2D Push Recovery and Balancing of the EVER3 - a Humanoid Robot with Wheel-Base, using Model Predictive Control and Gain Scheduling

2019 ◽  
Author(s):  
Nikhil Gupta ◽  
Jesper Smith ◽  
Brandon Shrewsbury ◽  
Bernt Bornich
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Humanoid Robot ◽  
Gain Scheduling ◽  
Push Recovery

scholarly journals Knee-stretched walking with toe-off and heel-strike for a position-controlled humanoid robot based on model predictive control

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110362
Author(s):  
Zelin Huang ◽  
Zhangguo Yu ◽  
Xuechao Chen ◽  
Qingqing Li ◽  
Libo Meng ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Energy Efficient ◽  
Humanoid Robot ◽  
Center Of Mass ◽  
Heel Strike ◽  
Gait Generation ◽  
Double Support ◽  
Transition Moments ◽  
Efficient Gait

Knee-stretched walking is considered to be a human-like and energy-efficient gait. The strategy of extending legs to obtain vertical center of mass trajectory is commonly used to avoid the problem of singularities in knee-stretched gait generation. However, knee-stretched gait generation utilizing this strategy with toe-off and heel-strike has kinematics conflicts at transition moments between single support and double support phases. In this article, a knee-stretched walking generation with toe-off and heel-strike for the position-controlled humanoid robot has been proposed. The position constraints of center of mass have been considered in the gait generation to avoid the kinematics conflicts based on model predictive control. The method has been verified in simulation and validated in experiment.

A Low Complexity Gain Scheduling Strategy for Explicit Model Predictive Control of a Diesel Air Path

2015 ◽  
Author(s):  
Mike Huang ◽  
Ken Butts ◽  
Ilya Kolmanovsky
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Low Complexity ◽  
Gain Scheduling ◽  
Operating Conditions ◽  
Path Model ◽  
Explicit Model ◽  
Scheduling Strategy ◽  
Explicit Model Predictive Control ◽  
Operating Points

This paper describes a gain scheduling strategy that can be used in conjunction with explicit Model “Predictive Control (MPC). Traditionally, explicit MPC is not reconfigurable to online model changes. To handle off-nominal plant conditions, a common practice is to design multiple explicit MPC’s which are each valid locally around their respective operating points. This inevitably requires large amounts of memory to store the explicit MPC’s and implementation of switching logic and observers. The gain scheduling strategy presented in this paper bypasses the need to store multiple explicit MPC’s. This is done by multiplying the control signal obtained from the nominal explicit MPC by a gain scheduling matrix such that the plant at off-nominal operating conditions is approximately matched to the nominal plant. This is further accomplished in a manner such that the original control constraints are satisfied. The gain scheduling strategy is demonstrated in simulations on a nonlinear diesel air path model over the New European Drive Cycle (NEDC).

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