This paper investigates distributed optimal teleoperation control for networked mobile manipulators (NMMs) subject to model uncertainties, nonholonomic constraints, and external disturbances. To achieve cost-minimization cooperative control with prescribed transient and steady-state performance, a hierarchical optimization prescribed performance (HOPP) framework is proposed by integrating a reinforcement learning-based optimization estimator (RLOE) with a prescribed performance stability controller (PPSC). In the proposed scheme, the RLOE generates distributed reference trajectories for slave mobile manipulators through local neighbor interactions while minimizing a cooperative performance index. The PPSC is then designed to guarantee bounded tracking errors with prescribed convergence behavior for both the master and slave manipulators. Lyapunov-based analysis is provided to establish the boundedness and convergence properties of the closed-loop system. Rooted in Lyapunov stability theory, the proposed control algorithm is designed to ensure reliability and efficacy. Simulation studies on a teleoperation system of 2-DoF mobile manipulators demonstrate that the proposed method achieves accurate tracking, reduced cooperative cost, and improved transient performance.

This paper presents a novel nonsingular fixed-time distributed optimal formation control strategy for interlinked marine surface vehicles (IMSVs) subject to actuator faults and failures. A cyber-physical framework is constructed to handle such problems. Firstly, a fixed-time distributed optimization estimator in the cyber layer is designed to derives the optimal solution based on the local objective functions through consensus-based algorithms. Secondly, a fixed-time robust tracking controller is constructed in the physical layer with the estimated optimal solution being the reference signal. The overall controller combines fixed-time stability theory and distributed optimization techniques, enabling each vehicle to achieve optimal formation positioning based on local interactions and global formation requirements. Furthermore, a fault-tolerant mechanism is embedded to maintain performance under partial actuator degradation. Lyapunov analysis rigorously proves the fixed-time stability of the closed-loop system, and simulation results validate the effectiveness and robustness of the proposed method.

This paper addresses the path planning and bipartite formation tracking (BFT) problem for multiple Euler-Lagrange agents (MELAs) subject to actuator faults and input saturation. A fully distributed three-layer framework is proposed. In the decision layer, a safe path-velocity Q-learning (SPV-Q-learning) algorithm is proposed, which ensures the rapid generation of reliable paths by introducing a decay mechanism and safety constraints. In the estimated layer, a time-base generator (TBG)-based observer is introduced to reconstruct the leader’s state using only local interactions, then, all followers acquire the leaders state under the fully distributed estimator. In the local layer, an anti-saturation fault-tolerant law is designed to handle model uncertainties, actuator degradation, and bounded inputs, thereby ensuring high-precision trajectory tracking for all agents. Simulation results demonstrate that the proposed method achieves both efficient path planning and robust BFT performance, significantly improving resilience under actuator faults and input constraints.

In this article, we investigate the task optimization for fixed-time control of intermittent human-robot interaction, where a human operator assists the robot intermittently in selecting the most appropriate Pareto solution. First, as for the Lyapunov fixed-time stability criterion inequality with and without the constant term, we all derive the Lyapunov stability conditions with time-varying exponents and coefficients, providing us with more flexibility and freedom to shape the contour of the convergence near the Lyapunov stable equilibrium. We then use them to propose a hierarchical fixed-time event-triggered optimization (HFTEO) algorithm based on human-oriented scheme, where the so-called human-oriented scheme means that the components constituting task information are known only to the human operator, but not to the robot, which is beneficial to ensure the confidentiality and security of the task. Simulation results are given to show the effectiveness of the proposed Lyapunov stability conditions and algorithm.

