Modelling and Simulation Analysis of Rolling Motion of Spherical Robot

Kamis, Nurul Nafisah; Embong, Abd Halim; Ahmad, Salmiah

doi:10.1088/1757-899x/260/1/012014

Cited by 3 publications

(1 citation statement)

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“…The fuzzy controller enhanced the system's adaptability to nonlinear characteristics and uncertainties, resulting in superior control performance compared to traditional proportion-differentiation (PD) control. Kamis designed a fuzzy PD controller to address the trajectory tracking issue of a spherical robot [25]. The controller uses fuzzy logic to adjust the parameters of the PD controller, which effectively eliminates the overshoot and improves the stability time, thus ensuring more accurate control of the robot's position.…”

Section: Introductionmentioning

confidence: 99%

Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning

Cao³

et al. 2023

Applied Sciences

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As deep space exploration tasks become increasingly complex, the mobility and adaptability of traditional wheeled or tracked probe robots with high functional density are constrained in harsh, dangerous, or unknown environments. A practical solution to these challenges is designing a probe robot for preliminary exploration in unknown areas, which is characterized by robust adaptability, simple structure, light weight, and minimal volume. Compared to the traditional deep space probe robot, the spherical robot with a geometric, symmetrical structure shows better adaptability to the complex ground environment. Considering the uncertain detection environment, the spherical robot should brake rapidly after jumping to avoid reentering obstacles. Moreover, since it is equipped with optical modules for deep space exploration missions, the spherical robot must maintain motion stability during the rolling process to ensure the quality of photos and videos captured. However, due to the nonlinear coupling and parameter uncertainty of the spherical robot, it is tedious to adjust controller parameters. Moreover, the adaptability of controllers with fixed parameters is limited. This paper proposes an adaptive proportion–integration–differentiation (PID) control method based on reinforcement learning for the multi-motion mode spherical probe robot (MMSPR) with rolling and jumping. This method uses the soft actor–critic (SAC) algorithm to adjust the parameters of the PID controller and introduces a switching control strategy to reduce static error. As the simulation results show, this method can facilitate the MMSPR’s convergence within 0.02 s regarding motion stability. In addition, in terms of braking, it enables an MMSPR with random initial speed brake within a convergence time of 0.045 s and a displacement of 0.0013 m. Compared with the PID method with fixed parameters, the braking displacement of the MMSPR is reduced by about 38%, and the convergence time is reduced by about 20%, showing better universality and adaptability.

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Section: Introductionmentioning

confidence: 99%

Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning

Cao³

et al. 2023

Applied Sciences

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Velocity Control for Spherical Robot using PI-Fuzzy Logic

Kamis

Embong

Ahmad

2019

2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

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This paper presents the finding on designing the fuzzy logic controller and analysis of the step input test on the model and control design. The PI-type fuzzy logic controller (FLC) was designed to control the velocity of the rolling spherical robot. The spherical robot model was tested with step input signal to analysis the effectiveness of the designed Fuzzy logic controller with 25-membership rule being constructed in Fuzzy toolbox MATLAB using triangular membership function and combination of gaussian and sigmoidal membership function. The output scaling factor (output gain) of FLC was tuned using Response Optimization toolbox and Particle Swarm Optimization to improve the system performance. Optimization using Matlab toolbox is done by specified the desired step response characteristics while in PSO, minimizing the Integral Absolute Error (IAE) is used as on objective of the optimization. The combined membership function shows better performance with less 8% overshoot, rise time less than 2s and settle at less than 3s after the response optimization process. Meanwhile, the PSO manage to tune the gain to reduce the IAE but contain large overshoot and longer settling time.

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