Robust Model Predictive Control Based on Active Disturbance Rejection Control for a Robotic Autonomous Underwater Vehicle

Diving control of Autonomous Underwater Vehicle based on improved active disturbance rejection control approach

The flatness-based active disturbance rejection control (ADRC) approach via online algebraic parameter identification is proposed to deal with the double tank problem. The control task is to maintain the lower tank level at a desired value by manipulating the inlet regulating valve. Firstly, the double tank problem is formulated based on the designed double tank platform, where the process input and output data are available in real-time in Matlab software installed in the upper monitor and the ABB AC800M in the lower computer. Then, the algebraic parameter identification algorithm is proposed to identify the unknown parameters of a typical second-order process, which was a flatness system with the lower tank level as the flat output. All the unmodelled dynamics, as well as the external disturbances are regarded as the total disturbance. Based on the identified model parameters of the process, an extended state observer is constructed to estimate the the flat output and its time derivative, as well as the total disturbance in real-time. A linear feedback control law is then designed to steer the lower tank level following the desired trajectory of the flat output, and thus the flatness-based ADRC approach is designed. The effectiveness of the proposed approach is validated by the simulation results based on the practical process input and output data in the platform.

A generalized active disturbance rejection control (ADRC) approach is proposed for general linear systems. The method follows the idea of the conventional ADRC by using extended state observer (ESO) to estimate the generalized disturbance and using it in the feedback control to attenuate it quickly. It is observed that the idea is appropriate under a matching condition assumption. The generalized ADRC method makes it possible to incorporate any known information of the plant in the control instead of just using the order and the gain information, thus better performance can be achieved, especially for nonminimum-phase processes and unstable processes. Moreover, the generalized ADRC structure is transferred to the well-known two-degree-of-freedom internal-model-control (IMC) structure, thus the stability analysis and parameter tuning can be done in the IMC framework.

In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various tight constraints. To consider this requirement, this article has proposed a lateral control for autonomous vehicle considering the lateral offset error constraints. A reduced-order dynamics of the vehicle has been considered for improving and easy implementation of backstepping. A nonlinear control has been developed by utilizing the Time-varying Asymmetric Barrier Lyapunov Function via the Active Disturbance Rejection Control (ADRC) approach. The Extended State Observer, a component of applied ADRC, estimates all unknown system states as well as the unknown disturbance. The desired trajectories (smooth and non-smooth) based on the yaw rate profile have been synthesized to test the performance of the vehicle. Simulation results are presented to show the effectiveness of the proposed closed-loop control strategy. The vehicle performance has been compared using the proposed method, the conventional Quadratic Lyapunov Function and Symmetric Barrier Lyapunov Function based control. The proposed control provides a smoother control signal, small tracking error and robustness in presence of hard road constraints for smooth/non-smooth vehicle motion.

The robust model predictive control (RMPC) based on extended state observer (ESO) is proposed for autonomous underwater vehicle (AUV) by considering the complexity and uncertainty of the marine environment and the strong nonlinearity and coupling. Based on the dynamics of the AUV, a nominal system is given by neglecting uncertainties and disturbances. Then, a robust model predictive control algorithm is designed for the nominal system and the robustness of the system is analyzed for the system uncertainty. The controller acting on the real system is designed by the feedback linearization method and the remained disturbances are estimated and compensated by the extended state observer. The asymptotic stability of the system is analyzed using Lyapunov theory and the input state stability of the error system is analyzed. It is demonstrated through simulation experiments that the extended state observer based robust model predictive controller has a better trajectory tracking performance and anti-disturbance capability than the common model predictive controller.

Distributed robust model predictive control-based formation-containment tracking control for autonomous underwater vehicles

There exists a steady-state error in the closed-loop system output with some conventional linear active rejection control approaches when the pitch channel control variable of the near space vehicle is the angle of attack. In this study, a cascaded linear active disturbance rejection control approach based on the cascaded control idea is proposed. Both the inner-loop controller and the outer-loop controller are designed by the linear active disturbance rejection control method. The inner-loop is built by angular rate feedback to improve dynamic performance of the closed-loop system, and the outer-loop is built by angle of attack feedback to remove the undesired steady-state error. The approach is employed to a hypersonic near space vehicle autopilot. The numerical simulations show that the proposed approach preserves the performances on time domain and frequency domain as good as those of the conventional active disturbance rejection control approaches, and it has the better performances on disturbance rejection and angle of attack command tracking accuracy than those of some previous active disturbance rejection control approaches.

AUV position tracking and trajectory control based on fast-deployed deep reinforcement learning method

This paper describes research on remote monitoring and control of autonomous underwater vehicles (AUVs). This work is part of a larger effort to create autonomous ocean sampling networks (AOSN), a new concept for collecting synoptic oceanographic data. AOSN is based on the operation of small, low cost autonomous underwater vehicles (AUVs) within an array of moorings that provide communication and power-supply nodes. Critical to the realization of AOSN will be the ability to control these assets and monitor the data collection process remotely. During the time in which our AOSN partners have been developing acoustic communications, our laboratory has performed a variety of remote monitoring and control experiments to test different types of network distributed control software. Experimental results are presented for runs in the Charles River in which a radio modem towed on a float was used to monitor and control the vehicle from a workstation on the Internet. In addition, we have used our acoustic navigation system to send simple command messages to an Odyssey vehicle, achieving simultaneous control of multiple AUVs operating in a high current environment in the Haro Strait.