Formation control based on path planning is an important and critical research topic in robotics, which focuses on generating collision-free paths for multiagent systems (MASs) from an initial position to a target position while maintaining the desired formation. This article realizes adaptive path planning and formation control for MASs with the presence of lumped uncertainties and saturation input. To achieve this goal, a hierarchical adaptive formation planning and control (HAFPC) framework, including a formation path planning layer and an adaptive formation control layer, is constructed. In the formation path planning layer, the signed-distance-field-based formation path planning (SDF-FPP) algorithm is proposed to find a collision-free continuous trajectory in an unknown environment from the initial position to the target position. Based on this collision-free trajectory, a nonanalytic function that evaluates the shortest distance between this collision-free trajectory and obstacles is computed via the signed distance field (SDF) method. Then, this nonanalytic function will be further processed in the next layer for obstacle avoidance of all agents. In the adaptive formation control layer, the proposed adaptive-offset formation control (AOFC) algorithm converts the nonanalytic function into the adaptive offset functions for all agents and manipulates MASs to achieve adaptive formation control for obstacle avoidance with the presence of lumped uncertainties as well as saturation input. Simulations are presented to validate the proposed architecture.

This paper presents the hierarchical Q-learning path planning (HQPP) architecture for solving the cooperative tracking control problem of multi-agent systems (MASs) with lumped uncertainties in an unknown environment. The presented architecture consists of three layers, namely, the decision layer, the distributed estimated layer, and the local control layer. Specifically, in the decision layer, we propose the dynamic parameter and trajectory fitting Q-learning (DPTF-Q-learning) algorithm to find a feasible continuous trajectory to the target in an unknown environment. In addition, two dynamic parameters are proposed and introduced into the DPTF-Q-learning algorithm to shorten the required minimum number of steps in the training process. Then, the distributed estimated layer is designed to broadcast the continuous trajectory generated from the decision layer based on the directed communication topology containing a spanning tree. In the local control layer, the cooperative tracking control (CTC) algorithm is proposed to achieve cooperative tracking for MASs in the presence of uncertain dynamics and external disturbances. The sufficient conditions for achieving cooperative tracking control are rigorously derived by employing Lyapunov argument. Finally, numerical simulations are presented to verify the effectiveness of the proposed architecture.Note to Practitioners—This paper is motivated by the need of developing an integrated path planning and control method for cooperative tracking of multi-agent systems in a no-signal environment and without the presence of users. Most related works are limited to separate fields: 1) most existing path planning techniques are only applicable to a single agent and discrete environments, and 2) most existing cooperative tracking algorithms focus on guaranteeing control stability and error convergence without decision-making capabilities. To address the above issues, this work proposes a hierarchical control architecture based on reinforcement learning for multi-agent systems to achieve path planning and cooperative tracking tasks. In addition, multi-agent systems exhibit strong robustness and fault tolerance due to their inherent characteristics, so the above mentioned research can be well applied to post-disaster rescue, intelligent logistics, future war, and so on. Numerical simulations based on Matlab and Python verify the effectiveness of the proposed architecture.

Since the teleoperation process of networked mobile robotic systems (NMRSs) is generally affected by the coupling of the human force, complex dynamical model, external environment and nonholonomic constraints, designing an effectiveness control to guarantee the desired performance for the teleoperation task is a challenging work. Besides, due to the limitation of the communication and control technology, the controller for the teleoperation system is usually required to have low computation load and low monitoring sampling frequency. To address these challenges, a novel self-triggered model predictive control (STMPC) framework, being consisted of the local STMPC, high-order estimation and M/R fixed-time controller, is constructed. Compared to traditional MPC methods, our proposed local STMPC offers lower computational load and requires fewer monitoring resources while retaining the ability to optimize control performance and handle multiple constraints. Using the presented STMPC framework in a hierarchical manner and newly designed high-order estimation, we successfully and simultaneously solve the teleoperation task while accounting for the self-triggering mechanism, decentralized control implementation, disturbance rejection, and complex nonholonomic model. Additionally, we derive sufficient conditions for ensuring the stability of the closed-loop system. Finally, the simulation and experiment results are provided to demonstrate the effectiveness of the proposed STMPC framework. Note to Practitioners—Our research introduces a groundbreaking framework, self-triggered model predictive control (STMPC), specifically designed to elevate control efficiency, quality, and reliability in the context of remote operation of networked mobile robotic systems (NMRSs). This innovation holds immense promise, particularly in domains requiring the utilization of autonomous vehicle fleets, such as large-scale exploration, search and rescue missions, and escort operations in hazardous or hard-to-reach areas (e.g., forest firefighting, warzone patrolling, saturation attacks, and space escort). These environments are often characterized by extreme external conditions and unpredictable external influences, such as meteorite impacts, flames, obstacles, and explosions, posing significant challenges for the control and task execution of unmanned vehicle fleets. Moreover, these missions demand high precision and control responsiveness from the robotic clusters to swiftly accomplish urgent tasks. Consequently, the need arises for autonomous vehicle fleets that not only possess the capacity to adapt to various constraints and external disturbances but also execute mission tasks swiftly and precisely. STMPC, as presented in this paper, demonstrates its capability to address these issues and requirements effectively, making it a versatile and powerful tool for enhancing control and system performance in various scenarios.

