Fuzzy Integral Sliding Mode Observer-Based Formation Control of Mobile Robots With Kinematic Disturbance and Unknown Leader and Follower Velocities

Boo, Junseok; Chwa, Dongkyoung

doi:10.1109/access.2022.3192839

Cited by 4 publications

(5 citation statements)

References 43 publications

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“…where F dist ij and F obs ij represent the environmental characteristics of the input feature, A k and B k represent the degree of membership of each characteristic, and Output result represents the output, which is the position evaluation level. The initial rule base for this design, based on the characteristics of the sliding mode [46], [47], [48], [49], [50], is shown in Table 4.…”

Section: ) Decision Making Logicmentioning

confidence: 99%

Adaptive Fuzzy Inference Decision Strategy for Service Robots Tidying Up Objects

Liu

et al. 2023

IEEE Access

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Tidying up objects poses a complex challenge for service robots, particularly when it involves scheduling operations that require real-time action for achieving optimal results. This paper presents a decision system aimed at selecting priority locations to address the problem of robots searching for miscellaneous items in indoor environments. Initially, three datasets with varying complexities are generated using real environmental data, and the environmental information is integrated into feature matrices. Subsequently, the rule base of the fuzzy inference system is trained and optimized using an equilibrium optimizer, and its performance is compared with other commonly used algorithms. The feature matrices and the optimized rule base are then incorporated into the fuzzy inference system, leveraging the traveling salesman problem to determine the optimal sequence for visiting locations. The accuracy and efficiency of the proposed method are validated through physical implementation tests using analog data. Finally, the system is applied and evaluated in real-world scenarios to validate its effectiveness. The video of experiment is available at https://youtu.be/rjMDgopM-9M.INDEX TERMS Autonomous mobile robot, equilibrium optimizer, fuzzy inference system, tidying up object problem, travelling salesman problem.

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Section: ) Decision Making Logicmentioning

confidence: 99%

Adaptive Fuzzy Inference Decision Strategy for Service Robots Tidying Up Objects

Liu

et al. 2023

IEEE Access

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“…Fuzzy logic control (FLC) presents an advantageous alternative in these circumstances because it limits the inputs within the range of the predefined membership functions as demonstrated in refs. [25][26][27][28][29][30][31][32][33][34][35]. The membership functions are typically designed to overlap with one another.…”

Section: Introductionmentioning

confidence: 99%

“…This approach provides interpolative reasoning capabilities as highlighted in refs. [25][26][27][28] where the FLC shows a high resilience against uncertainties and disturbances. Furthermore, as indicated in refs.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, unlike prior works in refs. [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] which completely rely on designers' expert knowledge to relate the fuzzy inputs and outputs, this study avoids complete dependency on expert knowledge by utilizing the gathered knowledge of an RL agent after extensive training in a simulated environment.…”

Section: Introductionmentioning

confidence: 99%

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An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

Zaman,

2024

Robotica

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In this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.

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“…It uses an approximation model to generate an approximation function to control the system. Some traditional approximation control models are adaptive neural network control [18], adaptive control with mobile robot [19][20]. This approach provides a flexible solution for complex systems that does not require fully precise modeling as the model-driven control or relies on complex optimization processes.…”

Section: Introductionmentioning

confidence: 99%

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Ngo,

Tran,

et al. 2023

Journal of Robotics and Control

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Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems.

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Fuzzy Integral Sliding Mode Observer-Based Formation Control of Mobile Robots With Kinematic Disturbance and Unknown Leader and Follower Velocities

Cited by 4 publications

References 43 publications

Adaptive Fuzzy Inference Decision Strategy for Service Robots Tidying Up Objects

Adaptive Fuzzy Inference Decision Strategy for Service Robots Tidying Up Objects

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

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