Unmanned surface vehicles: An overview of developments and challenges

In this paper, we consider the trajectory tracking of a marine surface vessel in the presence of output constraints and uncertainties. An asymmetric barrier Lyapunov function is employed to cope with the output constraints. To handle the system uncertainties, we apply adaptive neural networks to approximate the unknown model parameters of a vessel. Both full state feedback control and output feedback control are proposed in this paper. The state feedback control law is designed by using the Moore–Penrose pseudoinverse in case that all states are known, and the output feedback control is designed using a high-gain observer. Under the proposed method the controller is able to achieve the constrained output. Meanwhile, the signals of the closed loop system are semiglobally uniformly bounded. Finally, numerical simulations are carried out to verify the feasibility of the proposed controller.

Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function

This brief addresses the trajectory tracking control problem of a fully actuated surface vessel subjected to asymmetrically constrained input and output. The controller design process is based on the backstepping technique. An asymmetric time-varying barrier Lyapunov function is proposed to address the output constraint. To overcome the difficulty of nondifferentiable input saturation, a smooth hyperbolic tangent function is employed to approximate the asymmetric saturation function. A Nussbaum function is introduced to compensate for the saturation approximation and ensure the system stability. The command filters and auxiliary systems are integrated with the control law to avoid the complicated calculation of the derivative of the virtual control in backstepping. In addition, the bounds of uncertainties and disturbances are estimated and compensated with an adaptive algorithm. With the proposed control, the constraints will never be violated during operation, and all system states are bounded. Simulation results and comparisons with standard method illustrate the effectiveness and advantages of the proposed controller.

Adaptive Trajectory Tracking Control of a Fully Actuated Surface Vessel With Asymmetrically Constrained Input and Output

An underactuated mechanical system (UMS) is a system which has fewer independent control actuators than degrees of freedom to be controlled. Control of UMS is considered as one of the most active fields of research because of its diverse engineering applications. This survey reviews UMS from its history to the state-of-the-art research on modelling, classification, control, and to some extent, provides some unique insights for bottleneck issues and future research directions.

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A Survey of Underactuated Mechanical Systems

In this brief, we investigate the control problem of tracking a desired trajectory for a fully actuated marine surface vessel considering multiple outputs constraints. To prevent multiple output constraints violation, a symmetric barrier Lyapunov function (SBLF) is employed. Backstepping, in combination with adaptive feedback approximation techniques, is introduced to design an adaptive neural network control. Experimental simulations are provided to evaluate the feasibility and effectiveness of the proposed controller. Compared to the adaptive neural network control without multiple output constraints, the proposed adaptive neural network using the SBLF can guarantee that all the outputs remain bounded.

Adaptive Neural Network Control of a Fully Actuated Marine Surface Vessel With Multiple Output Constraints

This study concerns the robust tracking control problem for an underactuated surface vessel with parameter uncertainties. In recent work by McNinch and collegeaues, a sliding mode control (SMC) law is presented and experimentally implemented for trajectory tracking of an underactuated autonomous surface vessel. The authors believe, however, their theory formulation involves essential flaws. Motivated by the obtained result by McNinch and collegeaues, a new revised SMC law is proposed under the same assumptions. The control law is similarly developed by introducing a first-order sliding surface in terms of surge tracking errors and a second-order surface in terms of lateral motion tracking errors; however, the desired surge and sway velocities are presented as a new form to guarantee the convergence of position tracking errors. Simulation results are provided to validate their method.

Sliding mode tracking control of an underactuated surface vessel

Non-fragile H∞ consensus tracking of nonlinear multi-agent systems with switching topologies and transmission delay via sampled-data control

Non-fragile consensus control for singular multi-agent systems with Lipschitz nonlinear dynamics

This paper investigates the tracking consistency regulation of high-order multi-agent systems (MASs) subject to Lipschitz nonlinear perturbations. Rooting at the leader node, the interagent interaction is described by a directed graph that contains a directed spanning tree. On designing the tracking protocol, an aperiodic data sampling scheme is introduced such that in-neighbors’ information can be aperiodically sampled and transmitted via broadcast channels, where packet loss and communication delay are unavoidably existent. Thanks to algebraic graph theory and Lyapunov-Krasovskii functional technique, the concerned consensus tracking issue is cast into asymptotically stabilizing a class of switched nonlinear systems which consist of delayed and non-delayed components. It is proved that, moreover, the asymptotic stabilization of such systems essentially depends on packet loss characteristics. By explicitly characterizing the interplay among sampling interval, communication delay and network structure, consensualization criteria are formulated within the linear matrix inequality (LMI) framework. The design method is validated by numerical simulations.

Consensus Tracking of Data-Sampled Nonlinear Multi-Agent Systems With Packet Loss and Communication Delay

Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve the sampling efficiency and decrease the required interactions during the learning process, this paper develops a data-efficient learning variable impedance control method that enables the industrial robots automatically learn to control the contact force in the unstructured environment. To this end, a Gaussian process model is learned as a faithful proxy of the system, which is then used to predict long-term state evolution for internal simulation, allowing for efficient strategy updates. The effects of model bias are reduced effectively by incorporating model uncertainty into long-term planning. Then the impedance profiles are regulated online according to the learned humanlike impedance strategy. In this way, the flexibility and adaptivity of the system could be enhanced. Both simulated and experimental tests have been performed on an industrial manipulator to verify the performance of the proposed method.

Guihua Xia

Papers

Sliding mode tracking control of an underactuated surface vessel

Non-fragile H∞ consensus tracking of nonlinear multi-agent systems with switching topologies and transmission delay via sampled-data control

Non-fragile consensus control for singular multi-agent systems with Lipschitz nonlinear dynamics

Consensus Tracking of Data-Sampled Nonlinear Multi-Agent Systems With Packet Loss and Communication Delay

Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control.