Search

In this paper, the distributed coordination control of path tracking and nash equilibrium seeking of networked automated ground vehicles systems with unknown dynamics is investigated under the framework of graphical games. Different from existing works assuming that the vehicle dynamics are known, each vehicle with completely unknown system dynamics is considered in this paper. To solve this problem, a learning-based data-driven technique is proposed to identify and reconstruct the unknown system matrices. Then, based on the identified system matrices, an offline reinforcement learning (RL) algorithm is proposed to derive both the optimal control policies and the policy iteration solution for graphical games, as well as its corresponding convergence is analyzed. Besides, an online learning algorithm only relying on the online information of states and inputs in an online way is developed to solve the optimal path tracking control problem. As a result, the requirement of relying on the vehicle’s dynamics in the traditional tracking control protocols is completely relaxed by our proposed method. The optimal distributed control policies found by the proposed RL algorithm satisfies the global Nash equilibrium and synchronizes all tracked vehicles to the pinning vehicle. Numerical simulation results are provided to show the effectiveness of the theoretical analysis.

In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream of actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multirobot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment’s skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach’s feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.

This article considers the distributed leader–follower formation tracking control problem for a networked linear multiagent system (MAS) consisting of one nonautonomous leader and multiple homogeneous followers in a sample-data-based edge-event-triggering communication setting. A novel edge-state-estimate-based triggering function along with a triggering rule is proposed for each communication edge to regulate and reduce unnecessary data transmission. A new distributed formation tracking protocol is then developed for each follower based on only event-generator-regulated information. The formation tracking problem is reformulated as a stability analysis problem of a delayed system by defining the formation error dynamics. Lyapunov-based methods and linear matrix inequality (LMI) techniques are utilized to derive sufficient conditions for codesigning the event-generator and controller gains that ensure the asymptotic and exponential convergence of the closed-loop formation error dynamics. Numerical simulations and experimental implementations were carried out using a group of four unicycle-type mobile robots to demonstrate and validate the effectiveness of the proposed method.

This paper considers the distributed optimal path tracking control problem of autonomous ground vehicles (AGVs) with matched uncertainties under denial-of-service (DoS) attacks. First, to solve the uncertain system parameters, an inverse reinforcement learning (RL) technique is PROPOSED to seek the optimal controller instead of directly solving the robust control problem, which can ensure that the designed controller is globally optimal with respect to the proposal performance function. An off-policy RL algorithm is further proposed to learn the optimal control solution online without requiring any knowledge of the vehicle dynamics. Then, to lower the communication load among vehicles, a perception-based optimal resilient controller and its corresponding perception-based static deadband condition are developed, respectively. Moreover, the perception-based dynamic deadband control mechanism is further exploited to consider the secure path tracking control problem. The proposed algorithms verify that the RL-based optimal path tracking control can be solved, and the occurrence of the Zeno phenomenon can be avoided. Finally, a numerical simulation is demonstrated to verify the effectiveness of the proposed approach.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

This article studies the finite-time output-feedback cooperative formation problem of multiple marine surface vessels (MSVs) without using velocity information, where each MSV contains unknown time-varying actuator faults, ocean disturbances, and model uncertainties. Considering the performance specifications and requirements in practice, the formation errors and velocities are also required to change in a predefined compact set. Aiming at the challenge of unknown input gain caused by unknown time-varying multiplicative faults, a novel nonlinear extended state observer with an adaptive law is first constructed, which cannot only recover unmeasurable velocities from position-heading information but also simultaneously compensate for ocean disturbances, model uncertainties, and additive faults. Subsequently, combining the barrier Lyapunov function and the nonlinear tracking differentiator technique, a unified finite-time output-feedback cooperative control framework is developed, including both kinematics and kinetics, to prevent constraint deviation and avoid the computational complexity of the traditional backstepping method. Using finite-time stability theory, the designed output feedback cooperative controller can ensure that the desired cooperative performance is achieved within a finite time and tracking errors converge to a small neighborhood of the origin while ensuring that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation examples are shown to demonstrate the effectiveness of the proposed control scheme.

