MODEL PREDICTIVE CONTROL OF AN AUV USING DE-COUPLED APPROACH

2021 ◽  
Vol 160 (A1) ◽  
Author(s):  
M P R Prasad ◽  
A Swarup
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Satisfactory Performance ◽  
Dynamics And Control ◽  
Simple Implementation ◽  
Robotic Vehicle ◽  
And Control ◽  
Decoupled Structure ◽  
Underwater Robotic Vehicle

This paper considers the decoupled dynamics and control of an Autonomous Underwater Vehicle (AUV). The decoupled model consists of speed, steering and depth subsystems. Generally AUV model is unstable and nonlinear. The central theme of this paper is the development of model predictive control (MPC) for underwater robotic vehicle for ocean survey applications. The proposed MPC for decoupled structure can have simple implementation. Simulation results have been presented which confirm satisfactory performance. Decoupled approach is well suitable for applying control.

Model Predictive Control of an AUV Using De-Coupled Approach

2018 ◽  
Vol Vol 160 (A1) ◽  
Author(s):  
M P R Prasad
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Satisfactory Performance ◽  
Dynamics And Control ◽  
Simple Implementation ◽  
Robotic Vehicle ◽  
And Control ◽  
Decoupled Structure ◽  
Underwater Robotic Vehicle

This paper considers the decoupled dynamics and control of an Autonomous Underwater Vehicle (AUV). The decoupled model consists of speed, steering and depth subsystems. Generally AUV model is unstable and nonlinear. The central theme of this paper is the development of model predictive control (MPC) for underwater robotic vehicle for ocean survey applications. The proposed MPC for decoupled structure can have simple implementation. Simulation results have been presented which confirm satisfactory performance. Decoupled approach is well suitable for applying control.

scholarly journals Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Donald J. Docimo ◽  
Ziliang Kang ◽  
Kai A. James ◽  
Andrew G. Alleyne
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Performance Metrics ◽  
Control Strategies ◽  
Transportation Systems ◽  
Thermal Dynamics ◽  
Plant Optimization ◽  
Power And Energy Systems ◽  
And Control

Abstract This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation, and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electromechanical hybrid electric vehicle (HEV) powertrain model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV powertrain model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.

scholarly journals A Simplified Model Predictive Control for T-Type Inverter with Output LC Filter

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 31 ◽  
Author(s):  
Van-Quang-Binh Ngo ◽  
Minh-Khai Nguyen ◽  
Tan-Tai Tran ◽  
Young-Cheol Lim ◽  
Joon-Ho Choi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Performance Simulation ◽  
Control Scheme ◽  
Finite Control ◽  
Lc Filter ◽  
The Cost ◽  
And Control ◽  
Measurement And Control ◽  
Selection Of

In this paper, a model predictive control scheme for the T-type inverter with an output LC filter is presented. A simplified dynamics model is proposed to reduce the number of the measurement and control variables, resulting in a decrease in the cost and complexity of the system. Furthermore, the main contribution of the paper is the approach to evaluate the cost function. By employing the selection of sector information distribution in the reference inverter voltage and capacitor voltage balancing, the execution time of the proposed algorithm is significantly reduced by 36% compared with conventional model predictive control without too much impact on control performance. Simulation and experimental results are studied and compared with conventional finite control set model predictive control to validate the effectiveness of the proposed method.

An integrated control strategy of path following and lateral motion stabilization for autonomous distributed drive electric vehicles

2019 ◽  
pp. 095440701988416
Author(s):  
Yuan Zou ◽  
Ningyuan Guo ◽  
Xudong Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Integrated Control ◽  
Path Following ◽  
Control Technique ◽  
Programming Algorithm ◽  
Linear Quadratic ◽  
And Control

This article proposes an integrated control strategy of autonomous distributed drive electric vehicles. First, to handle the multi-constraints and integrated problem of path following and the yaw motion control, a model predictive control technique is applied to determine optimal front wheels’ steering angle and external yaw moment synthetically and synchronously. For ensuring the desired path-tracking performance and vehicle lateral stability, a series of imperative state constraints and control references are transferred in the form of a matrix and imposed into the rolling optimization mechanism of model predictive control, where the detailed derivation is also illustrated and analyzed. Then, the quadratic programming algorithm is employed to optimize and distribute each in-wheel motor’s torque output. Finally, numerical simulation validations are carried out and analyzed in depth by comparing with a linear quadratic regulator–based strategy, proving the effectiveness and control efficacy of the proposed strategy.

Sign in / Sign up

Export Citation Format

Share Document