Autonomous Underwater Vehicle Dynamic Model Research Articles

Autonomous Underwater Vehicle Positioning Control - a Velocity Form LPV-MPC Approach*

In this work, the positioning control of an autonomous underwater vehicle (AUV) is considered for docking operations in the presence of varying tidal currents. The AUV model is described by a dynamic model and a kinetic model, both are linear parameter varying (LPV). A velocity form LPV model predictive control (LPV-MPC) scheme is proposed, in which the AUV dynamic model is used for the states and the kinetic model is used for the output. The interdependence of AUV kinematic model and dynamic model is exploited to avoid increased state dimension. The complete velocity form controller design enables the cancellation of disturbance effects through the use of the AUV's velocity vector increment for predicting the future evolution of the system. Compared to the original predictive control for the Naminow-D AUV that uses a time-varying Kalman filter for state estimation to approximate disturbances, the proposed algorithm does not require an estimator to eliminate unknown current disturbance, therefore simplifies design and implementation. Simulation studies show the merit of the proposed controller over the original Naminow-D predictive controller especially in terms of improved transient response and reduced sensitivity to time-varying external disturbances.

Current Estimation and Path Following for an Autonomous Underwater Vehicle (AUV) by Using a High-gain Observer Based on an AUV Dynamic Model

A path following problem for autonomous underwater vehicles (AUVs) under a nonuniform current is presented in this paper. A dynamic model of an AUV in a nonuniform flow was adopted to develop a high-gain observer (HGO) for estimation of the three-dimensional current velocities along AUV trajectories. The HGO was chosen as a nonlinear estimation algorithm, and the observer gain was computed by solving a Linear Matrix Inequality (LMI) which represented the estimation error dynamics. The current velocities were determined by calculating the differences between the measured absolute velocities of the vehicle and the estimated relative velocities of the vehicle estimated by the observer. The estimation error means of the HGO using the LMI have smaller values than the state observer with a gain matrix determined by the pole-placement approach. For the path following study, the desired curved path was represented by using a Serret-Frenet frame which propagated along the curve. The path-following system includes a guidance law, an update law and a proportional and integral controller. Two cases of numerical simulations were conducted to verify the performance of the path following system combined with HGO for current compensation, and the results of both cases have shown that the AUV reached and converged to the desired path.

Path Following for an Autonomous Underwater Vehicle (AUV) by Using a High-Gain Observer based on an AUV Dynamic Model

In this paper a curved path following problem for autonomous underwater vehicles (AUVs) moving in a nonuniform ocean current is presented. A high-gain observer (HGO) based on an AUV dynamic model was used to estimate three dimensional current velocities. The current velocities were determined by calculating the differences between the vehicle’s absolute velocities and the vehicle’s relative velocities estimated by the observer. The HGO was chosen as a nonlinear estimation algorithm, and the observer gain was computed by solving a Linear Matrix Inequality (LMI) which represented the estimation error dynamics. The desired curved path was represented by using a Serret-Frenet frame which propagated along the curve. The path-following system includes a guidance law, un update law and a proportional and integral controller. Two cases of numerical simulations were conducted to verify the performance of the path following system.

Estimating Water Current Velocities by Using a Model-Based High-Gain Observer for an Autonomous Underwater Vehicle

For accurate control and navigation of an autonomous underwater vehicle (AUV) it is critical to know the water current velocities around the vehicle body. The AUV-onboard acoustic doppler current profilers are unable to measure the current near to the vehicle due to their blanking distance, so an AUV model-based observer can serve the purpose of estimating the current velocities surrounding the vehicle. In this paper, a high-gain observer based on an AUV dynamics model was used to estimate 3D water current velocities. The water current velocities were determined by calculating the differences between the vehicle velocities over the ground measured by a Doppler velocity log-aided inertial navigation system and the vehicle velocities through the water estimated by the model-based observer. Modeling and field trials of a Gavia AUV were used to demonstrate the approach. Instead of deriving the roll, pitch, and yaw motions, these were directly given as simulation inputs which allowed the AUV dynamics model to be simplified to 3–degrees of freedom. This paper presents a real-time model identification algorithm to identify the nonlinear parameters of the AUV model by utilizing a recursive least squares method. The real-time model identification algorithm allows the AUV model to be continuously updated in response to the operational environment. A high-gain observer was chosen as a nonlinear estimation algorithm to obtain the vehicle velocities through the water, and the Lyapunov stability of the estimation error dynamics was investigated. The observer gain was computed by solving the linear matrix inequality which represented the error dynamics. By utilizing the observer in the AUV dynamic model, the vehicle’s velocity vector through the water was estimated, then the current velocity vector was calculated. In order to investigate the differences between the estimated current velocities and the measured current velocities, the standard deviations between these two were quantified. The results showed that the current estimation found by using the model-based observer was improved compared with the previous water current estimation method, which found the water velocity components in a turbulent water column from the AUV motion response.

A regressor-free robust adaptive controller for autonomous underwater vehicles

This article focuses on the motion control of autonomous underwater vehicles in the ocean environment by a robust adaptive controller in which there is no regressor matrix. Due to the different atmospheric conditions in the ocean environment, the hydrodynamic coefficients of autonomous underwater vehicles cannot be exactly available and there are many uncertainties in the dynamic model. This prevents the traditional controllers to overcome these difficulties immediately. Hence, developing the adaptive controllers for the autonomous underwater vehicles encountered with uncertainties is required to provide appropriate performance. In the conventional adaptive control system, it is assumed that the autonomous underwater vehicle dynamic model can be linearly written into regressor form. Since the dynamics of the underwater vehicles is very complex, the derivation of the regressor matrix is very tedious. To overcome these problems, a regressor-free adaptive controller is proposed for the autonomous underwater vehicles in the general form of the equations of motion. In this approach, the controller is derived by the inverse dynamic method. Also, by utilizing known basis functions weighted by constant unknown coefficients, the uncertainties of the control law are estimated. The adaptation laws are derived based on Lyapunov stability theorem. The validity of the proposed method is verified by some simulation experiments. The simulation results show that the proposed approach can improve the robustness of adaptive controller to the dynamic model uncertainties and the external disturbance.

H∞ control for path tracking of autonomous underwater vehicle motion

In order to simplify the design of path tracking controller and solve the problem relating to nonlinear dynamic model of autonomous underwater vehicle motion planning, feedback linearization method is first adopted to transform the nonlinear dynamic model into an equivalent pseudo-linear dynamic model in horizontal coordinates. Then considering wave disturbance effect, mixed-sensitivity method of H∞ robust control is applied to design state-feedback controller for this equivalent dynamic model. Finally, control law of pseudo-linear dynamic model is transformed into state (surge velocity and yaw angular rate) tracking control law of nonlinear dynamic model through inverse coordinate transformation. Simulation indicates that autonomous underwater vehicle path tracking is successfully implemented with this proposed method, and the influence of parameter variation in autonomous underwater vehicle dynamic model on its tracking performance is reduced by H∞ controller. All the results show that the method proposed in this article is effective and feasible.

General path-following manoeuvres for an underwater vehicle using robust control synthesis

The current paper presents the robust autopilot controller design of an autonomous underwater vehicle (AUV) for path-tracking manoeuvres. First, an AUV dynamic model is established without external disturbances. Next, the control synthesis includes the H∞ loop-shaping design, incorporating co-prime factorization perturbations. Then the autopilot controller is reduced to a reasonable order. In addition, the resulting control system has been evaluated extensively in both the frequency and the time domains. Finally, it is found that the proposed vehicle system provides good manoeuvrability over a wide range of operating conditions, even under model uncertainties.