Near Optimal PID Controllers for the Biped Robot While Walking on Uneven Terrains

Mandava, Ravi Kumar; Vundavilli, Pandu R.

doi:10.1007/s11633-018-1121-3

Cited by 18 publications

(8 citation statements)

References 48 publications

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“…The two-legged robot dynamics were obtained using the Lagrange-Euler formulation. The Lagrange-Euler formulation is used to supply the joints of the two-legged robot with the required torques [15], [23]. Figure 1 show the two-legged robot structure.…”

Section: Modelling Of Two-legged Robotmentioning

confidence: 99%

“…The inverse kinematics concept has been used to calculate the direction of each joint for the TLR. To define the joint angles of TLR, the following mathematical expression is used [23]: ). While 𝛺 9 = 𝛺 10 − 𝛽, Now, the ankle joint angle is assumed to equal to zero concerning the vertical axis, thus 𝛺 6 = 𝛺 12 = 0.…”

Section: Modelling Of Two-legged Robotmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

Wassef

Hadi

2023

Eng. rev. (Online)

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In this paper, an adaptive neuro-fuzzy based on fractional-order proportional-integral-derivative (ANFFOPID) controller with an improved slime mould algorithm (ISMA) for the two-legged robot (TLR) is proposed to achieve the minimum angular displacement error of the joint motors. Achieving such error is considered a challenging and time-consuming process due to the gain values set for the FOPID controller. Thus the neural-fuzzy network is used to provide the FOPID input signals by the adaptive magnitude gains. The adaptive mechanism depends on the ISMA to train the neural network weights. The outstanding properties of the ANFFOPID controller are evaluated by comparing the proposed controller with other existing work that is modified chaotic invasive weed optimization based on neural network (MCIWO-NN) for various walking terrains that are flat surface, stair ascending, and stair descending. Finally, the results obtained show the effectiveness of the ANFFOPID controller.

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Section: Modelling Of Two-legged Robotmentioning

confidence: 99%

Section: Modelling Of Two-legged Robotmentioning

confidence: 99%

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

Wassef

Hadi

2023

Eng. rev. (Online)

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“…Aktuator servo dynamixel telah memiliki kontrol internal dengan menggunakan pengontrol PID untuk mengatur posisi sudut dengan kecepatan sudut yang diinginkan. Kendali PI akan melacak pergerekan dari seluruh tubuh robot selama berjalan diatas tangga [15]. Setiap pose dan posisi robot saat naik tangga akan mempengaruhi keseimbangan.…”

Section: Pengendali Keseimbanganunclassified

Realisasi Dan Desain Dinamika Berjalan Naik Diatas Tangga Pada 33 Dof Humanoid Robot

Sari

Dewanto²,

Pramadihanto³

2020

klik

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This paper presents a balance control for humanoid robot for walking on the stair. Kinematics, dynamics, and control system are the complex problems for humanoid robot especially in walking through the stair. The goal of this paper is to implement the control methods for the robot to be able to walk through the stair. The complexities are influenced by the number of joint movements of 33 degree of freedom (DoF) resulting flexible movements. This movement patterns in walking on the stair considers the height and the width of the stair by using the feedback sensors and the proportional-integral (PI) controller. Walking transition from single phase to double phase is needed to conduct the smooth transition. To solve that problem, an integrated motion and controller are divided to two conditions: working motion in the offline planning and working motion in the online implementation. Vertical movement and the step-length movement are needed due to the additional movements are required for walking on the stair. That is why, PI controller is used to achieve the balance condition. Each controller is used in detail with the results of experiments and simultions. The kinematics approach for simulation has 100% successful rate and the real-time experiments have 93% of error. The effectivity and results of the proposed algorithm for humanoid robot are verified by the experiments to walk on the stair.Keywords: dynamic, kinematic, balance control, humanoid robotMakalah ini menyajikan kontrol keseimbangan untuk robot humanoid dalam berjalan di atas tangga. Kinematika, dinamika, dan sistem kendali merupakan masalah kompleks pada robot humanoid khususnya untuk berjalan melewati tangga. Tujuan akhir makalah ini adalah penerapan metode kontrol pada robot humanoid agar robot dapat berjalan di atas tangga. Kompleksitas pada robot dipengaruhi oleh banyaknya penggerak sebesar 33 derajat kebebasan (DoF) sehingga menghasilkan gerakan yang fleksibel. Pola gerakan ketika berjalan diatas tangga ini mempertimbangkan tinggi tangga dan lebar tangga dengan adanya umpan balik pada sensor dan system kendali keseimbangan menggunakan proporsional-integral (PI) kontroler. Transisi berjalan dari fase dukungan tunggal ke fase dukungan ganda diperlukan untuk kelancaran siklus transisi. Untuk menyelesaikan masalah tersebut, sebuah gerakan terintegrasi dan pengontrol dibagi menjadi dua kondisi: gerakan bekerja pada perencanaan offline dan pengontrol bekerja yang berjalan online. Hal ini dikarenakan kebutuhan tambahan gerakan vertikal pada tinggi tangga dan panjang langkah ketika menaiki tangga. Oleh karena itu, kontroler PI digunakan untuk mencapai kondisi keseimbangan. Setiap pengendali ditangani secara detail dengan hasil eksperimen dan simulasi. Pendekatan Kinematika untuk simulasi memiliki hasil yang baik berdasarkan percobaan simulasi 100% dan percobaan secara langsung dengan robot memiliki error 93%. Efektivitas dan kinerja dari algoritma kontrol yang diusulkan diverifikasi melalui percobaan naik tangga pada humanoid robot.Kata kunci: dinamika, kinematika, kendali keseimbangan, robot humanoid

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“…Mandava et al proposed a multi-objective particle swarm optimization algorithm method for the gait optimization of humanoid robots for the trolley table model. This method uses a sliding mode controller to optimize robust tracking control and realizes the 3D simulation walking of a humanoid robot (Mandava and Vundavilli, 2018 ). Gülcü and Kodaz ( 2015 ) improved the performance of comprehensive learning particle swarm optimizer (CLPSO) through parallel computation and proposed PCLPSO.…”

Section: Introductionmentioning

confidence: 99%

Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

et al. 2021

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To improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natural zero-moment point trajectory planning method. Secondly, by changing the slave group structure of the PCLPSO algorithm, the gait training task is decomposed, and a parallel distributed multi-robot gait training environment based on RoboCup3D is built to automatically optimize the speed and stability of bipedal robot walking. Finally, a layered learning approach is used to optimize the turning ability of the humanoid robot. The experimental results show that the PCLPSO algorithm achieves a quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and flexible steering ability.

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Near Optimal PID Controllers for the Biped Robot While Walking on Uneven Terrains

Cited by 18 publications

References 48 publications

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

Realisasi Dan Desain Dinamika Berjalan Naik Diatas Tangga Pada 33 Dof Humanoid Robot

Gait Optimization Method for Humanoid Robots Based on Parallel Comprehensive Learning Particle Swarm Optimizer Algorithm

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