Dual-Mode Model Predictive Control of an Omnidirectional Wheeled Inverted Pendulum

Watson, Matthew T.; Gladwin, Daniel T.; Prescott, Tony J.; Conran, Sebastian

doi:10.1109/tmech.2019.2943708

Cited by 20 publications

(7 citation statements)

References 18 publications

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“…There have been many studies to solve this robot equilibrium and motion control problem using various control techniques in the literature. Nonlinear control structures with different analyses and designs [10], [11] dual-mode model predictive control [12], vision-based adaptive control [13], sliding mode control [14], adaptive fuzzy control [15], Takagi-Sugeno type Fuzzy Logic Control (FLC) [16], interval Type-2 FLC [17], semiconcave Control Lyapunov Function (CLF) [18] and advanced interval Type-2 Fuzzy sliding mode control [19] are some of them. Over the past decades, Machine Learning (ML) and Fuzzy Logic (FL) based intelligent control systems have been a dominant topic in research, robotics or control societies.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Load

Ünlütürk

Aydoğdu

2022

IEEE Access

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In this paper, a novel Machine Learning (ML) based Adaptive Fuzzy Logic-Proportional Integral (AFL-PI) controller was developed for the self-balancing and precision motion control of a two wheeled Underactuated-Mobile Inverted Pendulum (U-MIP) under variable payloads. One of the external disturbances in balance and movement control of the U-MIP is the amount of payload it carries on. To investigate the effectiveness of the proposed controller, a load bar was mounted on top of the U-MIP. The weights of 55gr each can be attached to this bar for variable payloads. The weights on the bar were labeled as three different classes: Low Load (LL), Normal Load (NL) and Heavy Load (HL). Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) models were tested to obtain the highest load class estimation. The highest load classification accuracy was achieved with ANN. Therefore, the ANN model was applied on the U-MIP. The balance performance of the U-MIP was compared by applying the classical FL-PI and ANN based AFL-PI controller on the robot. In order to compare the body tilt angle performance of the U-MIP, the optimal FL-PI parameter in LL was applied for NL and HL conditions without changing. Then, the proposed ANN-based AFL-PI controller was implemented on U-MIP. With the proposed controller, the body tilt angle variation of the U-MIP was improved by %29.42 for NL and %55.62 for HL compared to the classical FL-PI controller. The validity of the proposed controller was proved by real experiments.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Load

Ünlütürk

Aydoğdu

2022

IEEE Access

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“…Prior to this work only a simple dynamics model of the CMD has been derived [2], and no effort has been made to analyse the controllability or dynamical properties of this novel locomotion system. The CMD has also been shown to be approximately differentially flat [3], allowing for computationally efficient trajectory planning.…”

Section: Introductionmentioning

confidence: 99%

“…Excluding the unactuated Mecanum wheel rollers, as compared to a typical moving part within a robot these are very simple and low cost 2. Despite requiring only a minimum of three wheels, a four-wheeled configuration is chosen for the prototype in Fig.1in order to simplify suspension design.…”

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confidence: 99%

Collinear Mecanum Drive: Modeling, Analysis, Partial Feedback Linearization, and Nonlinear Control

2021

Self Cite

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The Collinear Mecanum Drive (CMD) is a novel robot locomotion system, capable of generating omnidirectional motion whilst simultaneously dynamically balancing, achieved using a collinear arrangement of three or more Mecanum wheels. The CMD has a significantly thinner ground footprint than existing omnidirectional locomotion methods, which does not need to be enlarged with increasing robot height as to avoid toppling during acceleration or external disturbance. This combination of omnidirectional manoeuvrability and a thin ground footprint allows for the creation of tall robots that are able to navigate through much narrower gaps between obstacles than existing omnidirectional locomotion methods. This allows for greater manoeuvrability in confined and cluttered environments, such as that encountered in the personal service and automated warehousing robotics sectors.This article derives the kinematics and dynamics models of the CMD, analyses controllability and accessibility, and determines the degree to which a CMD can be linearised by feedback. A partial feedback linearisation is then performed, and three practically useful nonlinear controllers are derived using a backstepping design approach, all with convergence and stability guarantees for the fully-coupled nonlinear model. These are demonstrated both in simulation and on a real-world CMD experimental prototype.

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“…In addition, researches involving the off-line robust model predictive control (RMPC) via linear matrix inequalities (LMIs) have increased in the last years (Costa et al, 2017;Zheng et al, 2018;Moradi et al, 2018;Hu and Ding, 2018). Besides, studies of the RMPC applied to inverted pendulum could be highlighted in this context (Yue et al, 2018;Jung and Wen, 2004;Nam et al, 2019;Watson et al, 2019). Moreover, Park et al (2011), Bumroongsri (2014), Longge and Yan (2017), and Ping (2017) presented applications of RMPC-LPV in their research fields, emphasizing the efficiency of this method as robust control design.…”

Section: Introductionmentioning

confidence: 99%

An off-line output feedback RMPC-LPV applied to an inverted pendulum using relaxed LMI procedures

Carvalho

Costa

Lima

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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This paper aims an off-line output-feedback robust model predictive control (RMPC) using linear matrix inequalities (LMIs) applied to the angle position control of an inverted pendulum modeled by Linear Parameters Varying (LPV) affine scheme. The presented methodology involves the an off-line RMPC-LPV state feedback, which the gains are LPV byLMIs and stored in the look-up table. Also, it is presented a robust observer design using LMIs that is ensured the feasibility of the output feedback stability. The control strategies presented in this study considers the online and off-line state space feedback and off-line feedback control design along with state observer. The comparative analysis of the numerical results and cost indices evidence the suitability of the proposed methodology and the advantages of output feedback RMPC-LPV in comparison with the typical approaches based in the same control design conditions.

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Dual-Mode Model Predictive Control of an Omnidirectional Wheeled Inverted Pendulum

Abstract: This is a repository copy of Dual-mode model predictive control of an omnidirectional wheeled inverted pendulum.

Cited by 20 publications

References 18 publications

Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Load

Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum With Variable Load

Collinear Mecanum Drive: Modeling, Analysis, Partial Feedback Linearization, and Nonlinear Control

An off-line output feedback RMPC-LPV applied to an inverted pendulum using relaxed LMI procedures

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