This article addresses the problem of fixed-time bipartite synchronization (FxTBS) of signed networks (SNs) affected by impulses. First, this article constructs a model of SNs that captures the multilayer properties of the network and takes into account the influence of nonlinear coupling strengths between nodes. To overcome the challenges brought by the introduction of nonlinear coupling strengths, this article adopts a Takagi–Sugeno fuzzy model to characterize the nonlinear variation of coupling strengths reasonably. Then, in the framework of average impulsive interval applicable to a wider range of impulsive signals, this article proposes a novel method for analyzing the fixed-time stability of impulsive systems, which not only loosens the restriction of the derivative of the Lyapunov function in the existing studies, but also gives a more accurate estimation of the settling time, and more importantly, provides a theoretical basis for designing appropriate impulsive signals to modulate the dynamic behavior of SNs toward achieving the desired goal. Based on the newly suggested method, this article derives a unified synchronization criterion suitable for evaluating the implementation of FxTBS of SNs under both desynchronizing and synchronizing impulses. Finally, this article visualizes the correctness of the aforementioned theoretical results utilizing a widely used numerical example.

Open multiagent systems (OMASs) feature a dynamic structure with agents continuously joining or leaving, resulting in shifting Nash equilibria and frequent disruptions of equality constraints. This inherent instability poses a significant challenge to traditional incremental-consensus-based distributed optimization or game methods, which rely on a stable and consistent agent population to compute and maintain equilibrium solutions effectively. The necessity for these methods to continuously enforce constraints and the time-intensive process of recalculating equilibria in response to agent dynamics present a substantial bottleneck in the optimization of OMASs. To address this challenge, we develop an innovative incremental consensus-based distributed (ICBD) algorithm to achieve the dynamic Nash equilibrium (NE) for constrained noncooperative game of OMASs. The ICBD algorithm leverages predefined-time stability and integral sliding-mode control to enable rapid recalibration to new equilibria and maintain constraints without the need for prolonged recalculations. Finally, several numerical simulations validate our approach to demonstrating its effectiveness.

Planning trajectories and trajectory tracking are significant and fundamental tasks for Lagrange-based autonomous ground vehicles. In this paper, a novel unified framework integrating path planning and trajectory tracking is proposed based on deep reinforcement learning for Lagrange-based autonomous ground vehicles considering motion limitation and energy consumption, namely, hierarchical piecewise-trajectory planning (HPP) framework. The framework consists of three layers, namely the path planning layer, the trajectory planning layer, and the local control layer. Firstly, the path planning layer enables the vehicle to find a discrete path from its initial position to its target position. Afterward, the trajectory planning layer ensures that discrete trajectory points are transformed into continuous trajectory functions based on the polynomial curve interpolation method. The adaptive asymptotic acceleration planning algorithm is proposed to satisfy the limitations of maximum velocity and acceleration for vehicles. Finally, the trajectory tracking control algorithm and poweroff trigger mechanism are developed to achieve the following two goals in the local control layer: 1) regulating the vehicle to follow its continuous trajectory curve, 2) switching off the power to save energy when its instantaneous kinetic energy is adequate to supply the energy consumption. Numerous simulation results show that our framework enables autonomous ground vehicles to accomplish integrated path planning and trajectory tracking tasks with the presence of motion limitation. Two extra examples are presented to demonstrate that our method is generalizable in terms of energy savings compared to existing optimization-based methods.

