Article
Computer Science, Information Systems
Yi-Jen Mon
Summary: This paper introduces the concept of intelligent control using Tikhonov regularization for nonlinear coupled systems. It explores the impact of Tikhonov regularization on these systems and aims to enhance intelligent control by determining the optimal regularization term and integrating it with other control methods. The proposed Tikhonov-tuned sliding neural network (TSN) controller ensures both stability and superior system performance. The results indicate that the proposed TSN methodologies are effective and feasible.
Article
Automation & Control Systems
Abhishek Kumar Kashyap, Dayal R. Parhi
Summary: The study focuses on gait planning for a humanoid robot NAO using the LIPM model and a PID controller tuned with PSO technique. The results show that applying the PSO tuned PID controller provides a predictable gait, reduces stabilization time, and essentially eliminates overshoot by 25%. Comparisons with other controllers and statistical analysis support the credibility of the proposed controller.
Article
Engineering, Mechanical
Sunil Gora, Shakti S. Gupta, Ashish Dutta
Summary: In this work, the motion of a nonlinear inverted pendulum (NIP) on deformable terrain due to foot contact forces is analyzed for energy and footstep placement. The mass moment of inertia and terrain deformation are taken into consideration in the model. The system's energy analysis provides foot placement regions and terrain stiffness limits for walking on uneven deformable terrain.
JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME
(2023)
Article
Engineering, Mechanical
Dayal R. Vikas, Dayal R. Parhi
Summary: A memory-based gravitational search algorithm with an evolutionary learning strategy is proposed for globally optimal collision-free path planning. The approach shows improved trajectory and fast convergence compared to other path-planning approaches.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2023)
Article
Robotics
Kohei Kimura, Kei Okada, Masayuki Inaba
Summary: This paper presents an extended balance stabilization control approach for humanoid robot on rotational slope, using the seesaw-inverted-pendulum model to stabilize the balance of both the humanoid robot and the rotational slope. Experiment results verify the effectiveness of the proposed approach on wobble floor and unicycle Segway.
Article
Chemistry, Analytical
Long Li, Zhongqu Xie, Xiang Luo, Juanjuan Li
Summary: This study introduces a linear pendulum model (LPM) for the double support phase (DSP) to address the walking stability issue caused by the neglect of DSP in the LIPM model. Additionally, various trajectory-planning methods for different scenarios are proposed to allow the biped robot to maintain stability while planning trajectories in real time.
Article
Robotics
Abhishek Kumar Kashyap, Dayal R. Parhi
Summary: This paper proposes a state-of-the-art simplified model, considering the vertical fluctuations of the center of mass and upper body movement for stable bipedal walking. The PID controller is tuned using converged teaching-learning based optimization technique, resulting in a feasible and energy-efficient gait pattern for the humanoid robot.
INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS
(2023)
Article
Chemistry, Multidisciplinary
Feng Qu, Wentao Yu, Kui Xiao, Chaofan Liu, Weirong Liu
Summary: This paper proposes a hybrid scheme using mutual learning and adaptive ant colony optimization (MuL-ACO) for trajectory generation and optimization of mobile robots in complex and uneven environments. The proposed scheme utilizes a 2D-H map to describe the uneven environment, and incorporates adaptive ant colony algorithm based on simulated annealing (SA) and mutual learning algorithm to generate collision-free trajectories with high comprehensive quality. Experimental results demonstrate the effectiveness of the proposed scheme in uneven environments.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Mechanical
Duy-Chinh Nguyen
Summary: This article discusses controlling the oscillation of a pendulum structure excited by the hanging point using an inverted pendulum-type tuned mass damper, with optimal parameters determined in clear analytical solutions. This provides scientists with an easier way to determine the best parameters to suppress oscillation in the pendulum structure.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART K-JOURNAL OF MULTI-BODY DYNAMICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Binbin Tian, Hui Peng
Summary: This paper introduces an event-triggered predictive control method based on the RBF-ARX model. By combining the data-driven RBF-ARX model into the ETMPC method, the issue of accumulating computational burden is addressed. A new event-triggering condition is proposed to apply ETMPC to complex industrial processes, and the stability analysis of RBF-ARX model-based ETMPC is conducted. Simulation results show that the proposed method significantly reduces computational burden while maintaining the performance of ETMPC similar to that of MPC.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Information Systems
Arnaldo de Carvalho, Joao Francisco Justo, Bruno Augusto Angelico, Alexandre Manicoba de Oliveira, Joao Inacio da Silva Filho
Summary: Artificial neural networks have been widely used in various fields over the past few decades, but they may face challenges when dealing with uncertain situations. The family of paraconsistent logics, as a powerful tool for handling uncertainty and contradictory information, has attracted attention from researchers in the field of artificial intelligence.
