Visual Feedback Balance Control of a Robot Manipulator and Ball-Beam System

Shih, Ching‐Long; Hsu, Jung-Hsien; Chang, Chee Jen

doi:10.4236/jcc.2017.59002

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Taking a slightly different approach ( Shih et al, 2017 ) developed an embedded PID/PD controller for a ball-beam system. The control structure consisted of a PD controller to choose the desired platform response and then a series of individual PID controllers on each motor to realize the idealized platform response by actuation of the motors.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Applying Deep Reinforcement Learning to Cable Driven Parallel Robots for Balancing Unstable Loads: A Ball Case Study

Grimshaw

Oyekan

2021

Front. Robot. AI

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The current pandemic has highlighted the need for rapid construction of structures to treat patients and ensure manufacturing of health care products such as vaccines. In order to achieve this, rapid transportation of construction materials from staging area to deposition is needed. In the future, this could be achieved through automated construction sites that make use of robots. Toward this, in this paper a cable driven parallel manipulator (CDPM) is designed and built to balance a highly unstable load, a ball plate system. The system consists of eight cables attached to the end effector plate that can be extended or retracted to actuate movement of the plate. The hardware for the system was designed and built utilizing modern manufacturing processes. A camera system was designed using image recognition to identify the ball pose on the plate. The hardware was used to inform the development of a control system consisting of a reinforcement-learning trained neural network controller that outputs the desired platform response. A nested PID controller for each motor attached to each cable was used to realize the desired response. For the neural network controller, three different model structures were compared to assess the impact of varying model complexity. It was seen that less complex structures resulted in a slower response that was less flexible and more complex structures output a high frequency oscillation of the actuation signal resulting in an unresponsive system. It was concluded that the system showed promise for future development with the potential to improve on the state of the art.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Applying Deep Reinforcement Learning to Cable Driven Parallel Robots for Balancing Unstable Loads: A Ball Case Study

Grimshaw

Oyekan

2021

Front. Robot. AI

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“…The system's inherent complexity, driven by factors such as saturation characteristics of its actuators, constraints posed by the direct current (DC) motor, and discontinuities in position measurements, mirrors the realworld challenges commonly faced in underactuated nonlinear systems [1]. Designing controls for such systems presents substantial challenges due to their unpredictable and dynamic characteristics [2], [3]. Practical applications of the BnB system include robotic load balancing [4]- [7], attitude control in space vehicles [8], [9], nonlinear control of actuators [10], and gyroscopic stabilization systems [11].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Ahmad

2023

IEEE Access

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The ball and beam (BnB) system serves as a benchmark in control engineering as it provides a foundational concept applicable to addressing stabilization challenges of various underactuated nonlinear systems. This includes tasks like maintaining the balance of goods carried by mobile robots and controlling the attitude of unmanned aerial vehicles. In this study, the focus is on enhancing control optimization strategies for BnB systems that take into account inherent nonlinearities arising from actuator constraints and state measurements. The work introduces a novel intelligent control approach, termed hybrid PSO-WOA, which combines Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) to automate optimal parameter search for proportional-integral-derivative (PID) and state feedback (SF) controllers. The collaborative technique between PSO and WOA is formulated to strike a balance between exploration and exploitation phases, and to mitigate premature convergence risks due to the system's complexities. Additionally, three control schemes, namely cascade PID-PID, cascade PID-SF, and cascade PID-observer are introduced, each with tailored cost functions for optimization through the hybrid PSO-WOA algorithm, accommodating both measurable and unmeasurable state scenarios. Simulation results consistently demonstrate the superior performance of the hybrid approach compared to individual PSO and WOA methods, as well as conventional PID and linear quadratic regulator approaches. Notable, the hybrid approach exhibits a significant improvement in error metrics, reducing integral-time absolute error by 18.99%, integral squared error by 35.37%, and steady-state error by 92.86%. This substantial enhancement suggests promising directions for future research in automated control parameter tuning for underactuated nonlinear systems.

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Improvement in dielectric properties of the three-phase GN–BaTiO3–PEK nanocomposites with and without silane coupling agent

Goyal

Tamhane

Tambat

2021

J Mater Sci: Mater Electron

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Visual Feedback Balance Control of a Robot Manipulator and Ball-Beam System

Cited by 6 publications

References 12 publications

Applying Deep Reinforcement Learning to Cable Driven Parallel Robots for Balancing Unstable Loads: A Ball Case Study

Applying Deep Reinforcement Learning to Cable Driven Parallel Robots for Balancing Unstable Loads: A Ball Case Study

Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

Improvement in dielectric properties of the three-phase GN–BaTiO3–PEK nanocomposites with and without silane coupling agent

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