Design of Model Predictive Control of two-wheeled inverted pendulum robot

Minouchehr, Niloufar; Sani, Seyed Kamal Hosseini

doi:10.1109/icrom.2015.7367827

Cited by 13 publications

(3 citation statements)

References 13 publications

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“…In the mechanical system, smooth responses without oscillations are always expected in order to avoid damaging parts; the proposed structure has lesser oscillations compared to the PID controller-based IP system in [65] due to the presence of LQR in the feedback path. Due to the presence of the reference model, the error also under control in the proposed work compared to the MPC-based IP system presented in [66].…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Design of State Feedback LQR Based Dual Mode Fractional-Order PID Controller using Inertia Weighted PSO Algorithm: For Control of an Underactuated System

Ramakrishnan

Nachimuthu

2021

J. Inst. Eng. India Ser. C

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Several control strategies are proposed and developed to enhance the performance of the various underactuated systems, particularly inverted pendulum. This paper presents the dual-mode fractional-order control with a reference model for pitch and angle control of an inverted pendulum. An inertia weighted PSO is utilized for optimal tuning of the FOPID parameters to ensure an optimal balance between local and global search. A cost function of this algorithm is framed based on the error between the reference model output and actual system output along with time-domain performance criteria. The reference model-based tuning improves the performance of the controller and steady-state error tracking. In addition, an optimal state feedback LQR is implemented using pole placement design in the feedback to stabilize and improve the robustness of the inverted pendulum. Compared to the conventional PID, the proposed structure illustrates the FOPID structure has significant performance improvement in both with and without disturbance conditions.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Design of State Feedback LQR Based Dual Mode Fractional-Order PID Controller using Inertia Weighted PSO Algorithm: For Control of an Underactuated System

Ramakrishnan

Nachimuthu

2021

J. Inst. Eng. India Ser. C

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“…The MPC design was done based on the design given in Minouchehr and Hosseini-Sani. 22 The angular position and angular velocity were constrained to 0.1° and 10 r/min, respectively. The control and prediction horizons were considered as 40, the sampling time was 0.01 s, and the weighting symmetric matrices were selected as …”

Section: Evaluation and Simulationmentioning

confidence: 99%

A MATLAB-based virtual robotics laboratory: Demonstrated by a two-wheeled inverted pendulum

Ghayoor

2018

International Journal of Electrical Engineering & Education

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In this paper, a procedure for designing a MATLAB-based virtual robotics laboratory is described that provides a realistic three-dimensional animation for the robot’s motion. The proposed laboratory allows students to obtain the linear model and stability condition of a robotic system, without getting involved in its mathematical modeling. The structure of a two-wheeled inverted pendulum robot has been used for demonstrating the laboratory’s features. As part of this virtual experiment, design and evaluation of proportional–integral–derivative, model predictive control, and fuzzy controllers for maintaining the two-wheeled inverted pendulum’s balance are considered. A set of graphical–user interfaces is designed for providing the interaction with the system. This virtual lab is readily extensible for other robotics structures and controllers, and its output data is promptly available for further analysis by other MATLAB toolboxes.

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“…Fiacchini et al [2] proposed linear and nonlinear controllers for stabilizing a personal pendulum vehicle. To compensate for the measurable disturbances, the work in [3] compared the performance of Model Predictive Controller and LQR. Multipoint pole placement control for velocity tracking of the TWIP is shown in [4].…”

Section: Introductionmentioning

confidence: 99%

Velocity control of a two-wheeled inverted pendulum mobile robot: a fuzzy model-based approach

Muhammad¹,

Bature²,

Zangina³

et al. 2019

Bulletin EEI

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This paper presents the design of a fuzzy tracking controller for balancing and velocity control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot based on its Takagi-Sugino (T-S) fuzzy model, fuzzy Lyapunov function and non-parallel distributed compensation (non-PDC) control law. The T-S fuzzy model of the TWIP mobile robot was developed from its nonlinear dynamical equations of motion. Stabilization conditions in a form of linear matrix inequalities (LMIs) were derived based on the T-S fuzzy model of the TWIP mobile robot, a fuzzy Lyapunov function and a non-PDC control law. Based on the derived stabilization conditions and the T-S fuzzy model of the TWIP mobile robot, a state feedback velocity tracking controller was then proposed for the TWIP mobile robot. The balancing and velocity tracking performance of the proposed controller was investigated via simulations. The simulation result shows the effectiveness of the proposed control scheme.

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Design of Model Predictive Control of two-wheeled inverted pendulum robot

Cited by 13 publications

References 13 publications

Design of State Feedback LQR Based Dual Mode Fractional-Order PID Controller using Inertia Weighted PSO Algorithm: For Control of an Underactuated System

Design of State Feedback LQR Based Dual Mode Fractional-Order PID Controller using Inertia Weighted PSO Algorithm: For Control of an Underactuated System

A MATLAB-based virtual robotics laboratory: Demonstrated by a two-wheeled inverted pendulum

Velocity control of a two-wheeled inverted pendulum mobile robot: a fuzzy model-based approach

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