Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator, the mul...

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Bibliographic Details
Published inIET control theory & applications Vol. 16; no. 1; pp. 7 - 19
Main Authors Sun, Yiyong, Zhao, Haotian, Chen, Zhang, Zheng, Xudong, Zhao, Mingguo, Liang, Bin
Format Journal Article
LanguageEnglish
Published Stevenage John Wiley & Sons, Inc 01.01.2022
Wiley
Subjects
Actuators
Algorithms
Bicycles
Control system analysis and synthesis methods
Controllers
Dynamic programming
Fuzzy control
Interpolation and function approximation (numerical analysis)
Mechanical variables control
Mobile robots
Optimisation techniques
Optimization
Particle swarm optimization
Robot control
Robot dynamics
Robots
Stability analysis
Steering
Transportation system control
Velocity
Wheels
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ISSN1751-8644
1751-8652
1751-8652
DOI10.1049/cth2.12199

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Summary:Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator, the multiple objective optimal balance control issue is studied here. A fuzzy state‐space model under different forward velocities is firstly offered based on the non‐linear Euler–Lagrange model. Based on this, a closed‐loop equation under a fuzzy controller is verified. To regulate the feedback gain of the fuzzy controller, a modified particle swarm optimization (MPSO) algorithm with two stages is proposed. In the MPSO's second stage, a novel objective fitness function, consisting of multiple objectives and combining the conventional Hurwitz stability analysis criterium, is designed. Procedures for the MPSO dynamic programming approach are presented. By two examples, the efficiency of the MPSO algorithm, for time‐varying and time‐constant velocity situations, and faster capacity for iteration convergence, are examined.
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ISSN:1751-8644
1751-8652
1751-8652
DOI:10.1049/cth2.12199