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Depth Control of Autonomous Underwater Vehicles Based on Constrained Model Predictive Control
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This paper studies the trajectory tracking problem of an underactuated autonomous underwater vehicle (AUV) under the uncertainty of model parameters and input delay. Different from previous algorithms, a novel control algorithm is proposed which is a combination of RBF neural network algorithm and state prediction using backstepping sliding mode control method. The RBF neural network algorithm is used to estimate the composite interferences of model parameter uncertainties and external disturbances. Meanwhile, an appropriate virtual control law is designed for the horizontal trajectory tracking of the underactuated AUV by backstepping technique. Further, the longitudinal and heading control laws are proposed using the nonsingular fast terminal sliding mode method. Then, it is proved that the AUV velocity tracking error can converge to zero in a finite time by the Lyapunov theory. Finally, the effectiveness and robustness of the approach is illustrated by the simulation results.
In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.
The method of Lagrangian particle tracking (LPT) is extended to autonomous underwater vehicles (AUVs) that are modeled as controlled particles. Controlled LPT (CLPT) evaluates the performance of ocean models used for the navigation of AUVs by computing the differences between predicted vehicle trajectories and actual vehicle trajectories. Such difference, measured by the controlled Lagrangian prediction error (CLPE), demonstrates growth rate that is influenced by the accuracy of the ocean model and the strength of the feedback control laws used for vehicle navigation. Despite the limited accuracy and resolution of the ocean model, the error growth can be bounded by feedback control when localization service is available, which is a unique property of CLPT. Theoretical relationship among CLPE growth rate, quality of ocean models, and feedback control are established for two control strategies: a transect-following controller and a station-keeping controller that are often used by AUVs. Upper bounds for error growth are derived and verified by both simulation and experimental data collected during the operations of underwater gliders in coastal ocean.
Autonomous Underwater Vehicles (AUVs) are widespread and effective tool for oceanographic research. The main applications of AUVs are long-term missions for survey of wide area and patrolling. However the key issue for long term cruising range is energy storage. One of approaches to reduce energy consumption is using of the variable buoyancy system (VBS) on the AUV. This approach allows to compensate AUV's buoyancy and to reduce energy on hovering due to it. Two combined depth control methods based on cooperative work of propulsion system and variable buoyancy system has been presented in this paper. Estimation of the methods based on calculation of energy consumption and transient response time has been made. Comparison of ones with methods based on standalone work of propulsion system and VBS has been made. Provided methods was proved on simulation and water pool tests on variable buoyancy AUV " CH-2 " designed at Institute of Marine Technology Problems FEB RAS year ago.
This article presents a variable buoyancy system that uses cephalopod-inspired, fiber-reinforced elastomer membranes as bladders to enable model-based depth and pitch control of an autonomous underwater vehicle. The fiber reinforcement allows for a single measurement of each membrane to fully define the geometry of the membrane, regardless of the membrane’s orientation or level of inflation. This allows for precise control of each bladder’s enclosed water mass and center of mass by adjusting the differential pressure between the inside of the bladder and the vehicle’s cabin using water from the environment as the working fluid. Positioning bladders at the fore and aft of the vehicle enables control of both the vehicle’s density and center of mass while minimizing center of mass disturbances due to sloshing in and gravitational loads on the membranes. A nonlinear, adaptive, backstepping, trajectory-tracking controller is presented and proven to be asymptotically stable with Lyapunov stability analysis. The advantages of the proposed variable buoyancy system and controller are demonstrated through simulation and validated with 2 degree of freedom depth and pitch step and trajectory-tracking experiments in a large water tank. We evaluate the performance of the bladders and controller on the CephaloBot, an autonomous underwater vehicle designed and built by our group.
A novel sliding mode control design method is developed to address the robust stabilization problem for uncertain time-varying networked control systems subject to state delay, which utilises auxiliary matrices to avoid the difficulties of finding quadratic Lyapunov functions and solving the linear matrix inequalities. By embedding the original system into a higher dimensional system, simplified piecewise nonnegative functions can be used to analyze the system. Stability criteria are obtained which can be satisfied and verified easily. An optimization problem is defined based on these criteria, to obtain the sliding mode control parameters to ensure the exponential stability of the closed-loop system.
Reinforcement learning (RL) is a promising technique for designing a model-free controller by interacting with the environment. Several researchers have applied RL to autonomous underwater vehicles (AUVs) for motion control, such as trajectory tracking. However, the existing RL-based controller usually assumes that the unknown AUV dynamics keep invariant during the operation period, limiting its further application in the complex underwater environment. In this article, a novel meta-RL-based control scheme is proposed for trajectory tracking control of AUV in the presence of unknown and time-varying dynamics. To this end, we divide the tracking task for AUV with time-varying dynamics into multiple specific tasks with fixed time-varying dynamics, to which we apply meta-RL for training to distill the general control policy. The obtained control policy can transfer to the testing phase with high adaptability. Inspired by the line-of-sight (LOS) tracking rule, we formulate each specific task as a Markov decision process (MDP) with a well-designed state and reward function. Furthermore, a novel policy network with an attention module is proposed to extract the hidden information of AUV dynamics. The simulation environment with time-varying dynamics is established, and the simulation results reveal the effectiveness of our proposed method.