Teleoperation of multiple agents has the unique advantage to complete tasks with wide range and is an effective solution to help agents avoid obstacles with human intelligence, especially when encounters the local-minima problem. In this article, a novel nonlinear single-master–multislave (SMMS) teleoperation control framework is proposed for multiple mobile agents to achieve obstacles avoidance under delays, nonlinearities, various uncertainties, and nonholonomic constraints. Namely, the slave trajectory planner is designed to cope with the nonholonomic constraints caused by underactuated characteristics of slave agents, while the slave obstacle avoidance planner is designed to cope with obstacles in the environment, which can avoid the obstacles by artificial potential function (APF)-based obstacle avoidance algorithm. Particularly, considering that the APF usually encounters the local-minima problem, a leader selection algorithm is designed for the slave obstacle avoidance planner and a virtual force feedback is designed for the master subsystem, where the slave agents can get rid of local-minima points while teleoperated by human operator with confident force feedback. The global stability of the overall system can be guaranteed under the proposed radial basis function neural network (RBFNN)-based adaptive sliding mode master controller and slave formation controller under delays, nonlinearities and various uncertainties. The comparative experiment is implemented, and the results show the effectiveness of proposed control framework in the achievement of good performance including position tracking, force feedback, and formation and obstacles avoidance while the stability is guaranteed.

In this paper, a framework is developed for three dimensional (3D) object recognition, localization, and anthropomorphic manipulation using YOLOv5, two low-cost webcams, and a Franka Emika (FE) Panda manipulator. Autonomous pick-and-place tasks are executed in a human-like manner using impedance control and novel difference-based Dynamic Movement Primitives (DMP) trajectory planning. The high accuracy vision system is developed with two webcams by using triangular relation to determine the object's position. The difference-based DMP algorithm and task execution plan are used to learn unique trajectories for different objects based on human demonstration and the object's coordinates. By integrating these components together, experimental studies on pick-and-place tasks are successfully performed for two different objects.

This paper investigates the resilient distributed secure output path following control problem of heterogeneous autonomous ground vehicles (AGVs) subject to cyber attacks based on reinforcement learning algorithm. Most existing results are subject to the same attack models for all communication channels, however multiple channels launched by different attackers are considered in this paper. First, a predictor-acknowledgement clock algorithm for each vehicle is proposed to judge whether the communication channel among neighboring vehicles is attacked or not by receiving or transmitting an acknowledgement. Then, a resilient distributed predictor is proposed to predict the pinning vehicle’s state for each vehicle. In addition, a resilient local control protocol consisting of the feedforward state provided by the predictor and the local feedback state of each vehicle is developed for the output path following problem, which is further converted to the optimal control problem by designing a discounted performance function. Discounted algebraic Riccati equations (AREs) are derived to address the optimal control problem. An off-policy reinforcement learning (RL) algorithm is put forward to learn the solution of discounted AREs online without any prior knowledge of vehicles’ dynamics. It is shown that the RL-based output path following control problem of AGVs imposed by cyber attacks can be achieved in an optimal manner. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

In this article, we focus on the position synchronization of nonlinear multimanipulator systems. For robot manipulators that are only equipped with joint position measurement devices with measurement noises, a continuous-discrete adaptive unscented Kalman filter (CD-AUKF) is implemented to acquire smoother manipulator position states, and, meanwhile, estimate high-order states (e.g., velocity and acceleration). However, in closed-loop control of networked multimanipulator systems, using estimated states may result in a drastic increase in tracking errors. This shortcoming is addressed by an energy index-based neighbor selection policy (NSP). To maintain a higher tracking performance, the NSP allows each agent to actively select well-performing neighbors to interact with, while the poor-quality neighbor data is discarded. Finally, to regulate the multimanipulator system in the presence of parametric uncertainties, friction, disturbances, time-varying network delays, and packet loss, an adaptive nonsingular terminal sliding-mode (ANTSM) controller is designed. A group of Phantom Omni robotic devices were used to carry out numerical simulations and experimental studies that demonstrate the effectiveness of the proposed ANTSM control method, CD-AUKF estimation, and active neighbor-selection policy.

