This paper investigates the distributed resilient control method for mesh stability of two-dimensional(2-D) plane intelligent connected and autonomous vehicle (ICAV) platoons with external disturbances and false data injection (FDI) attacks. Firstly, the third-order nonlinear dynamics of the vehicular platoons on the 2-D plane are presented by considering the kinematics and dynamics of vehicular systems on the longitudinal and lateral direction. Then, the prescribed-time composite observer is developed to solve the coupled effects of external disturbances and FDI attacks for each follower vehicles. A distributed prescribed-time mesh stability resilient control method is presented to ensure the mesh stability and resilience performance of the vehicular platoons within a prescribed-time. Finally, the simulation results are shown to verify the effectiveness of the proposed scheme.

In this paper, the distributed prescribed-time resilient control problem of the two-dimensional (2-D) plane heterogeneous connected vehicular platooning system (HCVPS) subject to the weaken communication and false data injection (FDI) communication attack threats is investigated. Firstly, for achieving the multi-lane vehicle merging, longitudinal following, and vehicular platooning lane changing, the third-order nonlinear dynamics of the HCVPS on the 2-D plane are considered. By developing the distributed information reconstruction observer, the weaken communication problem of leading vehicle can be mitigated by each following vehicles within a prescribed time. A distributed prescribed-time resilient control modeling framework is proposed that maintains the resilience performance and the desired safety inter-vehicle distance, ensures string stability within a prescribed-time. Finally, the results of Simulation of Urban Mobility (SUMO)-Simulink joint experimental platform and comparison cases are shown to verify the effectiveness of the developed scheme.

Network congestion and communication resource competition are common and destructive challenges in single-master-multiple-slaves (SMMS) teleoperation systems. In this paper, an adaptive event-triggered scheduling control for SMMS teleoperation systems is proposed. The event-triggered scheme is based on the position and velocity signals of robots with an adaptive threshold to dynamically adjust the triggering frequency. A scheduling protocol is constructed to allocate the signal transmission priority for slave robots based on three triggering states. Subsequently, a cooperative controller is developed utilizing the master triggered signals and the slave triggered and scheduled signals, guaranteeing the stability and position tracking performance of the system. The system stability and the convergence of the position tracking error are proved with Lyapunov functions and validated through simulations. The proposed method guarantees the stability and position tracking performance of SMMS teleoperation systems while concurrently alleviating network congestion and communication resource competition.

This paper studies an adaptive event-triggered security control for networked control systems (NCSs) under aperiodic denial of service (DoS) attacks and false data injection (FDI) attacks. A state-FDI attack observer is designed to estimate the unmeasurable system state and unknown FDI attacks simultaneously. An adaptive event-triggered scheme (AETS) is proposed to conserve network resources while eliminating the impacts of DoS attacks, where the event-triggering (ET) parameter is adjusted according to the average value and the rate of change of neighboring state estimation errors. Based on the AETS, a security control strategy is designed to compensate for FDI attacks actively. Meanwhile, the entire closed-loop switched system is constructed under DoS and FDI hybrid attacks. Using the Lyapunov–Krasovskii function, the exponential stability conditions of NCSs with [Formula: see text] are obtained. Furthermore, the gain matrices of the observer, the ET parameter of the AETS, and the gain matrices of the active compensation controller are co-designed. Finally, the proposed scheme is verified by a numerical example and practical experiment on a brushless direct current motor NCS. The proposed scheme can not only defend hybrid DoS and FDI attacks in NCSs but also effectively relieve network congestion and improve communication efficiency.

A fixed-time fractional second-order sliding mode active fault-tolerant control scheme is proposed for robotic systems with actuator faults. The actuator faults are first estimated via a fixed-time fault observer. Adopting a fractional-order sliding mode surface and introducing a double-power reaching law, a fixed-time fractional second-order sliding mode controller is obtained to achieve active fault-tolerant control of robotic systems. Subsequently, an adaptive law is designed to deal with the fault estimation error. It is mathematically proved that the position tracking error can achieve a fixed-time convergence with the proposed scheme. Simulations show that compared with integer-order finite-time sliding mode control schemes, the proposed control scheme can enhance the fault compensation ability, improve tracking accuracy, and accelerate system response within a fixed time while effectively mitigating chattering.