This article studies the secure surrounding formation (SSF) problem of networked marine surface vehicles subject to denial of service (DoS) attacks. A hierarchical control framework is developed for designing the predefined-time fuzzy reinforcement learning controller, which consists of two layers. The distributed resilient estimator is proposed to accurately estimate the trajectory of the leader center in the predefined-time under DoS attacks over digraphs. The fuzzy reinforcement learning local controller is designed to achieve the SSF within the predefined-time. The sufficient conditions for system convergence and stability are derived based on the Lyapunov stability theory. Finally, simulation experiments are conducted to verify the effectiveness of the theoretical results.

This article investigates the problem of predefined-time distributed formation tracking for networked marine surface vehicles in the presence of external disturbances. To address this problem, we propose a novel hierarchical predefined-time formation control (HPTFC) framework that integrates a novel sliding mode surface with the vector control Lyapunov functions-based (VCLFs) method. Specifically, the local control layer based on VCLFs is established, which circumvents the usual requirement for positive-definite Lyapunov functions and only necessitates semidefinite positive components. This enables us to search for more appropriate control algorithms in a broader solution space generated by more suitable Lyapunov functions with more general conditions. Through comprehensive theoretical analysis, we demonstrate that the proposed HPTFC framework achieves predefined-time convergence successfully. Eventually, numerical simulations are exhibited to illustrate the effectiveness and superiority of the proposed HPTFC scheme.

This paper presents a novel distributed predefined-time optimization scheme consisting of the distributed optimization estimator and the local controller for the networked marine surface vehicles. Concretely, the distributed optimization estimator is developed to estimate the optimal solution on to the set constraints. In the local control layer, a sliding mode scheme is built to guarantee that the sailing states of networked marine surface vehicles can track the optimal signals obtained by the distributed optimization estimator. Besides, the regulating time of both the estimation and the local tracking process is independent of the initial system states and can be artificially determined by directly adjusting the sum of serval control parameters. Later, a singularity avoidance scheme is further designed to avoid the possible singularity problems of the local control layer. Finally, a simulation example is given to show the efficacy of the proposed distributed predefined-time optimization algorithm.

In this article, considering three different working modes (including the human-supervised, human-aided, and human-manned modes), we present a novel mode-switched control architecture for solving the human-in-the-loop (HIL) teleoperation problem of multislave robots with local slave-to-slave (S2S) communication, long-distance master-to-slave (M2S) communication and transmission delays. Throughout the control process, the S2S communication is updated following the event-triggered mechanism; meanwhile, the data transmission is fully distributed, namely, no global information can be transmitted. Besides, we also deal with the concerns of enhancing “telepresence”, namely, reconstructing the interaction force between a user-determined slave robot and its task environment at the human side, and then allow the human operator to control the multislave robots in a “virtual reality” way. To this end, by making full use of the historical and current data, the data-training-based (DTB) observer is designed to obtain the interaction force at the side of the slave robot and then assist the human operator to choose a proper control mode. The presented architecture is hierarchically designed for data collection, data processing and physical regulation, involving the DTB observer and the fully distributed event-based (FDEB) estimator in a unified framework. Finally, numerical examples are conducted to demonstrate the effectiveness of the architecture.

