非线性倒立摆系统平衡点反馈控制参数的优化计算

本文基于圆轨倒立摆非线性系统模型,建立了平衡点状态反馈控制参数的高精度优化算法,编制计算程序确定出反馈控制参数,并对圆轨倒立摆平衡点的实例控制.与将系统线性化后的反馈参数的计算分析相比,本文研究方法具有较大平衡范围.仿真与实例平衡控制都表明了本文研究方法的可行性.     

基于模糊加权的倒立摆混合控制

针对小车倒立摆系统,提出了一种线性状态反馈控制和滑模控制模糊加权的控制方法.滑模控制器的作用是将摆角控制在零的一个邻域内,在此邻域内首先采用近似的线性化模型来描述倒立摆系统,然后采用基于极点配置的方法设计系统的线性状态反馈控制器以使系统的状态稳定在给定值,两个控制器的输出通过加权求和作为倒立摆的控制作用.仿真结果证实了该方法的有效性.     

基于PSO的二级倒立摆LQR最优控制器设计

根据微粒群算法的随机性、快速性、易于实现性等优点,针对LQR在二级倒立摆最优控制设计过程中对加权矩阵Q、R选择的盲目性,研究了基于PSO的LQR最优控制器的设计方法,该方法利用PSO算法的启发式全局优化特点对Q、R阵进行寻优,然后得到状态反馈控制律K。并设计了基于该方法的二级倒立摆的最优控制器,通过和基于遗传算法的LQR最优控制器比较,仿真结果表明：该方法所设计的最优控制器能使系统的响应时间更快,超调量更小,对二级倒立摆的控制效果更理想。     

基于能量控制与近似输出调节的二级倒立摆起摆和跟踪控制

二级倒立摆是一个典型的欠驱动非线性系统,其控制问题具有一定的挑战性.为了解决时变参考信号下二级倒立摆的起摆和跟踪控制问题,本文提出了一种基于能量控制与近似输出调节方法的起摆和三阶控制器设计方案.首先,采用能量控制方法将第1级摆杆从下垂位置摆起到倒立位置附近;其次,采用滑模控制方法将第1级摆杆稳定在倒立位置,同时,采用等效小车与能量控制相结合的方法将第2级摆杆摆起到倒立位置附近;最后,采用基于近似输出调节理论的多项式近似方法设计三阶控制器实现二级倒立摆的位置跟踪控制.仿真和实验结果均验证了该控制方案的有效性.     

车轨长度受限的并行双摆能量控制

采用牛顿-欧拉方法建立并行二级倒立双摆系统的数学模型.针对车轨长度受限的并行二级倒立摆系统,本文提出了一种基于能量控制思想和直接李雅普诺夫函数方法的摆起控制策略.所设计的控制器保证了小车的速度收敛到零和摆杆在达到垂直向上的位置时摆杆的能量为零.同时,它能实现对并行双摆的稳摆控制.控制器简单易行,参数调节方便.在并行二级倒立摆摆起控制器设计的基础上,简述了三级车摆的摆起控制器设计过程.最后,通过计算机仿真验证了控制方法在工作效率和抗干扰方面能保持良好的控制性能.     

基于参数优选法LQG最优控制的倒立摆控制

为了实现对单级倒立摆系统的控制,提出了一种基于LQG(线性二次型高斯控制)的倒立摆系统控制方法.利用LQR(线性二次最优)构建系态反馈矩阵,再结合Kalman滤波器,综合得到了LQG控制器,并对该控制器的参数进行了优化设计.通过MATLAB仿真,验证了采用的控制算法能够有效地对单级倒立摆系统进行控制,系统超调量较小,并具有一定的鲁棒性.     