This research focuses on the false data injection attacks (FDI) and communication resource competition in single-master-multiple-slaves (SMMSs) teleoperation systems and proposes a security control strategy based on dynamic try-once-discard (DTOD) scheduling protocol. An attack detector is designed to detect FDI attacks on signals transmitted through the communication channel. With the attack detection result, a DTOD scheduling protocol is designed to dynamically adjust the transmission priority of the slaves. Using the attack detection result and scheduled position and velocity, a switching controller is developed to ensure the position tracking between the master and slaves. The stability of the overall system is proven via Lyapunov functions, and comparative results validate the effectiveness of the proposed strategy. The proposed strategy not only optimizes resource allocation but also effectively defends against cyberattacks, enhancing the stability and tracking accuracy of SMMS teleoperation systems.

In this paper, adaptive iterative learning impedance control is developed for a lower limb rehabilitation exoskeleton subject to unknown reference trajectory, unknown nonlinearities, and actuator saturation. A novel dual-loop learning control strategy is proposed for human-exoskeleton interaction, where the outer control loop is designed to follow a target impedance model and the inner position loop is constructed for tracking a balanced trajectory. First, the contact force between the patient and the exoskeleton is used to learn the reference trajectory in an iterative manner. Second, under the framework of backstepping technique, an adaptive iterative learning controller is developed to deal with the unknown nonlinearities and improve the tracking performance. In order to ensure the safety of patient's limbs during human-exoskeleton interaction, the actuator saturation is considered and addressed by introducing an auxiliary system. Third, with the design of the reference trajectory learning algorithm and the adaptive iterative controller, the convergence of both the target impedance following and trajectory tracking is proved rigorously, and the boundedness of all the involved signals are guaranteed. Finally, the effectiveness of the proposed control scheme is verified by both simulation and experimental study on a 2-DOF exoskeleton.

Torque ripples and disturbances pose significant obstacles to achieving superior speed regulation performance of the permanent magnet synchronous motor (PMSM). This study proposes an enhanced sliding mode control (SMC) approach to further elevate the dynamic responsiveness and antidisturbance ability of the PMSM. First, an advanced fast reaching law (AFRL), which introduces system errors into the power and exponential terms, is proposed to simultaneously reduce the reaching time and sliding mode chattering. A modified sliding mode observer (MSMO) is then constructed to assess the variation of the load torques, and an iterative learning law is designed to learn the periodic disturbances. The integration of the MSMO and iterative learning law forms an iterative composite compensation strategy, which effectively elevates the observation accuracy of system disturbances and strengthen the system's robustness. The enhanced sliding controller is consequently developed according to the AFRL and the iterative composite compensation. The stability of the AFRL and the closed-loop PMSM system, as well as the convergence of tracking errors, are rigorously substantiated using the Lyapunov theory. Experimental results reveal that the proposed controller exhibits smaller torque ripples, faster convergence speed, reduced chattering, and considerable antidisturbance capability. Note to Practitioners-The inspiration for this article arises from the difficulties encountered in attempting to apply a PMSM to potential high-end industrial applications, such as robotics, numerical machining, and precise instruments. These scenarios demand remarkable dynamic responsiveness and antidisturbance ability, requiring a sophisticated control system. Most of the existing control methods exhibit contradictions in dynamic performance, including overshoot, convergence speed, and disturbance rejection capability. Therefore, this article proposes an enhanced disturbance attenuation control strategy for high-performance control of a PMSM system. This approach can be applied to a servo system equipped with PMSMs to enhance the dynamic performance and increase the robustness of the system.

A synchronization control method is proposed for nonlinear teleoperation systems with dynamic uncertainty, time-varying delay and input saturation. Time delay estimation (TDE) is utilized to estimate the dynamic uncertainty including the parametric uncertainty and unknown disturbance. A hyperbolic tangent saturation function with adjustable factor is designed to restrict the control input saturation and make the control input smoother at the critical saturation threshold. Further, the estimated value obtained from TDE is introduced into a PD-like controller to derive the position synchronization controller. The stability and convergence of the system is proved via Lyapunov function. It is experimentally verified that the synchronization control method can restrict the control input within the saturation threshold. Besides, the position tracking error is able to converge quickly under time-varying delay.

