Robust Optimal Navigation Using Nonlinear Model Predictive Control Method Combined with Recurrent Fuzzy Neural Network

Zhu, Qidan; Han, Yang; Cai, Chengtao; Xiao, Yao

doi:10.1155/2018/8014019

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…Both learn non-linear robot dynamics models from the robot’s interactions with terrains. Closed-loop feedback control, such as the Model Predictive Control (MPC) (Ostafew et al, 2016; Zhu et al, 2018) and Model Predictive Path Integral (MPPI) (Williams et al, 2016), were also designed to improve control robustness to terrain noise and robot model error.…”

Section: Related Workmentioning

confidence: 99%

Self-reflective terrain-aware robot adaptation for consistent off-road ground navigation

Siva,

Wigness,

Rogers

et al. 2024

The International Journal of Robotics Research

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Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot’s actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method’s performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.

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Section: Related Workmentioning

confidence: 99%

Self-reflective terrain-aware robot adaptation for consistent off-road ground navigation

Siva,

Wigness,

Rogers

et al. 2024

The International Journal of Robotics Research

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“…e principal fuzzy systems are Mandani and Takagi-Sugeno. In particular, the Mandani systems use various techniques that allow fuzzy set membership function tuning, such as genetic algorithms (GA) [16], adaptive neural networks [17,18], artificial bee colony optimization [19][20][21], ant colony optimization [22][23][24], and evolutionary algorithms [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Optimal Position Fuzzy Control of an Underactuated Robotic Finger

Espinosa-Garcia

Lugo-González

Téllez-Velázquez

et al. 2022

Mathematical Problems in Engineering

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In this paper, the optimal position control of an underactuated robotic finger is presented. Two trajectories, one for the proximal and the other for the medial phalanx, are proposed in order to emulate the finger’s flexion/extension movements. A Mandani fuzzy control is proposed due to the lack of a precise dynamical model of the system. In order to obtain the control parameters, an optimization strategy based on the membership functions is applied. Genetic algorithms (GA) are commonly used as an optimization method in diverse applications; however, in this case, the use of an autoadaptive differential evolution method is proposed in order to obtain a superior convergence behavior. Simulations of the virtual prototype are carried out using MATLAB/Simulink software to display the trajectory tracking. The results show that the maximum error between the proposed and obtained trajectories is 3.1352E − 04 rad.

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“…Using the linear and non-linear characteristics of the neural network, the static, dynamic and prediction models of linear and non-linear systems can be established, and the system modeling and identification can be achieved [20]- [22]. Neural networks can also be used as classifiers in the field of image analysis [23].…”

Section: Introductionmentioning

confidence: 99%

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

2021

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In this paper, a fractional-order sliding mode control method based on a double recurrent perturbation fuzzy neural network (DRPFNN) is proposed for a micro gyroscope system with parameter uncertainty and external disturbance. The DRPFNN is used to realize the adaptive estimation of the unknown part, which ensures that the controller is independent of the accurate mathematical model. The sine-cosine perturbation membership function is used to deal with the uncertainty of rules in neural network, increasing the accuracy, reduce the calculation load, and simplifying computational complexity. In addition, double recurrent links are added to transmit more information with superior dynamic ability. Then, the proposed control system is composed of a DRPFNN estimator and a fractional-order sliding mode controller (FSMC). The fractional calculus operator is introduced into the sliding surface to improve the controller flexibility. The parameter adaptive laws in the neural network are designed to be adaptively stabilized to the optimal value. Finally, simulation studies verify the effectiveness of the proposed control method, showing it can obtain higher control accuracy and enhance robustness than the traditional sliding mode control method. INDEX TERMSDouble recurrent perturbation fuzzy neural network; Recurrent neural network; Sliding mode control; Fractional order; Micro gyroscope.

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Robust Optimal Navigation Using Nonlinear Model Predictive Control Method Combined with Recurrent Fuzzy Neural Network

Cited by 11 publications

References 33 publications

Self-reflective terrain-aware robot adaptation for consistent off-road ground navigation

Self-reflective terrain-aware robot adaptation for consistent off-road ground navigation

Optimal Position Fuzzy Control of an Underactuated Robotic Finger

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

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