Model Predictive Adaptive Constraint Tracking Control for Underwater Vehicles

Gan, Wenyang; Zhu, Daqi; Hu, Zhen; Shi, Xianpeng; Yang, Lei; Chen, Yunsai

doi:10.1109/tie.2019.2941132

Cited by 65 publications

(17 citation statements)

References 25 publications

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“…Analytical model [1] Complexity and the nonlinearity of the model local-CFDL Avoid errors caused by the traditional linearization process.…”

Section: Authors Influencing Factors Methods Of Modeling Important Findingmentioning

confidence: 99%

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AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

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“…Analytical model [1] Complexity and the nonlinearity of the model local-CFDL Avoid errors caused by the traditional linearization process.…”

Section: Authors Influencing Factors Methods Of Modeling Important Findingmentioning

confidence: 99%

“…An underwater vehicle is a semi-autonomous or fully autonomous underwater robot equipped with sensing, decision-making, and execution capabilities [1]. It has been widely used in marine exploration and mapping, underwater pipeline inspection, and scientific and military missions.…”

Section: Introductionmentioning

confidence: 99%

AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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“…Therefore, large velocity or drastic change of velocity can be generated from a large error condition. This phenomenon is termed as speed jump in some articles [16], [17]. When backstepping to the dynamic control, the required force/torque at the jump point may exceed the maximal force/torque that the airship can provide, which is known as input saturation.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control for a Stratospheric Airship Subject to Constraints and Unknown Disturbances

et al. 2020

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This paper addresses the spatial trajectory tracking problem for a stratospheric airship with state constraints, input saturation and unknown disturbances. First, a Laguerre-based model predictive kinematic controller (LMPC) is proposed to tackle the state constraints and generate the desired velocity signal. To reduce the complexity of online optimization, Laguerre functions are applied to decrease the number of optimization variables by approximating the predicted control sequence. Second, in the dynamic loop, a sliding mode controller (SMC) with fast power rate reaching law (FPRRL) is introduced to track the desired velocity signal. The unknown disturbances in the dynamic model of airship are estimated and compensated by reduced-order extended state observer (ESO). An anti-windup compensator is incorporated into the FPRRL-based SMC controller to deal with the input saturation. Stability analysis implies that the tracking errors converge to a small neighborhood of zero. Comparative simulations about spatial straight and curve trajectory tracking are provided to evaluate the effectiveness and robustness of the proposed control scheme. INDEX TERMS Input saturation, model predictive control, state constraints, stratospheric airship, trajectory tracking, unknown disturbances.

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“…Moreover, submersible robotized vessels are subjected to model uncertainty and parametric variations while they are also affected by external perturbations [12][13][14][15][16]. For these reasons, the control problem of a submarine's depth and heading angle is a nontrivial one [17][18][19][20][21][22][23][24][25]. Previous results for the solution of this problem with the use of optimal control theory can be found in [26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control of autonomous submarines’ diving

Rigatos

Siano

Zouari

et al. 2020

Mar Syst Ocean Technol

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The article presents a nonlinear H-infinity (optimal) control approach for the problem of the control of the depth and heading angle of an autonomous submarine. This is a multivariable nonlinear control problem and its solution allows for precise underwater navigation of the submarine. The submarine's dynamic model undergoes approximate linearization around a temporary equilibrium that is recomputed at each iteration of the control algorithm. The linearization procedure is based on Taylor series expansion and on the computation of the submarine's model Jacobian matrices. For the approximately linearized model, the optimal control problem is solved through the design of an H-infinity feedback controller. The computation of the controller's gain requires the solution of an algebraic Riccati equation, which is performed at each time-step of the control method. The global stability of the control scheme is proven through Lyapunov analysis. First, it is demonstrated that for the submarine's control loop, the H-infinity tracking performance criterion holds. Moreover, under moderate conditions, it is shown that the control scheme is globally asymptotically stable.

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Model Predictive Adaptive Constraint Tracking Control for Underwater Vehicles

Cited by 65 publications

References 25 publications

AUV Trajectory Tracking Models and Control Strategies: A Review

AUV Trajectory Tracking Models and Control Strategies: A Review

Trajectory Tracking Control for a Stratospheric Airship Subject to Constraints and Unknown Disturbances

Nonlinear optimal control of autonomous submarines’ diving

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