Achieving a real-time precise grasp of a specified target object in densely cluttered environments is an essential capability for autonomous robot operation. Recently, considerable investigations on planar and spatial grasp have been carried out, and significant results have been obtained. However, these point cloud-based grasp prediction methods often fail to ensure that the generated grasp configurations meet the precise requirements of the task. Additionally, some of the existing grasp pipelines are too time-consuming to meet the demand for real-time robot response. In more challenging cluttered scenes, the quality of pose and gripper jaw opening estimation in highdimensional space requires further improvement. Therefore, this paper introduces a data- and model-independent and efficient method to generate 7-DoF grasp configurations for arbitrary target objects from single-view point cloud data in dense cluttered scenes. In addition, this paper proposes a grasp framework that generates the grasp configuration for the target object while reducing the time consumed during the grasp process, to enable robots to efficiently grasp target objects for designated tasks. The grasp pipeline focuses on guided regions via target detection and rapidly adjusts grasp configurations through multi-region point cloud distribution perception. Extensive real-world robot experiments have demonstrated the effectiveness of the proposed method in grasping target objects in cluttered scenes, achieving higher success rates and reduced runtime compared to baseline methods. The realized code and video are available at https://github.com/L-tj/7DGCG.

Nonholonomic vehicles in distributed networks are prone to triggering nested velocity and acceleration saturation during reactive safety formations, exacerbating oscillations. This paper proposes a hybrid secure distributed collaborative frame-work, integrating compound adaptive anti-windup strategies with vehicle kinematics and safe geofences to achieve smooth and effective obstacle and collision avoidance while suppressing saturation-induced oscillations. The vehicle’s safe behavior for bypassing obstacles is formed via acceleration envelopes from safe geofences and input saturation, which generate constraint velocity commands. Additionally, a low-trigger and power-adjustable enhanced artificial potential field is integrated into the safety coordination to fine-tune vehicle maneuvers at extremely close distances to hazardous targets, ensuring high reliability. Safe acceleration envelopes and nested kinematic saturation are utilized to design a compound adaptive auxiliary dynamic system, smoothing oscillations induced by dual command constraints during formation. A distributed formation controller is further designed to enable multitasking collaboration in formations. The overall stability is mathematically analyzed, and the method’s superior smoothness and safety in task coordination are validated through simulations and experiments with vehicle clusters. Note to Practitioners—In response to the severe trajectory oscillations caused by saturation triggered by existing reactive avoidance approaches, this paper proposes a novel hybrid safety collaborative control based on the nonholonomic vehicle kinematics that markedly enhances the smoothness and safeness of formations in obstacle environments. The integration of safety acceleration envelopes, as well as low-trigger and adjustable artificial potential functions, markedly mitigates oscillations from reaction saturation compared with the solitary traditional artificial potential functions, as evidenced by simulations and experiments that demonstrate reduced oscillation amplitudes and shorter recovery times when evading hazardous targets using the proposed method. In addition, existing velocity/acceleration nested windups in actual applications are concurrently considered for the first time, and the corresponding compound adaptive anti-windup method is employed to smooth oscillations caused by control saturation. The security and smoothing strategies outlined allow for collaborative operations in more complicated obstacle environments and enable the deployment of larger vehicle clusters in confined spaces, significantly enhancing multi-vehicle collaboration’s economic viability and efficiency. Furthermore, the safety collaborative control framework designed for kinematics is conveniently structured for engineers as a standalone module, which is easily transferrable to commercial robotic products. The composite approach to safeness and smoothness can also be applied in other unmanned and manned collaborative scenarios.

The autonomous and security are the crucial requirements in fields of the polar transportation. This paper proposes a newly iterative learning control framework for the autonomous surface vessels (ASV) to implement the path-following operation in the ice floes scenario. The proposed framework is divided into two parts: the guidance and control. For the former, the ice floes are firstly identified into threatening and non-threatening based on the size. Subsequently, the obstacle area of each threatening ice floe is programmed considering the underwater portion. Then the ice floes avoidance guidance with auto-select mechanism for ice-zone traversal mission is constructed by setting the hazard threshold and target point. For the latter, a robust adaptive iterative learning control (ILC) system is designed for the path-following mission, where the control accuracy increases with the number of iterations. The stability of the closed-loop control system is proved with utilization of the Lyapunov theorem. Finally, two numerical examples are provided to evaluate the advantages and accuracy of the proposed algorithm, where the ice floes are generated with irregular.