Article
Automation & Control Systems
Zhaowu Ping, Chenxi Liu, Yunzhi Huang, Ming Yu, Jun-Guo Lu
Summary: This article presents experimental results on the discrete-time NOR problem for a linear motor driven inverted pendulum (LMDIP) system, and proposes a novel controller by combining neural network approach and friction-feedforward compensation mechanism. The experimental results verify the effectiveness of the proposed control algorithm and comparisons are made with a linear controller.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Construction & Building Technology
Saeed Soheili, Hooman Zoka, Mahdi Abachizadeh
Summary: This study investigates the effects of TMD device using ACO method in different soil conditions and finds that optimized design can significantly reduce structural drifts, enhancing the benefits of utilizing the TMD device.
ADVANCES IN STRUCTURAL ENGINEERING
(2021)
Article
Acoustics
Haowen Yang, Bin Wu, Jinping Li, Yu Bao, Guoshan Xu
Summary: This paper explores the dynamics model and control methods of human running on vibrating surfaces, demonstrating the stability and effectiveness of control methods through numerical simulations and experimental tests. The impact factors of human-structure interaction are analyzed.
JOURNAL OF SOUND AND VIBRATION
(2022)
Article
Engineering, Mechanical
Duy-Chinh Nguyen
Summary: This study presents analytical solutions for the optimization of two orthogonal TMDs to eliminate vibrations of an inverted pendulum with two degrees of freedom. The study establishes the equivalent resistance forces of the TMDs on the pendulum and reveals the quadratic torque matrices of the vibration response. Optimal expressions are derived using the maximization of equivalent viscous resistance method, providing exact solutions for the problem. Numerical results confirm the effectiveness of the optimal TMD parameters in eliminating vibrations of the articulated tower in the ocean.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART K-JOURNAL OF MULTI-BODY DYNAMICS
(2021)
Article
Robotics
Chittaranjan Paital, Saroj Kumar, Manoj Kumar Muni, Dayal R. Parhi, Prasant Ranjan Dhal
Summary: The main purpose of this study is to achieve smooth and autonomous navigation of a mobile robot in a cluttered environment through the use of a modified grey wolf optimization control method. Through experiments, it has been observed that this technique is efficient in motion control and path planning, allowing the robot to reach its target position without any collision. This work provides guidance for the use of similar approaches in other forms of robots, as it combines both simulation and real-time experimental validation.
INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS
(2023)
Article
Robotics
Abhishek Kumar Kashyap, Dayal R. Parhi, Vikas Kumar
Summary: The article focuses on the development and modeling of a hybrid navigational controller to optimize the path length and time taken. It utilizes a combination of metaheuristic moth-flame optimization and reinforcement learning approaches to navigate humanoid robots in unknown environments. The article also highlights the importance of configuring a Petri-Net controller in multi-humanoid robot systems to prevent collisions and ensure smooth task completion.
Article
Telecommunications
Saroj Kumar, Sujit S. Dadas, Dayal R. Parhi
Summary: This paper proposes a novel navigational approach, DAYKUN-BIP, for mobile robot movement. The approach uses virtual target displacement method to calculate the safest path for robot navigation. The effectiveness of the proposed method is validated through experiments and simulations, and it is found to significantly improve path length and time consumption compared to existing techniques.
WIRELESS PERSONAL COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Abhishek Kumar Kashyap, Dayal R. Parhi
Summary: This paper proposes a hybrid trajectory planning technique combining artificial potential field and quasi-Newton method for humanoid robots in complex environments. The proposed technique proves to be efficient and robust compared to traditional navigation methods.
