Path-Following Control of an AUV: A Multiobjective Model Predictive Control Approach

2019 ◽  
Vol 27 (3) ◽  
pp. 1334-1342 ◽  
Author(s):  
Chao Shen ◽  
Yang Shi ◽  
Brad Buckham
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Path Following ◽  
Control Approach ◽  
Path Following Control

Nonlinear output path following control using a two-loop robust model predictive control approach

2021 ◽  
pp. 014233122097142
Author(s):  
Mohammad Ghassem Farajzadeh-Devin ◽  
Seyed Kamal Hosseini Sani
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Robot Manipulator ◽  
Path Following ◽  
Uncertain System ◽  
Loop Model ◽  
Control Approach ◽  
Output Tracking ◽  
Path Following Control ◽  
Nonlinear Uncertain System

In this paper, output tracking of a geometric path for a nonlinear uncertain system with input and state constraints is considered. We propose an enhanced two-loop model predictive control approach for output tracking of a nonlinear uncertain system. Additionally, we propose an optimal version of output path following control problem to improve the controller synthesis. Satisfaction of the dynamical constraints of a system such as velocity, acceleration and jerk limitations is added to the problem introducing a new augmented system. The recursive feasibility of the proposed method is demonstrated, and its robust stability is guaranteed such that relaxation on the terminal constraint and penalty are achieved. To validate the theoretical benefits of the proposed controller, it is simulated on a SCARA robot manipulator and the results are compared with a two-loop model predictive controller successfully.

Discrete and Analytic Model Predictive Control for Path Following of Underactuated Ships

2009 ◽  
Author(s):  
Xiaofei Wang ◽  
Zaojian Zou ◽  
Tieshan Li ◽  
Weilin Luo
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Feedback Linearization ◽  
Control Method ◽  
State Space Model ◽  
Path Following ◽  
Analytic Model ◽  
Space Model ◽  
Path Following Control

The control problem of underactuated surface ships and underwater vehicles has attracted more and more attentions during the last years. Path following control aims at forcing the vehicles to converge and follow a desired path. Path following control of underactuated surface ships or underwater vehicles is an important issue to study nonlinear systems control, and it is also important in the practical implementation such as the guidance and control of marine vehicles. This paper proposes two nonlinear model predictive control algorithms to force an underactuated ship to follow a predefined path. One algorithm is based on state space model, the other is based on analytic model predictive control. In the first algorithm, the state space GPC (Generalized Predictive Control) method is used to design the path-following controller of underactuated ships. The nonlinear path following system of underactuated ships is discretized and re-arranged into state space model. Then states are augmented to get the new state space model with control increment as input. Thus the problem is becoming a typical state space GPC problem. Some characters of GPC such as cost function, receding optimization, prediction horizon and control horizon occur in the design procedure of path-following controller. The control law is derived in the form of control increment. In the second algorithm, an analytic model predictive control algorithm is used to study the path following problem of underactuated ships. In this path-following algorithm, the output-redefinition combined heading angle and cross-track error is introduced. As a result, the original single-input multiple-output (SIMO) system is transformed into an equivalent single-input single-output (SISO) system. For the transformed system, we use the analytic model predictive control method to get path-following control law in the analytical form. The analytic model predictive controller can be regarded as special feedback linearization method optimized by predictive control method. It provides a systematic method to compute control parameters rather than by try-and-error method which is often used in the exact feedback linearization control. Relative to GPC, the analytic model predictive control method provides an analytic optimal solution and decreases the computational burden, and the stability of closed-loop system is guaranteed. The path-following system of underactuated ships is guaranteed to follow and stabilize onto the desired path. Numerical simulations demonstrate the validity of the proposed control laws.

scholarly journals Path following of underactuated surface ships based on model predictive control with neural network

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094595
Author(s):  
Ronghui Li ◽  
Ji Huang ◽  
Xinxiang Pan ◽  
Qionglei Hu ◽  
Zhenkai Huang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Input Saturation ◽  
Path Following ◽  
Environmental Disturbances ◽  
Control Approach ◽  
Underactuated Ship ◽  
Mmg Model ◽  
The Mathematical Model

A model predictive control approach is proposed for path following of underactuated surface ships with input saturation, parameters uncertainties, and environmental disturbances. An Euler iterative algorithm is used to reduce the calculation amount of model predictive control. The matter of input saturation is addressed naturally and flexibly by taking advantage of model predictive control. The mathematical model group (MMG) model as the internal model improves the control accuracy. A radial basis function neural network is also applied to compensate the total unknowns including parameters uncertainties and environmental disturbances. The numerical simulation results show that the designed controller can force an underactuated ship to follow the desired path accurately in the case of input saturation and time-varying environmental disturbances including wind, current, and wave.

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