This article investigates the distributed control of a group of stochastic high-order nonlinear systems in which the subsystems are with unknown and time-varying control coefficients of unknown signs, inherent nonlinear drift and diffusion terms. To solve the control problem with unknown control directions, where traditional available Nussbaum functions are not applicable for the consensus of stochastic nonlinear systems with unknown and time-varying coefficients of unknown signs, a novel type of Nussbaum function is proposed with a new paradigm of stability analysis in probability. Global consensus control of stochastic multiagent systems is achieved by designing distributed controllers which integrate designed distributed filters and novel Nussbaum functions. In addition, it can be proved that all signals in the closed-loop system are bound in probability, and the transient consensus errors of the followers are bounded by positive constants which can be adjusted arbitrarily small. The effectiveness of the proposed control scheme is demonstrated by simulation results.

This paper introduces a system developed to tackle the challenge of enclosing and tracking a moving target in multi-robot systems, taking into account motion and field of view (FOV) constraints. Initially, a method utilizing relative position measurement is proposed. Following this, a reference trajectory is crafted to adhere to the motion and FOV constraints. Finally, considering the uncertainty in mobile robots and incorporating the prescribed performance bound (PPB) technique, adaptive tracking control solutions are devised. Experimental results show that the robots efficiently follow the designated reference trajectory, ensuring guaranteed transient performance for position and direction tracking errors, complying with motion and FOV constraints, and achieving swift enclosure and tracking of target objects.

This article investigates the leaderless output consensus control problem for a class of nonlinear multiagent systems with heterogenous system orders and unmatched unknown parameters via output-feedback control. The interaction topology among the agents is undirected and jointly connected. Due to the heterogenous system orders and switching topology among the agents, the classical distributed adaptive backstepping-based control technique cannot be applied to solve the problem considered in this article. To solve this issue, a novel distributed reference system is first proposed for each agent, by using only relative outputs of the neighboring agents. Subsequently, a fully distributed reference system-based adaptive leaderless output consensus control scheme is designed via output-feedback control. A remarkable merit of the proposed control scheme lies in that precisely known nonlinear dynamics, system states, distributed parameter estimates, and the states of virtual reference system are no longer needed to be shared with neighbors. This implies that the communication burden can be effectively alleviated, and even the communication network can be replaced by some perception sensors. Finally, two illustrative examples are provided to verify the effectiveness of the proposed control scheme.

Many networked systems built upon real-life physical or social interactions have time-varying connections among individual units, where the temporal changes in connectivity and/or interaction strength lead to complicated dynamics. The temporal network model was proposed in the form of controlled linear dynamical systems acting in an ordered sequence of time intervals. One of the core challenges in network science is the control of networks and the optimization of the control strategy. However, most canonical frameworks for solving optimal control problems were established for static networks featuring constant topology. New theories and techniques are yet to be developed for the temporal networks, with an important case being that the input and the source-node connection are both variables. In this work, by formulating a quadratic energy cost without solving the Riccati differential equation, we show that the control effort can be reduced substantially by improving either the system trajectories or the input matrices. The two approaches are further combined in a coordinate descent framework, integrating linearly constrained quadratic programming, and a projected gradient descent method. Taken together, the results underline the potential of temporal networks as energy-efficient control systems and present strategies to improve the control input. Moreover, the proposed algorithms can serve as a starting point for future engineering of real-world temporal networks.

In this article, the problem of integral sliding mode control (ISMC) for a class of nonlinear systems with stochastic characteristics under cyber-attack is investigated. The control system and the cyber-attack are modeled as an Itô-type stochastic differential equation. The stochastic nonlinear systems are approached by the Takagi-Sugeno fuzzy model. A dynamic ISMC scheme is applied and the states and control input are analyzed within a universal dynamic model. It is demonstrated that trajectory of the system can be confined to the integral sliding surface within finite time, and the stability of closed-loop system under cyber-attack will be guaranteed by using a set of linear matrix inequalities. Following a standard procedure of universal fuzzy ISMC, it is shown that all signals in the closed-loop system will be guaranteed bounded, and the states are asymptotic stochastic stable if some conditions are met. An inverted pendulum is applied to show the effectiveness of our control scheme.

