无腿压电胶囊机器人的混合模型预测控制

S. Kalantari, A. A. Farahani, A. Doustmohammadi, M. Menhaj, A. Suratgar, H. Talebi
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引用次数: 3

摘要

本文研究了胶囊机器人系统的混合建模与预测控制。首先,从胶囊机器人动力学中得到相应的非线性状态空间方程。然后,将这些状态空间方程转化为一类混合系统——逐块仿射系统。其次,通过定义表示切换规则的二进制变量,将该系统转换为混合逻辑动态(MLD)框架。最后,利用二次代价函数设计了模型预测控制(MPC)方案,实现了混合整数二次规划(MIQP)。MPC方法在多变量系统、不稳定系统、非最小相位(NMP)系统和长延迟系统的控制方面具有优势,优于其他方法。促使我们将MPC用于胶囊机器人动力学的主要原因是它在达到预期性能的同时处理约束的能力。仿真结果验证了所提控制方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid model predictive control of legless piezo capsubot
In this paper, the hybrid modeling and predictive control of capsule robot (capsubot) systems are presented. First, the corresponding nonlinear state space equations are obtained from capsubot dynamics. Then, these state space equations are converted to a Piece-Wise Affine (PWA) system which is a class of hybrid systems. Next, this system is transformed to a Mixed Logical Dynamical (MLD) framework by defining binary variables representing the switching rules. At last, a Model Predictive Control (MPC) scheme is designed for this framework using a quadratic cost function resulting in a Mixed-Integer Quadratic Programming (MIQP). MPC approach excels other approaches due to its superiority in control of multivariable systems, unstable systems, non-minimum phase (NMP) systems, and systems with long delay. The main reason motivating us to use MPC for capsubot dynamics is its capability of handling constraints while reaching a desired performance. Simulation results illustrate the effectiveness of the proposed control approach.
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