Search

For a class of remotely operated vehicle (ROV) systems with thruster constraints, immeasurable states, and unknown nonlinearities, the trajectory tracking control problem was discussed in this paper. The unknown nonlinear functions were approximated by radial basis function (RBF) neural networks. An adaptive state observer based on neural networks was designed and the immeasurable states were estimated. Considering the problem of thruster saturation constraints, an auxiliary system for saturation compensation was designed and a saturation factor was constructed by the auxiliary system state. By applying the backstepping design method, an adaptive neural sliding mode trajectory tracking controller was developed, in which the saturation factor is contained in adaptive laws. It was proved that the uniformly ultimately bounded (UUB) of trajectory tracking errors can be obtained. Finally, the effectiveness of the proposed trajectory tracking control approach was checked by simulations. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of adaptive trajectory tracking control for remotely operated vehicles (ROVs). Considering thruster dynamics, a third-order state space equation is used to describe the dynamic model of ROVs. For the problem of unknown dynamics and partially known input gain, an adaptive sliding mode control design scheme based on RBF neural networks is developed using a backstepping design technique. Because of the saturation constraints of the thrusters, a first-order auxiliary state system is applied, and subsequently, a saturation factor is constructed for designing adaptive laws to ensure the stability of the adaptive trajectory tracking system when the thrusters are saturated. The proposed controller guaranteed that trajectory tracking errors are uniformly ultimately bounded (UUD). Finally, the effectiveness of the proposed controller is verified by simulations. • The thruster dynamics and saturation constraints were considered in controller design. • A third-order state space equation was constructed to describe the dynamic of ROVs. • The unknown dynamics and partially known gain were considered in controller design. • A saturation factor was constructed for the design of the adaptive laws. [ABSTRACT FROM AUTHOR]

This study focuses on the fault reconstruction for a class of second-order multi-input and multi-output (MIMO) nonlinear systems with uncertainties. An innovative design scheme of terminal sliding mode observer (TSMO) is presented for which the relative degree of the system is two. In comparison with the common sliding mode observer (SMO), the proposed TSMO can converge all state estimation errors to zero in finite time, even when some states cannot be measured directly. Given that state estimation errors converge to zero in finite time, a fault reconstruction method based on an equivalent output error injection concept and a SMO-based fault isolation strategy are presented, so that the fault information after isolating disturbances can be accurately known. Simulation examples of fault reconstruction on a small unmanned underwater vehicle are presented to demonstrate the effectiveness of the proposed method. • A novel TSMO is presented for a class of high-order uncertain nonlinear systems. • All state estimation errors converge to zero in a finite time. • A fault reconstruction strategy is developed to isolate uncertainties. • The assumptions for the uncertainties are relaxed in the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive control (MPC) method based on quantum-behaved particle swarm optimization (QPSO), combining with sliding mode control (SMC), is studied to solve the dynamic trajectory tracking problem of Unmanned Underwater Vehicle (UUV) in three-dimensional underwater environment. First, the kinematic controller based on QPSO-MPC is designed to obtain the velocity signal. QPSO limits the velocity of UUV within a specified range to handle the speed jump problem when compared with backstepping control. Then kinematic control is extended to dynamic control for verifying the feasibility of QPSO-MPC in the realistic dynamical UUV system. Here SMC is adopted in dynamic control because of its good robustness and anti-interference performance. The combination of kinematic and dynamic controllers can efficiently realize the trajectory tracking of UUV. The experimental simulation results prove that the algorithm proposed in this paper can solve the speed jump and the driven saturation caused by speed jump, and also can satisfy the propeller constraints of UUV. [ABSTRACT FROM AUTHOR]

This paper develops a novel adaptive fuzzy sliding mode controller for the diving control of autonomous underwater vehicle (AUV). Unlike most previous AUVs’ control approaches, in this paper, the problem of input saturation constraint of rudder angle is considered, and the new auxiliary systems are proposed. Considering the modeling uncertainty of the dynamic model, an adaptive sliding mode controller based on fuzzy logic system is proposed, and the new update laws of control parameters are given. For the adaptive sliding mode controller design, although the input gain of each subsystem is partially known, only one fuzzy logic system is needed for online identification, which is a distinct difference from the previous adaptive sliding mode controllers. The uniformly ultimately boundedness of tracking error is proven by Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulation. [ABSTRACT FROM AUTHOR]

This paper focuses on depth trajectory tracking control for a Remotely Operated Vehicle (ROV) with dead-zone nonlinearity and saturation nonlinearity of thruster; an adaptive sliding mode control method based on neural network is proposed. Through the analysis of dead-zone nonlinearity and saturation nonlinearity of thruster, the depth trajectory tracking control system model of a ROV which uses thruster control signals as system input has been established. According to the principle of sliding mode control, an adaptive sliding mode depth trajectory tracking controller is built by using three-layer feed-forward neural network for online identification of unknown items. The selection method and update laws of the control parameters are also given. The uniform ultimate boundedness of trajectory tracking error is analysed by Lyapunov theorem. Finally, the effectiveness of the proposed method is illustrated by simulations. [ABSTRACT FROM PUBLISHER]

In this paper, a new approach of tracking control is investigated for the 7000-m Jiaolong manned submarine vehicle (MSV). First, the formulation of control allocation problem and some background information about tracking control of the 7000-m Jiaolong MSV are presented. Then, a kinematic controller is derived. The filter design using bioinspired model is employed to handle the speed jump problem and to make sure that each thruster is within the saturation limit. The kinematic controller is then extended to incorporate a sliding-mode control technique to complete the dynamic control. The system stability is guaranteed, and tracking errors asymptotically converge to zero by Lyapunov stability theory. Another interesting phenomenon is that the bioinspired method can achieve more satisfactory tracking result with a less control effort compared to a typical backstepping method. Finally, simulations illustrate the performance of the derived cascaded tracking control technique. [ABSTRACT FROM PUBLISHER]

A biologically inspired neurodynamics-based tracking controller of underactuated Autonomous Underwater Vehicles (AUV) is proposed in this paper. The proposed control strategy includes a velocity controller with biological neurons and an adaptive sliding mode controller. The biological neurons are embedded into the backstepping velocity controller to eliminate the sharp speed jumps commonly existing in vehicles due to tracking errors changing suddenly. The outputs of the velocity controller are used as the command inputs of the sliding mode controller, and the thruster control constraints problems that are commonly seen in the backstepping control of AUV are solved by the proposed controller. Simulation results show that the control strategy achieved success in smoothly tracking AUV position and velocity. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper a novel hybrid control strategy is developed for trajectory tracking control of unmanned underwater vehicle (UUV). The proposed hybrid control strategy consists of two subsystems: a virtual velocity controller and a sliding-mode controller. The tracking errors are shown to asymptotically converge to zero by Lyapunov stability theory using the new approach, whereas in the traditional backstepping method, speed jump occurs if the tracking error changes suddenly. The biologically inspired model is designed to smooth the virtual velocity controller output, avoid speed jumps of underwater vehicles and satisfy the thruster control constraint. The effectiveness and efficiency of the proposed control strategy are demonstrated through simulations and comparison studies. [Copyright &y& Elsevier]