In this article, the decentralized adaptive secure control problem for cyber-physical systems (CPSs) against deception attacks is investigated. The CPSs are formed as a type of nonlinear interconnected strict-feedback systems with uncertain time-varying parameters. The attack affects the information transmission between sensor and actuator in a multiplicative manner. A novel decentralized adaptive backstepping secure control strategy is established by exploiting a particular kind of Nussbaum functions and a flat-zone Lyapunov function analysis approach. It is shown that all of closed-loop signals remain globally bounded, and each output signal eventually converges into a small neighborhood of the origin. Simulation results on an illustrative example are provided to display the effectiveness of the proposed control scheme.

Uncertainty estimation in real-world scenarios is challenged by complexities arising from peaking phenomena and measurement noises. This article introduces a novel scheme for practical uncertainty estimation to mitigate peaking dynamics and enhance overall dynamic behavior. A fusion estimation framework for lumped uncertainties using multiple extended state observers (ESOs) is constructed, and the low-frequency adaptive parameter learning technique is employed to approximate the optimal fusion. The adaptive fusion estimation not only attenuates transient peaks in uncertainty estimation but also attains fast convergence and high accuracy under the high-gain scheduling of ESOs. Furthermore, the robustness of uncertainty estimation against measurement noises is enhanced by cascading filters in the proposed adaptive fusion framework for multiple ESOs. Extensive theoretical analyses are executed to verify practical applicability in peak and noise rejection. Finally, simulations and experiments on the wheel velocity system of a mobile robot are conducted to test the validity and feasibility.

This paper investigates the adaptive control for a class of uncertain nonlinear systems under denial-of-service (DoS) attacks. We analyze the closed-loop system stability under DoS attacks in terms of attack duration, attack frequency and resting time duration respectively. Three scenarios of DoS attacks against the system stability are considered. Firstly, it is shown that if the duration of each attack is less than a given constant, asymptotical convergence of system output is still preserved. Secondly, if the bounds on the frequency and duration of attacks with respect to overall intervals meet certain conditions, the proposed event-triggered control scheme guarantees that all the closed-loop signals are globally bounded and the stabilization error converges to a ball with a radius arbitrarily small. Thirdly, if resting time duration meets certain conditions after an arbitrarily long attack, closed-loop boundedness is still preserved. Simulation results are shown to illustrate the effectiveness of the proposed control schemes.

Addressing for the consensus control of multi-agent system (MAS) under the conditions that the directed communication topologies are switching and system nonlinearities are completely unknown, a global consensus control with fully distributed manner is proposed combining with fuzzy logic systems (FLS). FLS are used to approximate the unknown disturbances to enhance system robustness. To deal with the switching topologies of MAS, a reconstructing mechanism using a novel piecewise differentiable function is firstly proposed for the state and consensus errors of agents, which renders the state and consensus errors zero at each switching time instant and facilitates to the control design based on barrier functions, against the unexpected controller action at switching time instant. Incorporating with this reconstructing mechanism, a novel distributed controllers is designed, which is featured with low-complexity structure, fully distributed manner and adaptively reconstructing ability. These technical attempts contribute to some new results. Firstly, the global consensus of MAS with switching directed topologies and unknown nonlinearities is firstly achieved. Secondly, the consensus errors can be explicitly regulated to a prespecified arbitrary small residual set in the sense that the range of the set is the predefined functions given by designer. Finally, simulation results demonstrate the claims.

Model structure representation and fast estimation of perturbations are two key research aspects in adaptive control. This work proposes a composite local learning adaptive control framework, which possesses fast and flexible approximation to system uncertainties and meanwhile smoothens control inputs. Local learning, which is a nonparametric regression approach, is able to automatically adjust the structure of approximator based on data distribution from the local region, but it is sensitive to the outliers and measurement noises. To tackle this problem, the regression filter technique is employed to attenuate the adverse effect of noises by smoothing the output response and state features. In addition, the stable integral adaptation is integrated into local learning framework to further enhance the system robustness and smoothness of the estimation. Through the online elimination of uncertainties, the nominal control performance is recovered when the plant encounters violent perturbations. Stability analysis and numerical simulations are performed to demonstrate the effectiveness and benefits of the proposed control method. The proposed approach exhibits a promising performance in terms of rapid perturbation elimination and accurate tracking control.

