Experimental Study on Optimal Tracking Control of a Micro Ground Vehicle

2017 ◽  
Vol 29 (4) ◽  
pp. 757-765 ◽  
Author(s):  
Soichiro Watanabe ◽  
◽  
Masanori Harada
Keyword(s):  
Experimental Study ◽  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Predictive Control ◽  
Tracking Control ◽  
Reference Trajectory ◽  
Ground Vehicle ◽  
Optimal Tracking ◽  
Tracking Controller

This paper investigates the application of optimal control to a micro ground vehicle (MGV) experimentally. The model predictive control (MPC) technique is used for the overall tracking controller during the maneuver. The reference trajectory for MPC is preliminarily obtained by numerical computation of the optimal control problem, which is prescribed as a minimum-time maneuver. The results provide nominal tracking performance and validate the feasibility of the approach.

Optimal Tracking Control of a Micro Ground Vehicle

2015 ◽  
Vol 27 (6) ◽  
pp. 653-659 ◽  
Author(s):  
Soichiro Watanabe ◽  
◽  
Masanori Harada
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Coordinate System ◽  
Predictive Control ◽  
Tracking Control ◽  
Minimum Time ◽  
Reference Trajectory ◽  
Ground Vehicle ◽  
Optimal Tracking

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/07.jpg"" width=""300"" /> Coordinate system of MGV</div>This paper investigates the application of optimal micro ground vehicle (MGV) control involving overall tracking by model-predictive control (MPC) during a minimum-time maneuver. The MPC’s reference trajectory is obtained beforehand by numerically calculating an optimal control problem described as a minimum-time maneuver. Results provide nominal tracking performance and confirm the feasibility of our approach.

scholarly journals Robust Tracking Control of Mobile Robot Formation with Obstacle Avoidance

2007 ◽  
Vol 2007 ◽  
pp. 1-10 ◽  
Author(s):  
Tiantian Yang ◽  
Zhiyuan Liu ◽  
Hong Chen ◽  
Run Pei
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Formation Control ◽  
Optimization Problem ◽  
Reference Trajectory ◽  
Robust Tracking Control ◽  
Wheeled Mobile Robots ◽  
Lmi Optimization ◽  
Control Scheme

We consider the formation control problem of multiple wheeled mobile robots with parametric uncertainties and actuator saturations in the environment with obstacles. First, a nonconvex optimization problem is introduced to generate the collision-free trajectory. If the robots tracking along the reference trajectory find themselves moving close to the obstacles, a new collision-free trajectory is generated automatically by solving the optimization problem. Then, a distributed control scheme is proposed to keep the robots tracking the reference trajectory. For each interacting robot, optimal control problem is generated. And in the framework of LMI optimization, a distributed moving horizon control scheme is formulated as online solving each optimal control problem at each sampling time. Moreover, closed-loop properties inclusive of stability andH∞performance are discussed. Finally, simulation is performed to highlight the effectiveness of the proposed control law.

scholarly journals Decentralized Reinforcement Learning Robust Optimal Tracking Control for Time Varying Constrained Reconfigurable Modular Robot Based on ACI andQ-Function

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Bo Dong ◽  
Yuanchun Li
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Control Theory ◽  
Continuous Time ◽  
Tracking Control ◽  
Control Policy ◽  
Time Varying ◽  
Optimal Tracking ◽  
Tracking Controller ◽  
Global Uncertainty

A novel decentralized reinforcement learning robust optimal tracking control theory for time varying constrained reconfigurable modular robots based on action-critic-identifier (ACI) and state-action value function (Q-function) has been presented to solve the problem of the continuous time nonlinear optimal control policy for strongly coupled uncertainty robotic system. The dynamics of time varying constrained reconfigurable modular robot is described as a synthesis of interconnected subsystem, and continuous time state equation andQ-function have been designed in this paper. Combining with ACI and RBF network, the global uncertainty of the subsystem and the HJB (Hamilton-Jacobi-Bellman) equation have been estimated, where critic-NN and action-NN are used to approximate the optimalQ-function and the optimal control policy, and the identifier is adopted to identify the global uncertainty as well as RBF-NN which is used to update the weights of ACI-NN. On this basis, a novel decentralized robust optimal tracking controller of the subsystem is proposed, so that the subsystem can track the desired trajectory and the tracking error can converge to zero in a finite time. The stability of ACI and the robust optimal tracking controller are confirmed by Lyapunov theory. Finally, comparative simulation examples are presented to illustrate the effectiveness of the proposed ACI and decentralized control theory.

