference input to avoid overshoot. A linear error feedback combining with estimated value compensation of disturbance is designed to ensure a good response of system. Moreover, stability analysis of the close-loop system is given by Lyapunov theory. Finally, simulation results verify the effectiveness of the proposed controller.

Advanced control theories and techniques based on Data Fusion provide higher-quality information from multiple sensor data by spatio-temporal data integration, the exploitation of redundant and complementary information, and the available context.Important applications exist in distributed sensor network, aerospace, robotics, monitoring and control of manufacturing processes.Techniques for such data fusion are drawn from a broad set of disciplines including: control theory, statistical estimation, signal and image processing, artificial intelligence, information sciences.Particular emphasis should be placed on advances in the theory of distributed sensing and processing, localization and tracking, nonlinear filtering, network resource management, selection and integration of algorithms, data fusion based on heterogeneous sensors.Recently, a wide range of research has been reported dealing with the related problems.The current state of the art of such research is the subject of 5 papers in the present "Special Section on International Journal of Control, Automation, and Systems".We briefly summarize the content of this special issue.

This paper is concerned with the stabilization problem for a class of discrete-time networked control systems (NCSs) with bounded time delays and packet losses. The controlled plant is represented by a Takagi-Sugeno fuzzy model, and both the state feedback control and output feedback control cases are considered. By guaranteeing the decrement of Lyapunov functional at each control signal updating step, a less conservative stability condition for the state feedback NCSs is derived, and the corresponding stabilizing controller design method is also presented. Under an observer-based framework, the output feedback stabilization problem is further studied, where the main contribution is the development of the separation principle for NCSs. Illustrative examples are provided to show the advantage and effectiveness of the developed results.