This work investigates finite-time tracking control for a multi-input–multi-output plant with multisource uncertainties. A multivariable finite-time prescribed performance control scheme is proposed, where the norm of the tracking error vector is constrained with a prescribed bound. Due to the positiveness of the norm of the error vector, a novel error transformation is given to transform the “constrained” problem into an equivalent “unconstrained” problem. Meanwhile, the composite neural adaptive law is established to attenuate the effect of multisource uncertainties. The parametric perturbations are counteracted by neural adaptive terms. Time-varying uncertain control gains and external disturbances in the multivariable systems are compensated by adaptively estimating their bounds and applying the Lyapunov control design. To tackle the practical tracking problem, the aforementioned method is integrated into a dynamic-surface-based backstepping framework. Additionally, the practical quaternion-based attitude tracking problem is addressed, in which a quaternion-based form of error-norm constraint is constructed to express the generality and scalability of our proposed.

This article investigates the control of underactuated crane systems with unknown system parameters and nonsmooth input nonlinearities. To solve this problem, a novel filter-based adaptive control method is designed for the underactuated crane systems. First, since the backstepping control scheme cannot be directly applied to the crane systems, a group of filters is designed such that the crane systems become a class of strict-feedback nonlinear systems in each step of backstepping. Then, in order to ensure that the swing angle and position error converge to the origin, the variable transformation method is adopted. The result demonstrates that the tracking errors of the underactuated crane systems could be guaranteed to converge to a ball of origin with an arbitrarily small radius. Finally, the simulation results show that the proposed scheme is effective.

This article investigates the leaderless consensus problem for uncertain high-order nonlinear multiagent systems with event-triggered communication. Under a directed graph condition, a fully distributed adaptive control strategy is presented. The main contributions are summarized as follows: 1) globally Lipschitz condition, as required in many existing literatures, is not needed in this article; 2) for each agent, only one filtered output signal is required to be broadcast to its neighbors, which can effectively relieve network transmission burden; 3) the distributed adaptive controllers and event-triggered conditions are co-designed based on a single Lyapunov function such that continuous monitoring of neighbors’ states is not needed; and 4) by introducing an exponential convergence term to the designed triggering condition, Zeno behavior is avoided while all the agents’ outputs can reach asymptotically leaderless consensus. Moreover, to easily implement the designed triggering condition and further reduce the triggering frequency when the consensus errors converge to the neighborhood of origin, a switching type of triggering condition is presented and bounded consensus can be guaranteed. Simulation results are given to validate the presented distributed consensus control scheme.

In this article, the adaptive control for a class of strict-feedback nonlinear systems with uncertainties under injection and deception attacks is considered. An adaptive control scheme is proposed to deal with the injection and deception attacks meanwhile guarantee that regulation errors could be made arbitrarily small by adjusting control parameters. Compared with existing works whose models are linear or relatively simple, the model we consider in this article is nonlinear with parametric uncertainties. A new type of feedback control scheme is introduced to solve this problem. A simulation example is given to verify the effectiveness of our proposed control scheme.

In this article, we investigate the output consensus tracking problem for a class of high-order nonlinear systems with unknown parameters, uncertain external disturbances, and intermittent actuator faults. Under the directed topology conditions, a novel distributed adaptive controller is proposed. The common time-varying trajectory is allowed to be totally unknown by part of subsystems. Therefore, the assumption on the linearly parameterized trajectory signal in most literature is no longer needed. To achieve the relaxation, extra distributed parameter estimators are introduced in all subsystems. Besides, to handle the actuator faults occurring at possibly infinite times, a new adaptive compensation technique is adopted. It is shown that with the proposed scheme, all closed-loop signals are globally uniformly bounded and asymptotically output consensus tracking can be achieved.

In this article, to maximize the dimension of controllable subspace, we consider target controllability problem with maximum covered nodes set in multiplex networks. We call such an issue as maximum-cost target controllability problem. Likewise, minimum-cost target controllability problem is also introduced which is to find minimum covered node set and driver node set. To address these two issues, we first transform them into a minimum-cost maximum-flow problem based on graph theory. Then an algorithm named target minimum-cost maximum-flow (TMM) is proposed. It is shown that the proposed TMM ensures the target nodes in multiplex networks to be controlled with the minimum number of inputs as well as the maximum (minimum) number of covered nodes. Simulation results on Erdős-Rényi (ER-ER) networks, scale-free (SF-SF) networks, and real-life networks illustrate satisfactory performance of the TMM.