The classical results of fixed-time stabilization (FxTS) are generally achieved via nonintermittent control, as well as cannot be employed to deal with discontinuous systems and strictly intermittent control. In this article, we establish a novel FxTS method for analyzing fixed-time convergence and newly develop a strictly intermittent control scheme to stabilize discontinuous systems within a fixed time based on it. The presented method can also be used to effectively estimate the settling time and to simultaneously reveal how the control period, control width, and control gain affect the convergence time of the controlled system. Additionally, we also extend the proposed FxTS method and use it to design a new strictly intermittent control scheme for achieving the preassigned-time stabilization (PaTS) of discontinuous systems. Finally, an example of Chua’s circuit is provided to illustrate the feasibility and applicability of the established FxTS and PaTS methods.

This article investigates the hierarchical synthesis of fuzzy fault-tolerant controllers for networked perturbed mechanical systems to solve the problem of finite-time multitarget surrounding, namely, encircling multiple moving targets in a finite time. Given the system with actuator failures, faults, and perturbations (namely, parametric uncertainties and external disturbances), we propose a hierarchical framework for synthesizing a cascade-form controller to stabilize the system state in a finite time, as well as decouple and solve the abovementioned problem. Specifically, the methods of fuzzy logic systems and finite-time adaptive laws are included within the synthesized controller to approximate the unknown perturbations, and meanwhile to compensate the negative effects of the actuator failures and faults. By employing the theories of perturbations and fault-tolerant analysis, we derive the sufficient conditions on control gains for finite-time convergence of the closed-loop system. Finally, we carry out several simulation experiments on a network of six 2-DOF robots and four moving targets to verify the performance of the synthesis method and the obtained controller.

In wireless sensor networks (WSNs), topology control is of great significance for energy efficiency. While various clustering algorithms have been proposed to handle the topology control of WSNs, the joint optimization of fairness, Quality of Service (QoS), load balancing, and congestion management has received much less attention. Additionally, the constrained resources and processing capacity of sensor nodes complicate the topology control problem. This article proposes a three-layer framework based on joint rate-aware fuzzy clustering and stable sensor association that considers various factors of sensor energy efficiency. First, a rate-aware fuzzy clustering method is proposed for initial clustering, and energy-aware cluster head (CH) selection is applied considering the total energy consumption and the residual energy of the potential CHs to improve the energy efficiency of WSNs. After that, the payoff functions of the CHs and the cluster members are constructed for sensor association with resource capacity limitations and QoS constraints. The optimal sensor association is obtained to maximize the payoffs. Furthermore, a low-complexity suboptimal sensor association approach is proposed to reduce the complexity with a tolerable performance gap. Finally, a congestion factor is introduced to balance the load of CHs, and the Gale–Shapley (GS) algorithm is used to avoid unstable sensor association caused by the processing capacity constraints. The simulation results show that the proposed algorithms can effectively improve the resource utilization and the energy efficiency of the WSNs.

This article studies the lag-bipartite formation tracking (LBFT) problem of the networked robotic systems (NRSs) with directed matrix-weighted signed graphs. Unlike the traditional formation tracking problems with only cooperative interactions, solving the LBFT problem implies that: 1) the robots of the NRS are divided into two complementary subgroups according to the signed graph, describing the coexistence of cooperative and antagonistic interactions; 2) the states of each subgroup form a desired geometric pattern asymptotically in the local coordinate; and 3) the geometric center of each subgroup is forced to track the same leader trajectory with different plus-minus signs and a time lag. A new hierarchical control algorithm is designed to address this challenging problem. Based on the Lyapunov stability argument and the property of the matrix-weighted Laplacian, some sufficient criteria are derived for solving the LBFT problem. Finally, simulation examples are proposed to validate the effectiveness of the main results.

