Design of optimized fuzzy cascade controllers by means of Hierarchical Fair Competition-based Genetic Algorithms

In this study, we present a design of an optimized fuzzy cascade controller based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) for a rotary inverted pendulum system. In this system, one controls the movement of a pendulum through the adjustment of a rotating arm. The objective is to control the position of the rotating arm and to make the pendulum maintain the unstable equilibrium point at vertical position. To control the system, we design a fuzzy cascade controller scheme which consists of two fuzzy controllers arrange in a cascaded topology. The parameters of the controller are optimized by means of the HFCGA algorithm. The fuzzy cascade scheme comprises two controllers located in two loops. An inner loop controller governs the position of the rotating arm while an outer controller modifies a set point of the inner controller implied by the changes of the angle of pendulum. The HFCGA being a computationally effective scheme of the Parallel Genetic Algorithm (PGA) has been developed to eliminate an effect of premature convergence encountered in Serial Genetic Algorithms (SGA). It has emerged as an effective optimization vehicle to deal with very large search spaces. A comparative analysis involving computing simulations and practical experiment demonstrates that the proposed HFCGA based fuzzy cascade controller comes with superb performance in comparison with the conventional Linear Quadratic Regulator (LQR) controller as well as HFCGA-based PD cascade controller.     

PF-MPC: Particle filter-model predictive control

In this article, a new model predictive control approach to nonlinear stochastic systems will be presented. The new approach is based on particle filters, which are usually used for estimating states or parameters. Here, two particle filters will be combined, the first one giving an estimate for the actual state based on the actual output of the system; the second one gives an estimate of a control input for the system. This is basically done by adopting the basic model predictive control strategies for the second particle filter. Later in this paper, this new approach is applied to a CSTR (continuous stirred-tank reactor) example and to the inverted pendulum. These two examples show that our approach is also real-time-capable.     

单级倒立摆的LQR控制和DMC控制Matlab仿真比较

建立了单级直线倒立摆的状态空间模型,分别从线性最优二次型（LQR）控制和模型预测控制的原理出发,在MAT-LAB/SIMULINK环境下,设计了LQR控制器和动态矩阵控制（DMC）控制器,对所建立的倒立摆模型对象进行系统控制仿真。仿真结果表明,作为先进控制的DMC控制的效果比作为最优控制的LQR控制效果更好,鲁棒性更强。     

单级倒立摆的逼近逆模型及趋近控制

基于非线性分级控制思想,研究了逼近逆模型的设计方法,并结合变结构趋近控制律规则,将基于综合误差方法的滑动模逼近控制应用在某单级倒立摆上,通过仿真分析,收到了良好的控制效果.     

Stabilization of the inverted pendulum around its homoclinic orbit

The inverted pendulum has been used as a benchmark for motivating the study of nonlinear control techniques. We propose a simple controller for balancing the inverted pendulum and raise it to its upper equilibrium position while the cart displacement is brought to zero. The control strategy is based on an energy approach of the cart and pendulum system.     

单级倒立摆建模及其控制方法设计

单级倒立摆控制是一个即复杂而又对准确性、快速性要求很高的非线性不稳定系统控制问题。单级倒立摆数学模型的建立对研究其稳定性具有指导作用。用拉格朗日功能平衡法建立了倒立摆的刚体动力学方程,采用了LQR法对其进行了控制设计,并且进行了MATLAB仿真,结果表明此方法均可以成功控制倒立摆,使其稳定并具有良好的鲁棒性。     

免疫优化神经网络对倒立摆的控制

人工免疫算法是基于生物免疫系统机理和特点发展而来的一种新型智能算法。针对BP神经网络传统训练算法的学习效率低、收敛速度慢且容易陷入局部最优等不足,采用免疫算法对BP神经网络权值进行优化,以实现对倒立摆的控制。实验结果表明,用免疫算法训练神经网络收敛速度快,并能有效避免陷入局部最优。优化后的神经网络能对实际的倒立摆装置进行控制,并取得了满意的效果。     

一类非线性系统的分步变换伪线性化方法

给出了单输入非线性系统的分步变换伪线性化方法及其算法。首先将原系统变换为标准型。利用（1）中的算法,可以获得将标准型变换为伪标准型的变换阵T。在第一步变换下,原系统的部分状态已经获得精确线性化,并且分步变换算法比由原系统直接伪线性化的算法简单,单杆倒立摆的计算机仿真表明了该算法的有效性。     

基于模糊动态模型的二级倒立摆系统的分析和设计

首先给出了一种离散系统模糊动态模型的辨识方法,提出了一个衡量聚类有效性的函数,以确定模糊规则的数目,并给出一个保证模糊系统全局稳定的充分条件,在此基础上给出了模糊控制系统的设计方法,并对二级倒立摆系统进行了控制,取得了较好的效果。     

小型弹箭惯性矩的测定

本文叙述了三线摆的结构原理 ,介绍了此摆小角及大角摆动周期的计算方法 ,利用三线摆可以测量小型弹箭、弹头、飞机模型及其他构件的惯性矩。当R/L <1/ 30 ,φ≤ 6 0°时 ,该装置的测量准确度可优于 1%。