Article
Robotics
Abhishek Kumar Kashyap, Dayal R. Parhi
Summary: The path planning methodology for humanoid robots focuses on improving path length and travel time. A hybrid approach combining Type-2 fuzzy system and adaptive ant colony optimization is proposed in this paper. The proposed controller shows efficient and collision-free navigation in complex terrains for single and multi-humanoid robot systems.
INTELLIGENT SERVICE ROBOTICS
(2023)
Article
Computer Science, Artificial Intelligence
Dayal R. Vikas, Dayal R. Parhi
Summary: The present work combines the classical approach with reactive techniques to analyze the navigation of humanoids in complex terrains. By merging the linear regression (LR)-based classical approach with the gravitational search algorithm (GSA), called RGSA, and incorporating Chaos, an optimum path is achieved. The proposed hybridization technique effectively improves upon local traps and ensures the smoothness of the path during trajectory planning. The analysis is conducted in real-time lab conditions and simulation environments using single and multiple humanoids with static and dynamic obstacles.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Vikas, Dayal Ramakrushna Parhi, Abhishek Kumar Kashyap
Summary: This study focuses on the optimal path planning of humanoid robots in rugged terrain using a hybrid-based improved gravitational search algorithm (IGSA) tuned with a differentially perturbed velocity (DV) approach. The primary IGSA has drawbacks of lower convergence rate and risk of getting trapped in optimal local conditions, which are eliminated by the hybrid IGSA-DV path planning approach. The algorithm aims to minimize the overall path length of the humanoid from source to goal in minimal time while considering path smoothness and energy efficiency optimization.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Abhijit Mahapatro, Prasant Ranjan Dhal, Dayal R. Parhi, Manoj Kumar Muni, Chinmaya Sahu, Sanjay Kumar Patra
Summary: This study focuses on path planning and stabilization of humanoids in uneven and dynamic environments. The work successfully improves the performance of humanoid robots in avoiding local minima and dead-ends during navigation. By integrating a PID controller with a fuzzy logic controller, the robot is able to stabilize itself on uneven surfaces and navigate through obstacle-rich terrains. Simulation and experimental tests demonstrate the effectiveness of the proposed controller, showing improvements in path length and computational time compared to previous techniques.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Robotics
Abhishek Kumar Kashyap, Dayal R. Parhi
Summary: This paper proposes a state-of-the-art simplified model, considering the vertical fluctuations of the center of mass and upper body movement for stable bipedal walking. The PID controller is tuned using converged teaching-learning based optimization technique, resulting in a feasible and energy-efficient gait pattern for the humanoid robot.
INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Manoj Kumar Muni, Saroj Kumar, Chinmaya Sahu, Prasant Ranjan Dhal, Dayal R. Parhi, Sanjay Kumar Patra
Summary: Researchers have improved artificial neural networks for robotic navigation by incorporating a hybrid fuzzy system, resulting in better decision-making and target-seeking abilities. This novel technique outperforms existing methodologies, achieving significant enhancements in track span.
JOURNAL OF COMPUTATIONAL SCIENCE
(2023)
Article
Robotics
Dayal R. Vikas, Dayal Parhi
Summary: The current research aims to enhance the humanoid NAO's ability to plan their overall routes through static and dynamic terrains. The strategy is based on the fusion of the modified hyperbolic gravitational search algorithm and dynamic window approach (DWA) for the navigation of humanoids in various complex terrains. The proposed method helps improve overall computational time and, hence, the cost associated with path planning.
INTELLIGENT SERVICE ROBOTICS
(2023)
Article
Automation & Control Systems
Saroj Kumar, Dayal R. Parhi
Summary: The autonomous robot has gained attention from researchers in the last decade due to the increasing demand for automation in defense and intelligent industries. In this study, a hybrid algorithm combining the modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) was implemented on wheeled robots to optimize multi-target trajectories and navigate obstacles in the workspace. The developed controller, together with a Petri-Net controller, successfully resolved conflicts during navigation. The proposed algorithm was tested and showed significant improvements in trajectory optimization (34.2%) and time consumption (70.6%).
Article
Engineering, Mechanical
Dayal R. Vikas, Dayal R. Parhi
Summary: A memory-based gravitational search algorithm with an evolutionary learning strategy is proposed for globally optimal collision-free path planning. The approach shows improved trajectory and fast convergence compared to other path-planning approaches.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2023)