In this article, the consensus of networked underactuated robotic systems subject to fixed and switched communication networks is discussed by developing some novel event-triggered control algorithms, which can synchronously guarantee the convergence of the active states, the boundedness of the velocities of passive actuators, and the exclusion of Zeno behaviors. In the cases of fixed networks, the sufficient criteria are established for the presented distributed event-triggered mechanisms with and without using neighbors' velocities, in order to achieve a better tradeoff between the communication load and system performance. Besides, in the situation of switched networks, the sufficient criterion is established by assuming that the union of the network has a spanning tree. A distributed sampled-data rule is constructed to decide when to update its own and neighbors' estimated positions, and thus further reduces the unnecessary control cost. Finally, by further extending the main results to three other sampled-data control algorithms, several examples with performance comparisons are provided to validate the efficiency and advantages of the theoretical results.

This article investigates the predefined-time stabilization problems of Takagi–Sugeno (T–S) fuzzy systems. For addressing the considered problems, a class of novel integral sliding mode surface is first designed based on the time-regulator function, on which the system states are forced to converge to the origin in a predefined time after the sliding mode surface is reached. Further, the proposed sliding surface is employed to construct predefined-time integral sliding mode controller for the time delayed and disturbed T–S fuzzy system. The settling time appears as the sum of two predefined-time parameters in the controller design, which, respectively, adjusts the convergence time for reaching the sliding mode surface and the convergence time for arriving the origin from the sliding mode surface. The sufficient conditions for maintaining the predefined-time stability of the T–S fuzzy systems are obtained through systematic Lyapunov stability analysis. Finally, a numerical simulation example on delayed and disturbed Chua circuit is presented to verify the effectiveness of the proposed predefined-time integral sliding mode controller.

This article investigates the multitarget tracking problem for multiple Lagrangian plants (MLPs) in the presence of sampled-data interactions, uncertain dynamic terms, and input-to-output redundancy. Two classes of impulsive estimator-based control (IEC) algorithms, including the first- and higher-order IEC algorithms, are newly designed to observe the dynamic uncertain terms, estimate the states of the multiple targets, and finally solve the above-mentioned problem. Based on the properties of the small-value norms, Lyapunov stability theory, Schur stability theory, and Hurwitz criterion, some sufficient conditions and the convergence radius are derived for guaranteeing the convergence of these IEC algorithms. Finally, numerical simulations are performed on networked heterogeneous manipulators to verify the effectiveness of the proposed algorithms.

This article investigates the coordination of a class of underactuated systems subject to limited energy supply and channel bandwidth and aims to stabilize system states and exclude Zeno behaviors simultaneously. First, by means of event-triggered (E-T) and quantized techniques, several novel quantized sampled-data-based E-T schemes are constructed, which only require discrete-time controller updates and partial quantized states, and thus efficiently mitigate the control and communication workloads. Then, in order to further lower the communication consumptions, several new triggered sampled-data-based communication rules under fixed and switched networks are established, where the communications are performed only at some specific instants and, thus, the ideally continuous-time signal transmission among neighbors can be avoided. Note that sufficient criteria for achieving the coordination of the underactuated systems are derived in terms of the Lyapunov–Krasovskii functional method. Finally, numerous simulations are carried out to demonstrate the effectiveness of the theoretical results.

In this paper, a unified framework is proposed to address the synchronization problem of memristor chaotic systems (MCSs) via the sliding-mode control method. By employing the presented unified framework, the finite-time and fixed-time synchronization of MCSs can be realized simultaneously. On the one hand, based on the Lyapunov stability and sliding-mode control theories, the finite-/fixed-time synchronization results are obtained. It is proved that the trajectories of error states come near and get to the designed sliding-mode surface, stay on it accordingly and approach the origin in a finite/fixed time. On the other hand, we develop an image encryption algorithm as well as its implementation process to show the application of the synchronization. Finally, the theoretical results and the corresponding image encryption application are carried out by numerical simulations and statistical performances.

