基于混合粒子群优化的仿袋鼠机器人站立平衡控制

为提高仿袋鼠机器人的站立平衡控制性能,基于混合粒子群算法对机器人的平衡控制进行了优化.首先,将在地面站立平衡时的仿袋鼠机器人简化成一个倒立摆模型,使用拉格朗日方法对机器人进行动力学建模.然后,基于机器人的动力学模型设计了线性二次型控制器,并使用混合粒子群算法对线性二次型控制器的权重矩阵进行优化.最后,使用优化的线性二次型控制器对仿袋鼠机器人站立平衡控制进行了仿真实验.优化后的控制器的调节时间比优化前明显缩短,结果表明:基于混合粒子群算法优化的线性二次型(linear quadratic regulator,LQR)控制器可以提高系统的稳定性和鲁棒性,能有效降低控制器参数的整定工作量.

Optimized Balance Control for Bionic Kangaroo Robot During Stance Phase Using Hybrid Particle Swarm Optimization

In order to improve the control performance of bionic kangaroo robot during stance phase, an optimization method for balance control is studied in this paper. The bionic kangaroo robot is first simplified to an inverted pendulum model during stance phase, and a multi-rigid-body dynamics model of the robot is established using Lagrange method. A linear quadratic regulator for stance balance control is designed based on the dynamics model, in which the optimum weight matrix is obtained by hybrid particle swarm algorithm. Simulations are conducted on balance control of the robot during stance using the optimized LQR regulator. The settling time of the optimized balance control is shorter. Results show that the optimized control method can improve the control performance of the bionic robot with good robustness and rapidity.