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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Published in:IET 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
Language:English
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
ISSN:1751-8644, 1751-8652
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Abstract 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.
AbstractList Abstract 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.
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.
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.
Author Zhao, Mingguo
Liang, Bin
Zhao, Haotian
Sun, Yiyong
Chen, Zhang
Zheng, Xudong
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  surname: Liang
  fullname: Liang, Bin
  organization: Tsinghua University
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Snippet Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be...
Abstract Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot...
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SubjectTerms 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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Title Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot
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