Grain quality is a complex trait in rice, compared with other staple crops as it is predominantly consumed as a whole grain. Although considered secondary to yield, to align with consumer preferences, breeders are increasingly interested in quality. At the early stages of a breeding program, grain quality‐related traits are often ignored as they are arduous and time‐consuming. Near‐infrared spectroscopy (NIRS) could be a suitable high‐throughput alternative to conventional wet chemistry and image processing‐related methods to be adopted for early screening. This study aims to quantify traits essential for rice breeders such as amylose, chalkiness, length, width, and the length/width ratio in rice samples with NIRS. We used conventional algorithms such as principal component analysis (PCA), partial least square regression (PLSR), multilayer perceptron (MLP), support vector classification (SVC), and linear discriminant analysis (LDA) to compare with the proposed convolutional neural network (CNN) for regression and classification. Our results showed that the proposed CNN outperformed the conventional models in estimating all traits. Unlike conventional models, CNN models could be developed with raw spectra with minimal to no preprocessing, and along with the transfer‐learning capabilities, the time required for model development could be significantly reduced. We recommend NIRS for quantitative estimation of amylose and chalkiness in rice and rather use classification/categorized estimation for other physical dimension‐related traits such as length and length/width ratio. We found NIRS to be an appropriate alternative to wet chemistry and image‐based methods for screening lines at the early stages of the breeding program. Estimation of physical parameters such as length and length/width ratio with NIRS is novel and appears reasonable for high‐throughput applications.

In this paper, a novel online neighbor selection policy is proposed in the control of nonlinear networked multi-manipulator systems where manipulators’ joints’ signals are subject to varying noise levels. By addressing the issue in many conventional control methods of multi-agent systems (MASs) where all available neighbor signals are used without evaluating the quality of the information, efforts of this paper seek to improve the overall tracking performance by actively selecting neighbor feedback signals in the robust non-singular terminal sliding mode (NTSM) control. A fast neighbor selection scheme is presented by incorporating an online noise covariance estimation into a nonlinear continuous-discrete unscented Kalman filter (CD-UKF). A selection index vector is recursively updated by the estimated noise covariance matrix for the control design. Simulation results of a group of six degrees of freedom (with three actuated joints) Phantom Omni models demonstrate the effectiveness of the online neighbor selection approach and compare it to previous work which does not actively select neighbor candidates.

Haptic systems in surgical training applications require highly accurate force feedback. However, high-resolution models of the virtual environment (VE) can be very computationally intensive, lowering the force feedback update rate. The objective of this work is to improve transparency by developing a predictor that approximates the complex nonlinear VE as a linear VE with a much higher update rate. By using feedback from the more accurate but slower VE, the predictor can provide increased transparency to the operator. The full control design of the predictor and haptic controller is considered for a nonlinear haptic device. The predictor is designed using Lyapunov-based methods, by numerical solution of a linear matrix inequality. The predictor uses a projection-type adaptation law to estimate the unknown VE parameters. Simulation results are shown to demonstrate the effectiveness of the method assuming unknown and time-varying VE parameters.

A new fractional interpolation-based smoothing scheme is proposed for variable structure control. Comparing with the conventional fractional interpolation scheme, as well as other similar smoothing schemes such as the hyperbolic tangent and the exponential smoothing schemes, the new scheme achieves the designated tracking precision bound with an adequate and yet moderate gain. Comparing with the well known saturation scheme, the new scheme achieves a smoother control profile, and possesses extra flexibility in adjusting the equivalent control gain while retaining the same precision bound. Substantial comparisons are conducted to confirm the effectiveness of the proposed smoothing control scheme.

A consensus-based formation control for a class of networked multiple mobile robots is investigated with a virtual leader approach. A novel distributed control algorithm is designed based on the Lyapunov method and linear matrix inequality (LMI) technique for time delay systems. A multiple Lyapunov Krasovskii functional candidate is proposed for investigating the sufficient conditions to linear control gain design for the system with constant time delays. Simulation results as well as experimental studies on Pioneer 3 series mobile robots are shown to verify the effectiveness of the proposed approach.