柔性连接倒立摆的最优控制及状态观测器的应用

本文研究具有柔性连接的倒立摆系统的动力学特性,通过数学分析提出应用线性二次型最优控制策略设计状态反馈控制器,利用降阶状态观测器获得不易测量的振动小车位置,控制倒立摆系统稳定地平衡在“倒立”状态,最后通过仿真实验证明了此种方法的有效性。     

平面二级倒立摆自适应滑模控制器设计

研究平面二级倒立摆系统稳定性和速度特性优化问题,由于倒立摆系统的外界扰动的不确定性,建立平面二级倒立摆的数学模型,应用变结构控制理论(SMC)和模糊逻辑系统设计了自适应滑模控制器,把趋近律和切换控制的模糊化相结合,采用模糊系统调整趋近速率的大小,在加快趋近速度的同时用模糊逼近切换控制,为减少控制量的抖振和优化控制系统,同时倒立摆控制具有了滑模控制对外界扰动和参数摄动的不变性。进行仿真的结果验证了控制器的稳定性,表明控制器系统能保证在不同的运行条件下具有快速性和鲁棒性。     

基于鲁棒H∞理论的三级倒立摆控制系统研究

为了研究三级倒立摆的鲁棒控制方法,设计出三级倒立的状态反馈H∞控制器。仿真结果表明三级倒立摆具有良好的鲁棒稳定性、鲁棒性、抗干扰性,实现了对三级倒立摆的稳定控制。同时运用了状态空间极点配置法和LQR最优控制法,分别设计出针对三级倒立摆的控制器。经比较研究：采用状态反馈H∞方法设计的三级倒立摆控制器的控制效果非常好,使其具有较小的振荡和超调量,倒立摆起摆迅速,稳定控制性能优良。     

二级倒立摆的Sugeno型模糊神经网络控制

为了提高二级倒立摆系统实时控制的响应速度和稳定性,在设计Mamdani型模糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器。该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani型控制器的仿真对比及实际控制实验结果,表明该Sugeno型模糊神经网络控制器对二级倒立摆实验装置的控制具有良好的稳定性、快速性和较高的控制精度。     

Stabilization and Tracking Control of X‐Z Inverted Pendulum Using Flatness Based Active Disturbance Rejection Control

A flatness based robust active disturbance rejection control technique scheme with tracking differentiator is proposed for the problem of stabilization and tracking control of the X‐Z inverted pendulum known as a special underactuated, non‐feedback linearizable mechanical system. The differential parameterization on the basis of linearizing the system around an arbitrary equilibrium decouples the underactuated system into two lower order systems, resulting in two lower‐order extended state observers. Using a tracking differentiator to arrange the transient process utilizes the problem of stabilization and tracking control and gives a relatively small initial estimation error, which enlarges the range of the controller parameters. The convincing analysis of the proposed modified linear extended state observer is presented to show its high effectiveness on estimating the states and the extended states known as the total disturbances consisting of the unknown external disturbances and the nonlinearities neglected by the linearization. Simulation results on the stabilization and tracking control of the X‐Z inverted pendulum, including a comparative simulation with an all‐state‐feedback sliding mode controller are presented to show the advantages of the combination of flatness and active disturbance rejection control techniques.     

Big Bang-Big Crunch optimized hierarchical sliding-mode control of X-Z inverted pendulum

The control of the X-Z inverted pendulum is a challenging work since the X-Z inverted pendulum is an underactuated, open-loop unstable and multi-input-multi-output (MIMO) nonlinear system. In this paper, we will present a novel state transformation method for the X-Z inverted pendulum and Big Bang–Big Crunch (BBBC) optimized hierarchical sliding-mode control (HSMC) structure. We will firstly show that through the proposed transformation, the model of the X-Z inverted pendulum can be transformed to a typical underactuated form. Thus, based on the obtained system model, the hierarchical sliding-mode control (HSMC) can be directly applied in the trajectory tracking control of the X-Z inverted pendulum. Then, to ensure a convergent performance of the auxiliary sliding surfaces, the BBBC method is applied to obtain the optimal coupling factors for the HSMC. The control performance of the proposed BBBC based HSMC structure is compared with that of the present SMC and the HSMC with particle swarm optimization (PSO). Simulation results show the effectiveness of the proposed controllers for the X-Z inverted pendulum.     