scholarly journals Self-Triggered Model Predictive Control for Perturbed Underwater Robot Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhongxian Xu ◽  
Lile He ◽  
Ning He ◽  
Lipeng Qi
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Control Problem ◽  
Predictive Control ◽  
Information Transfer ◽  
Control Method ◽  
Kinematic Model ◽  
Underwater Robot ◽  
Robot Systems

Aiming at solving the control problem of a constrained and perturbed underwater robot, a control method was proposed by combining the self-triggered mechanism and the nonlinear model predictive control (NMPC). The theoretical properties of the kinematic model of the underwater robot, as well as the corresponding MPC controller, are first studied. Then, a novel technique for determining the next update moment of both the optimal control problem and the system state is developed. It is further rigorously proved that the proposed algorithm can (1) stabilize the closed-loop underwater robot system, (2) reduce the time of solving the optimal control problem and (3) save the information transfer resources. Finally, a case study is provided to show the effectiveness of the developed researched scheme.

scholarly journals Parametric Approach to Trajectory Tracking Control of Robot Manipulators

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Shijie Zhang ◽  
Yi Ning
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Optimal Trajectory ◽  
Open Loop ◽  
Parametric Approach ◽  
Trajectory Tracking Control ◽  
Loop Control

The mathematic description of the trajectory of robot manipulators with the optimal trajectory tracking problem is formulated as an optimal control problem, and a parametric approach is proposed for the optimal trajectory tracking control problem. The optimal control problem is first solved as an open loop optimal control problem by using a time scaling transform and the control parameterization method. Then, by virtue of the relationship between the optimal open loop control and the optimal closed loop control along the optimal trajectory, a practical method is presented to calculate an approximate optimal feedback gain matrix, without having to solve an optimal control problem involving the complex Riccati-like matrix differential equation coupled with the original system dynamics. Simulation results of 2-link robot manipulator are presented to show the effectiveness of the proposed method.

Choosing the number of time intervals for solving a model predictive control problem of nonlinear systems

2021 ◽  
pp. 014233122110073
Author(s):  
Jasem Tamimi
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Control Problem ◽  
Predictive Control ◽  
Optimization Problem ◽  
The State ◽  
Nonlinear Program ◽  
Box Constraints ◽  
Time Intervals

Model predictive control (MPC) is a control strategy that can handle state and control multi-variables at same time. To use the MPC using direct methods for solving the a dynamic optimization problem, one needs, for example, to transform the optimization problem into a nonlinear programming (NLP) problem by dividing the prediction horizon into equal time intervals. In this work, we suggest a tool and procedures for helping to choose a ‘compromise’ number of time intervals with a needed accuracy, objective cost, number of turned NLP iterations and computational time. On the other hand, we offer a simplified nonlinear program to ensure the convergence of a class of finite optimal control problem by modifying the state box constraints. In particular, a special type of box constraints were used to the constrained optimal control problem to enforce the state trajectories to reach the desired stationary point. These box constraints are characterized by some parameters that are easily optimized by our proposed nonlinear program. Our proposed tools are tested using two case studies; nonlinear continuous stirred tank reactor (CSTR) and nonlinear batch reactor.

scholarly journals Prediction error's minimization through a controller based on a Metaheuristic Algorithm

2020 ◽  
Vol 43 (3) ◽  
pp. 5-11
Author(s):  
Viorel Mînzu
Keyword(s):  
Optimal Control ◽  
Theoretical Analysis ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Predictive Control ◽  
Closed Loop ◽  
Open Loop ◽  
The Other ◽  
Metaheuristic Algorithm ◽  
Prediction Technique

A Metaheuristic Algorithm (MA) can be a realistic method to solve a given Optimal Control Problem (OCP), but the result is an open-loop solution. If the Metaheuristic Algorithm is integrated within the Model Predictive Control (MPC) structure, a closed-loop solution can be achieved. The controller works using a prediction technique and prediction error's minimization. On the other side, the MA optimizes (minimizes or maximizes) the OCP's objective function. The controller is faced with two optimization tasks. This paper proves through theoretical analysis and simulations that the prediction error's minimization is implicitly accomplished.

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