DYNAMIC MODEL BASED PREDICTIVE CONTROL FOR MOBILE ROBOTS (original) (raw)

Dynamic nonlinear model based predictive control for mobile robots

In this paper, a nonlinear predictive controller (NMPC) to control a unicycle-like mobile robot for trajectory tracking has been developed. A dynamic model of a PIONNER 3-DX mobile robot is used, where external forces and wheels sliding have been considered. Restrictions on control actions and system states are also considered. Simulations results at both tracking and regulation (positioning) are shown, these results show the good performance of the developed controller. Finally, this paper shows that the controller can be implemented in real-time by using an analysis of the calculating times of the NMPC algorithm.

Comparative Study of PI Controller and Model Based Predictive Control for Mobile Robot

Universal Journal of Control and Automation, 2017

This paper focuses on resolving the trajectory tracking problem of two wheeled mobile robot. We begin by presenting the kinematic model of the robot which is the base of the control law then we present a PI controller and a model predictive controller to solve the problem of trajectory tracking. We performed a comparison between the performances of the classical PI controller and the predictive controller which is an interesting approach that considers an explicit performance criterion and minimizes it during the computation of the control law. Simulation results are provided in order to show the effectiveness of model predictive control in the resolution of trajectory tracking problem.

Controlling of Mobile Robot by Using of Predictive Controller

IAES International Journal of Robotics and Automation (IJRA), 2017

In this paper implementation of Model Predictive<br />Controller on mobile robot was explained. The conducted<br />experiments show effectiveness of the proposed method on<br />control of the mobile robot. Furthermore the effects of the model<br />parameters such as control horizon, prediction horizon,<br />weighting factor and signal filter band on the controller<br />performance were studied. Finally, a comparison between the<br />designed MPC controller and PID and adaptive controllers was<br />presented demonstrating superior performance of the Model<br />Predictive Controllers.

Model predictive Controller for Mobile Robot

Transactions on Environment and Electrical Engineering, 2017

This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is implemented on a real robot. The comparison between a PID controller, adaptive controller, and the MPC illustrates advantage of the designed controller and its ability for exact control of the robot on a specified guide path.

Comparative Application of Model Predictive Control Strategies to a Wheeled Mobile Robot

Journal of Intelligent & Robotic Systems, 2017

Model predictive control strategies refer to a set of methods relying on a process model to determine an optimal control signal by minimising a cost function. This paper reports on the application of predictive control strategies to a wheeled mobile robot. As a first step, friction forces originating from the motor gearboxes and wheels were estimated and a feedforward compensation was applied. Step response tests were then carried out to identify a linear model to design several simple control strategies, such as the Proportional-Integral-Derivative (PID) controller. The PID response constitutes the reference to assess the efficiency of two predictive control strategies: the generalised predictive control (GPC) and the linear quadratic model predictive control (LQMPC) algorithms. These control strategies were tested in simulation with Matlab and EasyDyn (a C++ library for multibody system simulations) and in real life experiments. All three control strategies offer satisfactory reference tracking but MPC allows a reduction of the

Model Predictive Control of a Differential-Drive Mobile Robot

Acta Universitatis Sapientiae Electrical and Mechanical Engineering

This paper presents a model predictive control (MPC) for a differential-drive mobile robot (DDMR) based on the dynamic model. The robot’s mathematical model is nonlinear, which is why an input–output linearization technique is used, and, based on the obtained linear model, an MPC was developed. The predictive control law gains were acquired by minimizing a quadratic criterion. In addition, to enable better tuning of the obtained predictive controller gains, torques and settling time graphs were used. To show the efficiency of the proposed approach, some simulation results are provided.

The Control Design for Trajectory Tracking of Four-wheeled Mobile Robot using Model Predictive Control: A Preliminary Study

2018

The mobile robot is one of the Unmanned Vehicle that belongs to Unmanned Ground Vehicle (UGV) which has the ability to be remotely controlled. The advancement in navigation has enabled new opportunity to send the UGV to explore new area or disaster mitigation. The path is set with the help of digital imaging of a certain area, then the mobile robot is expected to follow the path. In this paper, we conduct a simulation of fourwheeled mobile robot trajectory tracking. We design a control for trajectory tracking using the Model Predictive Control approach. By simulating for some values of control and reference, the simulation shows that the control design for the trajectory tracking in four wheeled mobile robots need some improvement. Keywords—model predictive control, mobile robot, trajectory tracking

Nonlinear model predictive control for trajectory tracking of nonholonomic mobile robots

International Journal of Advanced Robotic Systems, 2018

Trajectory tracking for autonomous vehicles is usually solved by designing control laws that make the vehicles track predetermined feasible trajectories based on the trajectory error. This type of approach suffers from the drawback that usually the vehicle dynamics exhibits complex nonlinear terms and significant uncertainties. Toward solving this problem, this work proposes a novel approach in trajectory tracking control for nonholonomic mobile robots. We use a nonlinear model predictive controller to track a given trajectory. The novelty is introduced by using a set of modifications in the robot model, cost function, and optimizer aiming to minimize the steady-state error rapidly. Results of simulations and experiments with real robots are presented and discussed verifying and validating the applicability of the proposed approach in nonholonomic mobile robots.

Mobile robot trajectory tracking using model predictive control

II IEEE LARS, 2005

This work focus on the application of model-based predictive control (MPC) to the trajectory tracking problem of nonholonomic wheeled mobile robots (WMR). The main motivation of the use of MPC in this case relies on its ability in considering, in a straightforward way, control and state constraints that naturally arise in practical problems. Furthermore, MPC techniques consider an explicit performance criterion to be minimized during the computation of the control law. The trajectory tracking problem is solved using two approaches: (1) nonlinear MPC and (2) linear MPC. Simulation results are provided in order to show the effectiveness of both schemes. Considerations regarding the computational effort of the MPC are developed with the purpose of analyzing the real-time implementation viability of the proposed techniques.

Navigation of a Differential Drive Mobile Robot Using Nonlinear Model Predictive Control

2021

Welid Benchouche , Rabah Mellah, and Mohammed Salah Bennouna 1 L2CSP Laboratory,Faculty of Electrical and Computing Engineering, University Mouloud Mammri of TiziOuzou, 15000, Algeria 2 Mechanical engineering dept, University Kasdi Merbah, Ouargla, 30000, Algeria Abstract In this paper, an implementation of a very fast nonlinear model-based predictive controller using a newly developed open-source toolkit (CasADi) was used to attain the two control goals of differential drive mobile robots, point stabilization (regulation) and trajectory following (time-varying reference). The controller’s stability was assured by the addition of final state equality constraints, which in general require a long optimization horizon for feasibility. In the work presented here, we performed a full-scale simulation proving the applicability of the terminal stabilization equality constraint have been performed. The obstacle avoidance problem has been solved by adding the obstacle position as a constrain...