In order to address actuator faults in an underactuated system, a fault-tolerant control strategy based on extended state observer is proposed. The extended state observer is designed to estimate the actuator fault information. With the estimated fault value, a fault-tolerant controller is developed to accomplish position tracking, where the position error of the system is confined within a specified range by a strictly monotonically decreasing constraint function. The stability of the closed-loop system and the convergence of position tracking are proved by Lyapunov theory. The effectiveness of the proposed method is validated through comparative simulations. The proposed approach not only compensates for the impact of actuator faults on underactuated system but also ensures rapid convergence of position error, keeping it within the prescribed constraints.

The tracking performance of robotic teleoperation systems is constrained by limited communication resources. To improve the communication efficiency and tracking accuracy, an integral adaptive event-triggered predefined-time control strategy is proposed. The integral adaptive event-triggered mechanism determines whether the master and slave robots need to update their data by evaluating the error integral between the current and previously triggered data, thereby reducing the communication network access frequency. Using the transmitted data at triggering moments, a predefined-time sliding mode controller is designed to ensure accurate position tracking between the master and slave. The stability of the overall system is proven and the superiority of the proposed strategy is verified via simulations. The proposed approach can significantly reduce information transmission and ensure that the position tracking error between the master and slave converges within a predefined time.

To address the influence of multiple forces on the accuracy of position tracking in human-robot interaction systems, this paper proposes a vision impedance control method based on game theory. With the end-effector position obtained by vision feedback and the adaptive impedance law, the human-robot interaction force can be estimated. The position error is then converted into a constraint force to ensure the output position remains within a specified limit. The preset control force, human-robot interaction force, and constraint force are regarded as participants and a multi-party cooperative differential game algorithm is developed to derive the optimal impedance controller for the end-effector. The convergence of the position error is proved using Lyapunov functions. The performance of proposed method is validated through simulations and experiments. The proposed method can flexibly adjust the proportions of various complex forces on the end-effector during the human-robot interaction. During the interaction phase under multiple complex forces, the mean squared error of the end-effector position with the proposed method is merely 7.7 % and 6.0 % of those obtained with the sensorless force estimation-based control and the repetitive impedance learning-based control, respectively. Meanwhile, it can reduce the computation complexity of conventional vision methods and improve the tracking accuracy within the constraints.

A predefined-time fault-tolerant control is proposed for robotic systems with output constraint. A predefined-time sliding mode observer (PTSMO) is designed to estimate the actuator faults. The position tracking error is constrained by a performance function and transformed by a transform function. With the transformed error and the fault information estimated by the PTSMO, a PTSM controller is designed. The system is proved to be stable and convergent within the predefined time while satisfying the output constraint performance. Simulation results indicate that when the robotic system encounters actuator faults, the proposed control can guarantee that the position tracking error is within the performance range and converges fast within a predefined time.

A fixed-time control strategy based on adaptive event-triggered communication and force estimators is proposed for a class of teleoperation systems with time-varying delays and limited bandwidth. Two force estimators are designed to estimate the operator force and environment force instead of force sensors. With the position, velocity, force estimate signals, and triggering error, an adaptive event-triggered scheme is designed, which automatically adjusts the triggering thresholds to reduce the access frequency of the communication network. With the state information transmitted at the moment of event triggering while considering the time-varying delays, a fixed-time sliding mode controller is designed to achieve the position and force tracking. The stability of the system and the convergence of tracking error within a fixed time are mathematically proved. Experimental results indicate that the control strategy can significantly reduce the information transmission, enhance the bandwidth utilization, and ensure the convergence of tracking error within a fixed time for teleoperation systems.

An adaptive event-triggered security control method is proposed for networked robotic teleoperation systems subject to time-varying delays and false data injection (FDI) attacks. An event-triggered scheme is designed via the position and velocity signals of the master and slave robots, where the triggering thresholds can change adaptively with the system states. The position and velocity triggered signals and the feedback error between the operator and the environment forces are utilized to detect whether the transmitted signals suffered from FDI attacks. Then, a switching controller is designed, which is able to adopt the corresponding control strategy according to the corresponding detection result. The stability of the system and the convergence of the tracking errors are proved through Lyapunov functions, and the proposed method is validated through practical experiments. The proposed method is able to adaptively adjust the triggering frequency and effectively reduce the transmitted data, thus saving the network resources. Meanwhile, it can ensure the stability of networked teleoperation systems under time-varying delays and FDI attacks, as well as the position and force tracking performance.