倒立摆的自适应积分反步控制策略

针对直线单级倒立摆在模型参数不确定和外部扰动情况下的稳定控制问题,提出一种自适应积分反步控制策略。采用拉格朗日方程建立倒立摆系统的运动学模型,为减少稳态误差,将误差积分项引入反步法,设计了倒立摆的控制器;对含有未知参数的系统非线性状态微分方程,设计适当的Lyapunov函数推导出系统未知参数的自适应更新律,削弱了参数不确定性的影响。将自适应积分反步控制与一般的反步法控制、模糊控制及神经网络控制的仿真结果进行了对比,并在LabVIEW开发环境下进行了实物实验。结果表明,自适应积分反步法可以较为迅速且精确地完成稳定控制,较好地克服系统参数不确定及外部扰动的影响,具有较强的鲁棒性。     

Robust stability analysis and fuzzy-scheduling control for nonlinear systems subject to actuator saturation

Takagi-Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation. The TS fuzzy representation of a nonlinear system subject to actuator saturation is presented. In our TS fuzzy representation, the modeling error is also captured by norm-bounded uncertainties. A set invariance condition for the system in the TS fuzzy representation is first established. Based on this set invariance condition, the problem of estimating the domain of attraction of a TS fuzzy system under a constant state feedback law is formulated and solved as a linear matrix inequality (LMI) optimization problem. By viewing the state feedback gain as an extra free parameter in the LMI optimization problem, we arrive at a method for designing state feedback gain that maximizes the domain of attraction. A fuzzy scheduling control design method is also introduced to further enlarge the domain of attraction. An inverted pendulum is used to show the effectiveness of the proposed fuzzy controller.     

一类非线性欠驱动系统的混沌优化LQ控制

提出了一种基于混沌优化线性二次最优控制器权矩阵参数的三级倒立摆控制方法;根据系统控制的目标,设计了一类适合多变量系统的优化性能指标函数;这类性能指标函数综合考虑三级倒立摆系统各个输出间的重要程度,以及动态特性和稳定性要求,结合文中的性能指标函数,首先利用混沌粗搜索得到控制器权矩阵参数的次优解,再在次优解的邻域内继续寻优,得到全局最优的权矩阵参数;利用这种方法得到的LQ控制器,有效地实现了对三级倒立摆的稳定控制。     

Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA)

In this paper, fuzzy threshold values, instead of crisp threshold values, have been used for optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a single inverted pendulum having parameters with probabilistic uncertainties. The objective functions that have been considered are, namely, the normalized summation of rising time and overshoot of cart (S_RO–C) and the normalized summation of rising time and overshoot of pendulum (S_RO–P) in the deterministic approach. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for single inverted pendulum problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic single inverted pendulum using the conflicting objective functions in time domain. Such Pareto front is then obtained for single inverted pendulum having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Monte Carlo simulation (MCS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA with fuzzy threshold values includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers.     

三级倒立摆的自动摆起与稳定控制

采用非线性逆系统轨迹控制实现三级倒立摆的自动摆起,并设计了变增益LQR控制器将其稳定在竖直倒立位置.首先,三级倒立摆从静止下垂状态摆起到竖直倒立位置的过程,从数学角度看是一个两点边值问题,通过求解该两点边值问题获得摆起的标称轨迹,利用逆系统方法设计前馈控制,同时结合增益调度反馈控制使摆起过程稳定;其次,在稳定控制阶段,...     

Incremental Evolutionary Design of TSK Fuzzy Controllers

This paper proposes a novel method for the incremental design and optimization of first order Tagaki-Sugeno-Kang (TSK) fuzzy controllers by means of an evolutionary algorithm. Starting with a single linear control law, the controller structure is gradually refined during the evolution. Structural augmentation is intertwined with evolutionary adaptation of the additional parameters with the objective not only to improve the control performance but also to maximize the stability region of the nonlinear system. From the viewpoint of optimization the proposed method follows a divide-and-conquer approach. Additional rules and their parameters are introduced into the controller structure in a neutral fashion, such that the adaptations of the less complex controller in the previous stage are initially preserved. The proposed scheme is evaluated at the task of TSK fuzzy controller design for the upswing and stabilization of a rotational inverted pendulum. In the first case, the objective is a time optimal controller that upswings the pendulum in to the upper equilibrium point in shortest time. The stabilizing controller is designed as a state optimal controller. In a second application the optimization method is applied to the design of a fuzzy controller for vision-based mobile robot navigation. The results demonstrate that the incremental scheme generates solutions that are similar in control performance to pure parameter optimization of only the gains of a TSK system. Even more important, whereas direct optimization of control systems with more than 35 rules fails to identify a stabilizing control law, the incremental scheme optimizes fuzzy state-space partitions and gains for hundreds of rules.