Trajectory tracking control for rotary steerable systems using interval type-2 fuzzy logic and reinforcement learning

Zhang, Chi; Zou, Wei; Cheng, Ningbo; Gao, Junshan

doi:10.1016/j.jfranklin.2017.12.001

Cited by 32 publications

(6 citation statements)

References 40 publications

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“…Kong et al [26] introduced an approximate optimal strategy to resolve the non-linearity saturation problem of n-DOF manipulators. In [27], an adaptive fuzzy neural network control method with impedance learning was presented for robots with constraints. In [28], the optimal coordination control which was applied to multi-robots to follow expected trajectories was presented by means of reinforcement learning.…”

Section: Related Workmentioning

confidence: 99%

Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

Ren

2021

Complex Intell. Syst.

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This paper investigates a feature tracking control method for visual servoing (VS) manipulators adaptive dynamic programming (ADP)-based the unknown dynamics. The major superiority of ADP-based optimal control lies in that the visual tracking problem is converted to the feature tracking error control with optimal cost function. Moreover, an adaptive neural network observer is developed to approximate the entire uncertainties, which are utilized to construct an improved cost function. By establishing a critic neural network, the Hamilton–Jacobi–Bellman (HJB) equation is solved, and the approximate optimal error control policy is derived. The closed-loop VS manipulator system is verified to be ultimately uniformly bounded with the developed ADP-based feature tracking control strategy according to the Lyapunov theory. Finally, simulation results under various situations demonstrate that the proposed method achieves higher tracking accuracy than other methods, as well as satisfies energy optimal requirements.

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Section: Related Workmentioning

confidence: 99%

Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

Ren

2021

Complex Intell. Syst.

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“…In recent years, the development of artificial intelligence technology is getting faster and faster, so the research on the intelligent control methods of isolated intersection signals is also increasing. Among them, fuzzy control is very popular in the field of traffic control [2] because it does not rely on the mathematical model of the controlled system. J. Guo proposed a particle swarm optimization to reduce vehicle delays based on Akcelik delay model [3] .…”

Section: Establishment Of Four Phase Isolated Intersection Modelmentioning

confidence: 99%

Signal Adaptive Control of Isolated Intersection Based on Type-Two Fuzzy Control

Ma¹,

Chen²,

Wang³

2021

dtcse

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In this paper, the signal control problem of isolated intersection during peak period is studied based on the type-two fuzzy control method to reduce the vehicle delay of isolated intersection. Firstly, the traffic flow model and evaluation index model of isolated intersection are established, and the factors of saturation flow rate and lane length are fully considered. In order to relieve the traffic pressure effectively, a type-two fuzzy controller is proposed for the signal control method, which solves the coordination and dynamic uncertainty problems in the traffic of isolated intersection. By using adaptive genetic algorithm to optimize the parameters of membership function in the type-two fuzzy controller, the parameters of the type-two fuzzy controller can be adjusted in real time according to the change of traffic flow, so that the controller can achieve adaptive control effect of traffic signals. At last, the simulation results show that the type-two fuzzy controller designed in this chapter has a better control effect in the peak period of traffic flow and reduces the vehicle delay of isolated intersection.

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“…us, a specific order condition is considered when the phase is selected in this paper. A type-2 fuzzy signal controller is introduced [15][16][17]. is controller can deal with the uncertainty of the traffic model effectively, but it is difficult to select suitable parameters of membership functions and fuzzy rules are difficult.…”

Section: Introductionmentioning

confidence: 99%

Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme

Jiang

Wang

Chen

2021

Mathematical Problems in Engineering

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This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly.

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Trajectory tracking control for rotary steerable systems using interval type-2 fuzzy logic and reinforcement learning

Cited by 32 publications

References 40 publications

Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

Signal Adaptive Control of Isolated Intersection Based on Type-Two Fuzzy Control

Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme

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