In this article, a distributed adaptive iterative learning control for a group of uncertain autonomous vehicles with a time-varying reference is presented, where the autonomous vehicles are underactuated with parametric uncertainties, the actuators are subject to faults, and the control gains are not fully known. A time-varying reference is adopted, the assumption that the trajectory of the leader is linearly parameterized with some known functions is relaxed, and the control inputs are smooth. To design distributed control scheme for each vehicle, a local compensatory variable is generated based on information collected from its neighbors. The composite energy function is used in stability analysis. It is shown that uniform convergence of consensus errors is guaranteed. An illustrative example is given to demonstrate the effectiveness of the proposed control scheme.

In this article, the event-triggered adaptive output feedback control of multivariable system under nonsmooth actuator nonlinearities, including deadzone, backlash, and hysteresis is investigated. The nonsmooth actuator nonlinearities are handled with a unified framework by modeling them with a time-varying disturbance and a control input. With an event-triggered mechanism and an one-parameter estimation approach, the communication consumption for the control of multivariable system is significantly reduced. It is shown that all signals in the closed loop system are bounded. The Zeno behavior is also avoided. The simulation results demonstrate the effectiveness of the control scheme.

The minimum cost control problem is one of the most important issues in controlling complex networks. Different from the previous works, in this paper, we consider the minimum cost control problem with selectable inputs by adopting the cost function summed over both quadratic terms of system input and system state with a weighting factor. To address such an issue, the orthonormal-constraint-based projected gradient method is proposed to determine the input matrix iteratively. Convergence of the proposed algorithm is established. Extensive simulation results are carried out to show the effectiveness of the proposed algorithm. We also investigate what kinds of nodes are most important for minimizing average control cost in directed stems/circles and small networks through simulation studies. The presented results in this paper bring meaningful physical insights in controlling the directed networks from an energy point of view.

The paper focus on its unknown trajectory concluding uncertain dynamics, sensor failures and even unanticipated actuator faults. Introducing a speed function, this work puts forward an indirect adaptive neural network control protocol which is adopted to achieve the object that the uncertain MIMO nonlinear systems tracks the unknown trajectory. We, to be specific, purposes a model to link estimated target trajectory with the actual hidden one mathematically. Similarly, we have the relationship between the predicted and the polluted. It is shown that the instantaneous behaviour of the tracking process during the main course of the system operation is improved and all the signals are uniformly ultimately bounded. The numerical simulation examples are taken advantage to expound the effectiveness of controller design scheme in this work.

In this paper, we investigate the adaptive consensus control for a class of high-order nonlinear systems with different unknown control directions where communications among the agents are represented by a directed graph. Based on backstepping technique, a fully distributed adaptive control approach is proposed without using global information of the topology. Meanwhile, a novel Nussbaum-type function is proposed to address the consensus control with unknown control directions. It is proved that boundedness of all closed-loop signals and asymptotically consensus tracking for all the agents' outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme.

In this paper, a distributed adaptive control scheme is proposed for nth order multi-agent systems with pure integrator type of subsystem dynamics. It is assumed that the information transmission condition among different subsystems is represented by a fixed, balanced and weakly connected directed graph. The full knowledge of desired trajectory is allowed totally unknown by part of the subsystems, except that its first nth derivatives are bounded. It is shown that the globally uniform boundedness of all closed-loop signals and asymptotically consensus tracking for all the subsystem outputs can be guaranteed.

The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low rank and sparse matrices, and further recast as a semidefinite programming problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low-rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers, and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered "clean" data from the proposed method can give much better parameter estimation compared with that based on the raw data.

In this paper, we consider the problem of designing distributed adaptive consensus tracking controllers for multiple nonlinear systems with unknown parameters and external disturbances. The desired trajectory is time varying given by the state of a reference system, which is only available to a portion of the group of the systems. Besides, the dynamics of the reference state is bounded but unknown to all of the systems. The communication graph characterizing the interactions among the systems is assumed to have undirected, fixed and connected topology. By introducing distributed estimators for the bound of the reference dynamics, two control schemes are proposed to address the problem. In the first scheme, a sign function is employed and perfect consensus tracking can be achieved. In the second scheme, an alternative control law is developed and the chattering phenomenon caused by the sign function can be reduced. However, new challenge will be triggered which is to compensate for possible destabilizing effects of the coupling elements relating to local parameter estimation errors and the synchronization errors of the neighbors. The overall communication graph is firstly reduced to an undirected spanning tree with single system notified of the reference state. Based on this, new synchronization error for each subsystem is then defined as the weighted distance relative to only one of its neighbors. It is shown that all the synchronization errors will converge to a prescribed bound which can be made as small as desired in this case.