Model Predictive Fault Tolerant Control for Omni-directional Mobile Robots

Karras, George C.; Fourlas, George K.

doi:10.1007/s10846-019-01029-7

Cited by 32 publications

(30 citation statements)

References 20 publications

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“…Researchers [ 26 , 27 ] proposed trajectory tracking control of a four-wheeled omnidirectional mobile robot based on the reference model approach.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Dumitrescu

Ciotîrnae

Vizitiu

2021

Sensors

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When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

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“…Researchers [ 26 , 27 ] proposed trajectory tracking control of a four-wheeled omnidirectional mobile robot based on the reference model approach.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Dumitrescu

Ciotîrnae

Vizitiu

2021

Sensors

View full text Add to dashboard Cite

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“…A lot of technologies are implemented in various applications [1,2], e.g., mobile robots have the ability to perform specific tasks in an industrial work environment, homes, hospitals, among others [3]. Some representative mobile robots include household cleaning robots, military surveillance drones, warehouse robots and autonomous robots [4,5]. The latter have become indispensable components in many applications, including research, warehouse management, surveillance and safety, and autonomous vehicles [6,7].…”

Section: Introductionmentioning

confidence: 99%

Control of an Omnidirectional Robot Based on the Kinematic and Dynamic Model

Gallo

Paste

Ortiz

et al. 2021

Communications in Computer and Information Science

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This work focuses on the proposal for the implementation and evaluation of control algorithms for the monitoring of autonomous trajectories based on the kinematic and dynamic model of an omnidirectional robotic platform in four wheel configuration type mecanum also built for the process of identification and validation of the dynamic parameters obtained from the mathematical model by means of an identification algorithm, the evaluation of these control algorithms is carried out experimentally on the Robotic Platform in real four-wheel omnidirectional configuration with which it also allows to evaluate the operation of the Hardware in the Loop control scheme (HIL), a technique that constitutes the untimely connection of external hardware with computer equipment allowing to simulate in real time the behavior of the robotic system with omnidirectional traction in four-wheel configuration type mecanum. Subsequently the data resulting from the Hardware in the Loop (HIL) control scheme will be compared to the data obtained in the experimental test for corresponding validation.

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“…Event-Triggered Active Fault-Tolerant Control System is proposed in [12], based on event-triggered transmission mechanism to reduce the amount of data sent to the observer module. The attractive fault-tolerant control scheme for an omni-directional mobile robot with four mecanum wheels, based on the integration of Nonlinear Model Predictive Control module and basic Fault Detection and Isolation scheme, is proposed in [13]. Thrust Distribution Control System for ROVs, based on adaptive back-stepping controller, is proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the edge 13 is determined by vertices 1 and 3. The binary representations of 1 (001) and 3 (011) differ in the second bit, which means that free control is u 13 2 , while fixed controls are u 13 1 = u 1 = −1 and u 13 3 = u 3 = +1. Once the fixed and free controls for i j are obtained, it is easy to find coordinates v ij 1 and v ij 2 of P ij from (24) by removing the equation that corresponds to free control and replacing the right-hand sides of other equations with corresponding limits for fixed controls.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Geometric Insight into the Control Allocation Problem for Open-Frame ROVs and Visualisation of Solution

et al. 2020

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The overall control system for an open-frame Remotely Operated Vehicle (ROV) is typically built from three subsystems: guidance, navigation and control (GNC). The control allocation plays a vital role in the control subsystem. Typically, open-frame underwater vehicles have p actuators (thrusters) for the motion in the horizontal plane, and the control allocation problem, in this case, is very complex and hard to visualise, because the normalised constrained control subset is a p-dimensional unit cube. The aim of this paper is to give a clear picture and a geometric interpretation of the problem and to introduce a hybrid method, based on the integration of a weighted pseudoinverse and the fixed-point method. The main idea of the hybrid method is visualised, and the deep geometric insight is provided using a “virtual” ROV in low-dimensional control spaces, including visualisation of the attainable command set, solution lines, control energy spheres and the role of pseudoinverse and fixed-point iterations. The same concepts are then extended to higher-dimensional cases, for open-frame ROV with four X-shaped (vectored) horizontal thrusters, which is one of the most common thruster configurations for commercial ROVs. The proposed hybrid method has been developed, integrated into a generic fault-tolerant ROV control system and evaluated in virtual and real-world environments off the west coast of Ireland using observation-class ROV Latis and work-class ROV Étaín.

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Model Predictive Fault Tolerant Control for Omni-directional Mobile Robots

Cited by 32 publications

References 20 publications

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Control of an Omnidirectional Robot Based on the Kinematic and Dynamic Model

Geometric Insight into the Control Allocation Problem for Open-Frame ROVs and Visualisation of Solution

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