This article investigates the finite-time output multiformation tracking (OMFT) problem of networked heterogeneous robotic systems (NHRSs), where each robot model involves external disturbances, parametric uncertainties, and possible kinematic redundancy. Besides, the interactions among robotic systems are described as a directed graph with an acyclic partition. Then, several novel practical finite-time hierarchical control (FTHC) algorithms are designed. The convergence analysis of the closed-loop dynamics is extremely difficult due to the lack of effective analysis methods. Based on the mathematics induction and reductio ad absurdum, a new nonsmooth Lyapunov function is proposed to derive the sufficient conditions and settling time functions. Finally, numerical simulations are performed on the NHRS to verify the main results.

This article addresses two kinds of formation tracking problems, namely: 1) the practical formation tracking (PFT) problem and 2) the zero-error formation tracking (ZEFT) problem for multiple Euler-Lagrange systems with input disturbances and unknown models. In these problems, the bounded input constraint, which can be possibly caused by actuator saturation and power limitations, is taken into consideration. Then, the two classes of model-independent distributed control approaches, in which the prior information (i.e., the structures and features) of the system model is not used, are proposed correspondingly. Based on the nonsmooth analysis and Lyapunov stability theory, several novel criteria for achieving PFT and ZEFT of multiple Euler-Lagrange systems are derived. Finally, numerical simulations and comparisons are presented to verify the validity and effectiveness of the proposed control approaches.

This paper proposes a unified framework to design sliding-mode control for stabilization of delayed memristive neural networks (DMNNs) with external disturbances. Under the presented framework, finite-time stabilization, and fixed-time stabilization of the controlled DMNNs can be, respectively, obtained by choosing different values for a specific control parameter. It is proved that the system responses can be made reaching the designed sliding-mode surface in finite and fixed time, and then stay on it. Moreover, it also illustrates that the inevitable external disturbances can be rejected by the designed sliding-mode control. Finally, the efficiency and superiority of the obtained main results are verified by comparisons with related works and numerical simulations.

This paper proposes several hierarchical controller-estimator algorithms (HCEAs) to solve the coordination problem of networked Euler-Lagrange systems (NELSs) with sampled-data interactions and switching interaction topologies, where the cases with both discontinuous and continuous signals are successfully addressed in a unified framework. The HCEAs comprise two main layers (i.e., a control layer and an estimator layer) and one optional layer (i.e., a filter layer), in which the coordination problem is tackled in the main layers and the transient response can be optionally smoothed in the filter layer. For stabilizing the corresponding cascade closed-loop systems, several sufficient conditions on the upper bound of the aperiodic sampling intervals and the lower bound of the control parameters are established. In addition, the HCEAs are extended to address the task-space coordination problem of networked heterogeneous robotic systems, which shows the versatility of the HCEAs. Finally, comparison studies and simulation results are provided to demonstrate the effectiveness, significance, and advantages of the presented algorithms.

This paper studies the fully-distributed consensus problem for multi-agent systems with unknown dynamics and bounded external disturbances. The interaction topology of the multi-agent system is assumed to contain a directed spanning tree. Based on adaptive gains, we present the fully-distributed consensus protocol which can obtain consensus without using any global information. The simulation examples are given to verify the effectiveness of the main results.

A novel adaptive sliding mode control algorithm is derived to deal with synchronized joint position tracking control of robot manipulators. The proposed algorithm does not require the precise dynamic model, and is very practical. The cross-coupled technology is incorporated into the adaptive sliding mode control architecture through feedback of joint position errors and synchronization errors. Its robustness is verified by the Lyapunov stability theory. Simulation results obtained from a 3-link non-linear planer robot manipulator demonstrate the effectiveness of the approach under various disturbances.

A novel adaptive sliding mode control (ASMC) algorithm is derived to deal with seam tracking control problem of welding robotic manipulator, during the process of large-scale structure component welding. The controllers robustness is verified by the Lyapunov stability theory, and the analytical results show that the proposed algorithm enables better high-precision tracking performance with chattering-free than classic sliding mode control (SMC) algorithm.