Abstract
This paper dealing with the problem of generation motion of biped robot, to solve this problem, was introduces a new method of implementation a biped robot walking pattern generation byusing a Model Predictive control (MPC) First, the basic of biped robots’ motion was reviewed and was discussed the elements of motion controller. The dynamics of a biped robot is modeled as a runningcart on a table which gives a convenient representation for generation ZMP trajectory. After reviewing approach of 3D Linear Inverted Pendulum Model, a pattern generator was designed, lastly in MATLAB/Simulink and using Model Predictive Control Toolbox (MPCT) which provides functions, an app, and Simulink blocks was used for designing and simulating a motion generator based on model predictive controllers (MPC).The results of simulation are shown that we can realize such controller for generating trajectory of Center of Mass and Zero Moment point by using the MPC. The smooth trajectory of CoM is generated and the resulted ZMP follows the reference with good accuracy.Ref. 13, pic. 6
Highlights
A trajectory generator is a control system that computes the reference trajectory for the robot’s joint [1]
We address the problem of simultaneous planning of desired trajectory of center of mass (CoM) and zero moment point (ZMP)[6], a problem that has been of interest in the humanoid robotics community for several years [7]
After defining the step parameters. we are going to generate the trajectories of ZMP and CoM, for that we implement the Model Predictive control (MPC), Note that the MPC controller in this paper is used for generation motion and not for controlling the Biped robot
Summary
A trajectory generator is a control system that computes the reference trajectory for the robot’s joint [1]. We are going to generate the trajectories of ZMP and CoM, for that we implement the Model Predictive control (MPC), Note that the MPC controller in this paper is used for generation motion and not for controlling the Biped robot. A = [0,1,0;0,0,1;0,0,0]; B = [0;0;1]; C = [1,0,-h_com/g; 1,0,0; 0,1,0]; D = [0;0;0]; AK_lip_x = ss(A,B,C,D); AK_lip_y = ss(A,B,C,D); AK_lip_x.InputName = 'xddd'; AK_lip_x.StateName = {'x', 'xd', 'xdd'}; AK_lip_x.OutputName = {'px', 'x', 'xd'}; AK_lip_y.InputName = 'yddd'; AK_lip_y.StateName = {'y', 'yd', 'ydd'}; AK_lip_y.OutputName = {'py', 'y', 'yd'}; From (11) and (12) we discretize the system and assign measured/unmeasured outputs using the c2d function as following: AKA_lip_x_d = c2d(AKA_lip_x,Ts); AKA_lip_y_d = c2d(AKA_lip_y,Ts); We need to specify the measured outputs This is very important for the MPC formulation.
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