The intelligent motion control technology of Autonomous Underwater Vehicle (AUV) is the basis of long-distance voyage for AUV. The motion control capability in vertical plane is an important technical for AUV to complete multifarious missions. So, it is important and realistic significance to research on motion control in vertical plane of AUV. Motion control problem of AUV in vertical plane, especially depth control problems is researched based on active disturbance rejection control (ADRC) method in this paper. The kinetic and dynamic model in vertical plane of AUV is established and simplified. The active disturbance rejection controller design method is introduced. Detailed algorithms of each part for active disturbance rejection controller are discussed according to separability principle of ADRC. Based on ADRC, the depth controller is designed and applied in AUV. At last, simulation results indicate that the method is feasibility and effectiveness for AUV depth control.

In this paper, the problem of maximizing energy yield from the available wind is addressed by using a robust Proportional-Derivative (PD) controller in combination with a Linear Active Disturbance Rejection Control (LADRC) approach. Firstly, the theory of the Wind Turbine (WT) pitch control algorithms is analysed and then based on the analysis, 1.5 MW WT model with a Servo System (SS) and Doubly Fed Induction Generator (DFIG) is developed. The blade pitch angle (R) is controlled to ensure that the variable speed WT system operate safely and the optimum power is achieved. Developed LADRC algorithm reveal dynamic performance of the blade pitch angle, rotor speed and rated power characteristics. Within the wind turbine standard operating regimes, controllers have shown promising results. Proposed LADRC technique validates superior performance. Finally, MATLAB/SIMULINK tool box establishes the corresponding model to simulation test in order to verify the effectiveness and correctness of the analysed algorithms. Simulation result demonstrates that the developed pitch servo control system has good dynamic, steady state performance and robustness.

The multiple autonomous underwater vehicle (AUV) formation plays an important role in underwater missions, such as oceanographic sampling and water pollution monitoring. This article presents the mechatronic design, modeling, formation control, and experiments of multiple AUVs. The structure of the AUV and a simplified mathematical model for tracking control are described. To achieve formation control, we formulate a control framework for the multiple AUVs. The upper layer is a formation algorithm based on a novel leader-follower control law. The bottom layer is a dynamic controller based on active disturbance rejection control (ADRC). The formation algorithm is in charge of calculating reference values for the followers to maintain a desired pattern with the leader. The stability and convergence properties of the algorithm have been analyzed using the Lyapunov stability method. Meanwhile, an ADRC approach-based dynamic controller is established to track the reference values. Numerical simulations are carried out to analyze formation control and validate the control framework. The multiple AUVs can switch and maintain the formation between the one-line pattern and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern. Finally, extensive formation field experiments involving the one-line pattern and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern show the good motion ability of the self-designed AUVs and also verify the feasibility of the proposed control approach. Note to Practitioners—The motivation of the article is to design a practical formation control approach for multiple AUVs and verify the control approach in the field. Although there have been a lot of prior research studies on multiple AUVs, how to design a formation control approach subjected to communication bandwidth constraints and how to develop multiple AUVs and verify the effectiveness of the control method in the field are worthy of intense investigation. Hence, this article builds the mechatronic design and dynamic model of the AUV and proposes a novel leader-follower formation control approach based on the dynamics and kinematic model of AUV. Besides, the stability of formation control for multiple AUVs is proven by the Lyapunov theorem. Multiple AUVs can switch and maintain formation between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern and one-line pattern with a smaller error. Finally, the performance of the proposed formation control strategy is experimentally verified using three self-made AUVs inside a large reservoir. The proposed method is suitable for multiple AUVs missions involving underwater surveillance, underwater pipeline inspection in the ocean.

A practical design method is developed for path following and coordinated control of Autonomous Underwater Vehicles (AUVs). Each AUV is modeled by a system with time-varying parameters, unknown nonlinear dynamics and unknown disturbance. A robust adaptive distributed controller is designed for each AUV such that all AUVs ultimately synchronize to the desired paths with required formation. Moreover, these controllers are distributed in the sense that the controller design for each AUV only requires relative state in formation between itself and its neighbors. Finally, a simulation example demonstrates the effectiveness of the control method.

The cooperative control of marine vehicles finds many applications in a variety of marine missions and tasks. This paper investigates the receding horizon formation control problem of a feet of nonholonomic autonomous underwater vehicles (AUVs), in which the follower AUVs are required to keep a prescribed formation pattern with respect to the leader one. A novel optimization problem and a new receding horizon control (RHC) algorithm are proposed. Results on the feasibility of the receding horizon formation algorithm and on the stability of the closed-loop systems are provided. Simulation studies for the formation-keeping control of the nonholonomic AUVs are carried out, and the simulation results verifies the effectiveness of the proposed algorithm.

Trajectory tracking control for autonomous underwater vehicle with disturbances and input saturation based on contraction theory