This article is concerned with the multiloop decentralized
fuzzy proportional–integral–derivative-like (PID-like) control problem for discrete-time Takagi–Sugeno fuzzy systems with time-varying delays under dynamical event-triggered mechanisms (ETMs). The sensors of the plant are grouped into several nodes according to their physical distribution. For resource-saving purposes, the signal transmission between each sensor node and the controller is implemented based on the dynamical ETM. Taking the node-based idea into account, a general multiloop decentralized fuzzy PID-like controller is designed with fixed integral windows to reduce the potential accumulation error. The overall decentralized fuzzy PID-like control scheme involves multiple single-loop controllers, each of which is designed to generate the local control law based on the measurements of the corresponding sensor node. These kinds of local controllers are convenient to apply in practice. Sufficient conditions are obtained under which the controlled system is exponentially stable with the prescribed
performance index. The desired controller gains are then characterized by solving an iterative optimization problem. Finally, a simulation example is presented to demonstrate the correctness and effectiveness of the proposed design procedure.
This article proposes a model predictive control (MPC)-based depth control system for the gliding motion of a gliding robotic dolphin. An injector-based buoyancy-driven mechanism is employed to achieve more precise control of net buoyancy. In the system, a novel framework of depth control is proposed on the basis of a simplified model, including a depth controller with improved MPC, a heading controller with velocity-based proportional-integral-derivative, and a sliding mode observer. Extensive simulation and experimental results demonstrate the effectiveness of the proposed control methods. In particular, a variety of slider-based experiments are also conducted to explore the performance of a movable slider in the depth control so as to better govern the gliding angle. The results obtained reveal that it is feasible to realize regular gliding angles via regulating the slider, which offers promising prospects for bio-inspired gliding robots playing a key role in ocean exploration.
This paper investigates the trajectory tracking control problem for autonomous underwater vehicles whose initial starting position differs substantially from that specified by the desired trajectory. This scenario is very likely to cause serious chattering in the control output, especially in the early stages, when the large tracking error is the input to the controller. A novel trajectory tracking control strategy is developed based on fuzzy re-planning of a local desired trajectory. At each time instant, a local desired trajectory is reconstructed based on the AUV's current position and that specified by the original desired trajectory at a future time. Also the control effort is computed based on the local desired trajectory, rather than the original one. Moreover, the interval between each time instant is determined by a new single-input fuzzy model, where the input is determined by the distance between AUV's current and desired trajectories and the change in the distance. Finally, the effectiveness of the new control strategy is verified by simulation-based case studies using an actual vehicle model as a necessary step prior to experimental verification.
This article addresses a problem of seabed terrain following control (STFC) for an underwater vehicle-manipulator system (UVMS). The motivation is to perform a visual search of marine products closely to seabed in unknown environment. In terms of this issue, we propose a novel and robust STFC framework for our UVMS to maintain an appropriate height to seabed. A nonlinear model predictive control (NMPC) method is formulated to solve the STFC problem. To relieve online computational burden and system noisy influence, Ohtsuka's continuation/generalized minimal residual (C/GMRES) algorithm incorporated with a tracking differentiator (TD) is investigated. In order to improve the following accuracy, the system state prediction part of the NMPC and a long short-term memory (LSTM) network are elaborated to predict future seabed terrain using a depth gauge and an altimeter, respectively. Finally, the three different physical scenarios for STFC problem are established using ROS to demonstrate the robustness and efficiency of the proposed algorithm.
This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively. Specifically, for the critics, the expected absolute Bellman error-based updating rule is used to choose the worst critic to be updated in each time step. Subsequently, to calculate the loss function with more accurate target value for the chosen critic, Pseudo Q-learning, which uses subgreedy policy to replace the greedy policy in Q-learning, is developed for continuous action spaces, and Multi Pseudo Q-learning (MPQ) is proposed to reduce the overestimation of action-value function and to stabilize the learning. As for the actors, deterministic policy gradient is applied to update the weights, and the final learned policy is defined as the average of all actors to avoid large but bad updates. Moreover, the stability analysis of the learning is given qualitatively. The effectiveness and generality of the proposed MPQ-based deterministic policy gradient (MPQ-DPG) algorithm are verified by the application on AUV with two different reference trajectories. In addition, the results demonstrate high-level tracking control accuracy and stable learning of MPQ-DPG. Besides, the results also validate that increasing the number of the actors and critics will further improve the performance.
This paper studies the trajectory tracking control problem of an autonomous underwater vehicle (AUV). To explicitly deal with the finite thrust capability in the controller design, the Lyapunov-based model predicative control (LMPC) technique is applied. A nonlinear backstepping tracking control law is exploited to construct the contraction constraint in the formulated MPC problem so that the closed-loop stability can be guaranteed. Due to the optimization essential, the thrust allocation (TA) subproblem can be easily incorporated into the LMPC tracking control design. Sufficient conditions that ensure the recursive feasibility hence closed-loop stability are provided analytically. A guaranteed region of attraction (ROA) is explicitly characterized. In the meantime, the robustness of the tracking control can also be improved by the receding horizon implementation that is adopted in the LMPC control algorithm. Simulation results on the Saab SeaEye Falcon model AUV demonstrate the enhanced trajectory tracking control performance via the proposed LMPC.
