This paper develops a novel integral sliding mode controller (ISMC) for general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured velocities, unknown disturbances, uncertain hydrodynamics robot have been successfully solved in control design. An adaptive MIMO-ESO is designed not only to estimate unmeasurable linear angular but also external disturbances. ISMC then using Lyapunov synthesis,...

In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered our design. Based on dynamics derived discrete-time domain, two neural networks (NNs), including critic an action NN, integrated into adaptive The NN is introduced to evaluate long-time performance of designed current time step, used compensate unknown...

This paper proposes an enhanced robot skill learning system considering both motion generation and trajectory tracking. During demonstrations, dynamic movement primitives (DMPs) are used to model robotic motion. Each DMP consists of a set systems that enhances the stability generated toward goal. A Gaussian mixture regression integrated improve performance DMP, such more features can be extracted from multiple demonstrations. The learned scaled in space time. Besides, neural-network-based...

In this paper, automatic motion control is investigated for wheeled inverted pendulum (WIP) models, which have been widely applied modeling of a large range two modern vehicles. First, the underactuated WIP model decomposed into fully actuated second-order subsystem Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> consisting planar movement vehicle forward and yaw angular motions, passive (nonactuated) first-order...

Autonomous underwater vehicles (AUVs) have been widely employed in ocean survey, monitoring, and search rescue tasks for both civil military applications. It is beneficial to use multiple AUVs that perform environmental sampling sensing the purposes of efficiency cost effectiveness. In this paper, an adaptive path planning algorithm proposed estimate scalar field over a region interest. method, measurable model composed basis functions defined represent field. A selective function Kalman...

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In this paper, an adaptive trajectory tracking control algorithm for underactuated unmanned surface vessels (USVs) with guaranteed transient performance is proposed. To meet the realistic dynamical model of USVs, we consider that mass and damping matrices are not diagonal input saturation problem. Neural networks (NNs) employed to approximate unknown external disturbances uncertain hydrodynamics USVs. Moreover, both full-state feedback output presented, unmeasurable velocities controller...

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment be interacted is modeled as a linear system. the presence of dynamics environments, observer in robot joint space employed estimate interaction torque, and control adopted regulate behavior at points. An adaptive neural controller using radial basis function guarantee trajectory tracking. A cost that defines performance torque regulation tracking minimized by...

This study is concerned with the synchronised tracking control for multiple agents high-order dynamics, whereas desired trajectory only available a portion of team members. Using weighted average neighbours' states as reference signal, adaptive neural network (NN) designed each agent in both full-state and output feedback cases. It proved that NN law guarantees error converges to an adjustable neighbourhood origin cases although some them do not access directly. Two simulation examples are...

In this paper, the problem of near-optimal motion planning for vehicles with nonlinear dynamics in a clustered environment is considered. Based on rapidly exploring random trees (RRT), we propose an incremental sampling-based algorithm, i.e., RRT (NoD-RRT). This algorithm aims to solve problems kinodynamic constraints. To achieve cost/metric between two given states considering constraints, neural network utilized predict cost function. On basis, new reconstruction method search tree...

Summary In this paper, an actuator robust fault‐tolerant control is proposed for ocean surface vessels with parametric uncertainties and unknown disturbances. Using the backstepping technique Lyapunov synthesis method, adaptive tracking first developed by incorporating configuration matrix considering saturation constraints. The changeable caused rotatable propulsion devices considered. Next, case when faults occur in of vessel. Rigorous stability analysis carried out to show that can...

This article presents a modified line-of-sight (LOS) guidance law and an adaptive neural network (NN) controller for underactuated marine vehicles in the presence of uncertainties constraints. Unlike conventional LOS guidance, proposed counteracts drift caused by external disturbances to maintain zero cross-track error. Furthermore, NN is designed using barrier Lyapunov function (BLF) deal with system constraints affecting unknown vehicle dynamics. The stability analysis guarantees uniform...

This paper presents a cooperative multiagent search algorithm to solve the problem of searching for target on 2-D plane under multiple constraints. A Bayesian framework is used update local probability density functions (PDFs) when agents obtain observation information. To global PDF decision making, sampling-based logarithmic opinion pool proposed fuse PDFs, and particle sampling approach represent continuous PDF. Then Gaussian mixture model (GMM) applied reconstitute from particles,...

This paper proposes a novel adaptive control methodology based on the admittance model for multiple manipulators transporting rigid object cooperatively along predefined desired trajectory. First, an is creatively applied to generate reference trajectory online each manipulator according path of object, which input controller. Then, innovative integral barrier Lyapunov function utilized tackle constraints due physical and environmental limits. Adaptive neural networks (NNs) are also employed...

In this paper, an integral reinforcement learning-based adaptive neural network (NN) tracking control is developed for the continuous-time (CT) nonlinear system with unknown directions. The long-term performance index in CT domain prescribed. Critic and action NNs are designed to approximate unavailable dynamics, respectively. signal explicitly embedded updated law of NN then estimated can be minimized. Rigorous theoretical analysis provided show that closed-loop stabilized all signals...

SUMMARY This paper investigates task allocation for multiple robots by applying the game theory-based negotiation approach. Based on initial using a contract net-based approach, new method to select and construct set is proposed employing utility functions. A mechanism suitable decentralized also presented. Then, strategy achieve Pareto-optimal solution reallocation. Extensive simulation results are provided show that solutions after better than allocation. In addition, experimental further...

In this paper, event-triggered reinforcement learning-based adaptive tracking control is developed for the continuous-time nonlinear system with unknown dynamics and external disturbances. The critic action neural networks are designed to approximate an long-term performance index controller, respectively. dead-zone condition reduce communication computational costs. Rigorous theoretical analysis provided show that closed-loop can be stabilized. weight errors filtered error all uniformly...