Simulation of Disturbance Recovery Based on MPC and Whole-Body Dynamics Control of Biped Walking

Shi, Xiuzhi; Gao, Junyao; Lu, Yizhou; Tian, Dingkui; Liu, Yi

doi:10.3390/s20102971

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…In the vertical direction, the oscillation magnitude increases with speed and is equal to about 2% of the body height [24]. Various methods have been proposed for control humanoid robots that focus on the CoM trajectory generation, such as the Predictive Control (MPC) Model [25,26], the minimized falling damage method which divided into two phases: (a). the optimal parametric strategy based on an inverted pendulum with flywheel used to plan the robot's motion, (b).…”

Section: Com (Center Of Mass) Trajectorymentioning

confidence: 99%

Optimization of Humanoid Robot Leg Movement Using Open CM 9.04

Wajiansyah

Malani

Supriadi

et al. 2022

Journal of Robotics and Control

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The Indonesian Robot Dance Contest (KRSTI) is a branch of the Indonesian Robot Contest (KRI) with the theme of dance. The robot used is a humanoid robot that can dance. Every year at the event, the provisions for robots constantly change, both the type of dance being demonstrated and the requirements for the robot's height. The taller the robot, the more difficult it is to control its walking movements because of the load it carries. This study uses a suitable algorithm to make the walking movement more natural and minimize the robot's falling. Human ROM data is used as a parameter for the range of motion of the servos that act as joints in the robot's legs. The algorithm created serves to determine the initial position of the angle on the servo to avoid the wrong initial movement position between one servo and another. The robot used is the Bioloid Robot’s leg Type A and uses OpenCM 9.04-A as the controller. The results showed that ROM on human feet could not be fully implemented on robot legs due to the robot's structure and the need for a robot that only relies on an algorithm to find the correct fulcrum to maintain balance. The comparison results show that the movement when walking on the ankle (ID servo 15) ranges from 749-567, while the ROM range is only between 580-512. When walking (servo ID 16), movement ranges from 460-291, while the ROM range ranges from 580-512.

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Section: Com (Center Of Mass) Trajectorymentioning

confidence: 99%

Optimization of Humanoid Robot Leg Movement Using Open CM 9.04

Wajiansyah

Malani

Supriadi

et al. 2022

Journal of Robotics and Control

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“…The task goals consist of nine equations and the robot has only three actuated joints, that is, three unknown variables, which is obviously an over-constrained and occasionally conflicting problem. To achieve a real-time solution in each sampling interval (4 ms), we were inspired by the solution method for the robot’s walking pattern in [ 24 , 25 , 26 ] and unified the indispensable constraints and over-constrained goals into a framework based on QP optimization with different weights in front of each objective to embody the priority of the task goals. Therefore, the actuated angular acceleration in each sampling interval can be estimated and the input angles of the actuated joints can then be obtained through a simple iterative process.…”

Section: Simplified Jump Model and Main Scheme Of Jumpingmentioning

confidence: 99%

Simulation of Upward Jump Control for One-Legged Robot Based on QP Optimization

Tian

Gao

Liu

et al. 2021

Sensors

Self Cite

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An optimization framework for upward jumping motion based on quadratic programming (QP) is proposed in this paper, which can simultaneously consider constraints such as the zero moment point (ZMP), limitation of angular accelerations, and anti-slippage. Our approach comprises two parts: the trajectory generation and real-time control. In the trajectory generation for the launch phase, we discretize the continuous trajectories and assume that the accelerations between the two sampling intervals are constant and transcribe the problem into a nonlinear optimization problem. In the real-time control of the stance phase, the over-constrained control objectives such as the tracking of the center of moment (CoM), angle, and angular momentum, and constraints such as the anti-slippage, ZMP, and limitation of joint acceleration are unified within a framework based on QP optimization. Input angles of the actuated joints are thus obtained through a simple iteration. The simulation result reveals that a successful upward jump to a height of 16.4 cm was achieved, which confirms that the controller fully satisfies all constraints and achieves the control objectives.

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“…Therefore, this paper selects path tracking as the ultimate goal of autonomous vehicle lateral control, and the tracking accuracy as the main indicator to measure the performance of the control system. Model predictive control can be divided into linear time-varying model predictive control (LMPC) [26] and nonlinear model predictive control (NMPC) [27]. Compared with NMPC, the LMPC uses the linear predictive model and has better real-time performance, which is a very important character for the motion control of autonomous vehicles.…”

Section: Linearization Of Vehicle Dynamics Modelmentioning

confidence: 99%

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Geng

Чулин

Wang

2020

Sensors

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The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

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Simulation of Disturbance Recovery Based on MPC and Whole-Body Dynamics Control of Biped Walking

Cited by 5 publications

References 22 publications

Optimization of Humanoid Robot Leg Movement Using Open CM 9.04

Optimization of Humanoid Robot Leg Movement Using Open CM 9.04

Simulation of Upward Jump Control for One-Legged Robot Based on QP Optimization

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

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