In order to stabilize underactuated robotic systems with external disturbances, an adaptive hierarchical sliding mode control strategy based on extended state observer is proposed. The extended state observer is designed to estimate the joint states and lumped disturbance composed of matched and unmatched disturbances. The underactuated robotic system is divided into two subsystems. For each subsystem, a sub-sliding mode surface is constructed to obtain the first layer sliding mode surface and the second layer sliding mode surface is derived from the first layer sliding mode surface. Then the hierarchical sliding mode controller is designed with the estimated state obtained from the observer to compensate the lumped disturbance and an adaptive law is designed to adjust the switching gain. The stability of the system is proved by Lyapunov theory and the effectiveness of the proposed control strategy is verified by comparative simulations. With the proposed control, the tracking performance of the underactuated robotic system is effectively improved and the convergence time of the system is reduced.

This paper concentrates on network congestion and security issue for networked control systems under the hybrid attacks. The hybrid attacks including deception attack, replay attack and denial‐of‐service attack are modelled as Bernoulli random process. A general form adaptive event‐triggered scheme (AETS) under the hybrid attacks is designed to alleviate the network congestion and save communication resources utilizing adaptive threshold and the weighted average of data packets. Meanwhile, the security issue under the hybrid attacks is addressed by a dynamic output feedback controller (DOFC) based on the AETS. Moreover, the sufficient conditions are obtained by a piecewise Lyapunov function to guarantee that the closed‐loop system is exponentially mean‐square stable. A practical experiment on networked motor control system verifies the effectiveness of the proposed scheme. The proposed scheme can not only save communication resources to further alleviate network congestion, but also defense the hybrid attacks in the network.

A position and force tracking control based on force estimation is proposed for bilateral teleoperation systems with time-varying delays. A time-delay state observer is employed to estimate the system state variables affected by the delays. To estimate the interaction forces effectively, a force estimation algorithm with adaptive law is designed. Based on the estimated states and forces, a P+D controller is designed to simultaneously guarantee the position and force tracking of the system. The stability and tracking performance of the closed-loop system are proved via Lyapunov functions, and the feasibility of the proposed control is verified by both simulations and experiments. The proposed control can improve the position and force tracking performance of bilateral teleoperation systems under time-varying delays. Meanwhile, it neither requires force measurement nor the bound of the derivative of the time-varying delays to be within one.

Due to the essence of openness, networked control systems (NCSs) are susceptible to malicious attacks, network congestion and data leakage, which brings significant control challenges. This paper investigates NCSs under denial-of-service attacks (DoS) and proposes an adaptive memory-event-triggered encryption control method. An adaptive memory-event-triggered mechanism (AMETM) is designed to reduce the loss of critical data and save network resources. Based on the AMETM, an encryption-decryption scheme is designed to guarantee data privacy. Then an augmented switched system is constructed to characterize the impact of DoS attacks, AMETM and encryption-decryption scheme in a unified framework. The conditions for exponential stability of the system are obtained. Solutions to the observer gain, triggering parameters and controller gain are also given. The effectiveness of the proposed scheme is verified by a simulation. The results show that the scheme not only saves network resources and reduces the loss of critical data under DoS attacks, but also ensures data privacy and system stability.

Adaptive tracking control method for shearer remains challenging caused by the contradiction between coal recovery rate and equipment safety under current technological limitations, has drawn more and more attention over the past decades due to its important role in reducing personnel, increasing safety, and improving efficiency for underground mining. Therefore, a novel adaptive tracking control method for shearer cutting trajectory based on the combined strategy is proposed, whose core is the switching method of the sub-strategies based on the linear quadratic regulator (LQR) and error band control (EBC) with switching threshold determined by the Multi-strategy Marine Predator Algorithm. The performance of the proposed method is demonstrated by comparing with the existing methods reported. The experimental results reveal that the proposed method maintains a high recovery rate while improving coal mining efficiency.