In this paper, we consider depth control problems of an autonomous underwater vehicle (AUV) for tracking the desired depth trajectories. Due to the unknown dynamical model of the AUV, the problems cannot be solved by most of model-based controllers. To this purpose, we formulate the depth control problems of the AUV as continuous-state, continuous-action Markov decision processes (MDPs) under unknown transition probabilities. Based on deterministic policy gradient (DPG) and neural network approximation, we propose a model-free reinforcement learning (RL) algorithm that learns a state-feedback controller from sampled trajectories of the AUV. To improve the performance of the RL algorithm, we further propose a batch-learning scheme through replaying previous prioritized trajectories. We illustrate with simulations that our model-free method is even comparable to the model-based controllers as LQI and NMPC. Moreover, we validate the effectiveness of the proposed RL algorithm on a seafloor data set sampled from the South China Sea.
In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered in our control design. Based on the dynamics model derived in the discrete-time domain, two neural networks (NNs), including a critic and an action NN, are integrated into our adaptive control design. The critic NN is introduced to evaluate the long-time performance of the designed control in the current time step, and the action NN is used to compensate for the unknown dynamics. To eliminate the AUV's control input nonlinearities, a compensation item is also designed in the adaptive control. Rigorous theoretical analysis is performed to prove the stability and performance of the proposed control law. Moreover, the robustness and effectiveness of the proposed control method are tested and validated through extensive numerical simulation results.
Ant colony algorithm (ACA) is a heuristic evolutionary algorithm, which is based on bionic population, and PID controller of remote control underwater vehicle (ROV) can be optimized by it. Optimal combination of PID control parameters can be obtained by simulating the way ants find the shortest path, and then these parameters can be improved by ACA. The simulation results show that PID controller optimized by ACA will rise faster and overshoot smaller. The PID parameters improved by ACA will be better controlled than before.
In this paper, based on the dynamics model, taking into account the disturbance and control constraint, diving depth asymptotic stability controller of autonomous underwater vehicles is designed. Based on the establishment of the mathematic model of AUV and the linearization of processing , and assumed that the hydrodynamic parameters of the vehicle nonlinear model are precisely measured, the control question researched have much practical significance by introducing bending deflection of fin angle control which has rigid limit characteristics. By using linear matrix inequality processing method, a robust tracking controller is designed. Depth asymptotically tracking of AUV is realized under the condition of control constraint. The result of simulation shows that the depth controller is effectively accomplished in spite of control fin angle deflection constraint.
In recent twenty years, tens of the bionic underwater prototype propulsors which imitate aquatic animals have been invented. The bionic principles hand classification of bionic propulsors were presented firstly. The main advance of the bionic propulsors in the world has been listed in table form. Then the bottlenecks which include low efficiency and massive mechanism, hard-shelled reverse control method and exterior hydro-dynamic analysis are discussed. The future trends of the bionic underwater propulsors are forecasted. The main potential breakthroughs include innovative smart actuators and bionic neural control methods.
For the purpose of extending application area of a humanoid robot, we study recognition method for motion change under water and guess the depth from the surface of water. A property of an action under water is different from the one performed on land because of the influence of resistance inertia force by water wave. Therefore, it is more difficult to control actions under water. We use a small humanoid robot ldquoRobovie-Mrdquo and put a diving suit on it. When the robot stands up under water, its motion of standing up has to be adjusted. The robot falls down and floats up onto the surface of water it is gets up with the same motion at a different depth. The stand up motion should differ depending on its depth from the surface of water. We propose a method of estimating the robot's depth under water by measuring the motion of standing up and inclination angle of the upper body.
Sewerage is a very large and important infrastructure in the water
industry. Because around 20% of the municipal pipe network is estimated
to be significantly damaged, it is of high priority to inspect, maintain
and repair public sewers. During recent years mobile robots have been
developed to inspect but also to repair the pipes. To navigate through
the hostile environment, they are generally only equipped with video
cameras and connected to the outside world via an umbilical cord which
transmits the image information. The tethered robots are tele-operated
from a remote service station placed at the end of a maintenance shaft.
These pipe robots are commonly wheeled systems especially adapted to
withstand the adverse conditions of the waste pipe environment. In this
paper the actual state of the art in the field of mobile robots for
repair and inspection of sewerage is given and the possible improvements
of the next generation of robots caused by developments of the sensors,
the material, the tele-operation techniques and the locomotion
principles are reported. This will allow the robots to increase their
capabilities and to enlarge the application areas to pipes with smaller
diameters and pipes in industrial plants
MPC-based Motion Control of Underwater Vehicle with Fixed Depth
- yu
MPC-based Motion Control of Underwater Vehicle with Fixed Depth
- W Yu
- Q Liang
- N Xiong
- C Hong