立方体机器人自抗扰平衡控制方法

针对立方体机器人动力学模型多变量、强耦合的问题,提出一种基于自抗扰控制的平衡控制器设计方法.引入虚拟控制量,并在控制量与输出向量之间并行地嵌入多个自抗扰控制器,从而实现对多变量系统的解耦控制,将系统的动态耦合和外部扰动视为各自通道上的自抗扰控制器的总扰动,在为期望姿态安排过渡过程基础上,设计扩张状态观测器对总扰动进行估计并实时补偿.综合采用经验试凑法和带宽法对控制器参数进行整定,对自抗扰控制器系统进行稳定控制、姿态跟踪、抗扰性和鲁棒性实验,并与PID控制系统进行定量对比分析.仿真结果表明,所设计的自抗扰控制器不仅能有效实现立方体机器人的平衡控制,而且较PID控制器具有更好的响应速度、控制精度和强鲁棒性.     

基于模糊PD控制方法的两轮直立自平衡电动车研究北大核心CSCD

通过机构设计、硬件开发、软件策略等方面,搭建两轮直立电动车控制系统,满足了直立车的控制要求。设计以陀螺仪和加速计为传感器的姿态获取模块,利用卡尔曼滤波算法提高控制精度。基于系统结构模型,采用牛顿力学的分析方法构建两轮自平衡直立车动力学方程。设计了模糊PD控制器,以直立车的位移和速度为输入变量进行模糊控制器控制,倾斜角度和角速度由PD控制器控制。最后,建立了直立车实时控制系统平台,用于两轮自平衡直立车的参数调试及实时控制。实验结果表明,该系统具有较好的稳定性,实时响应性高,具有较高的市场推广价值。     

测试转台的输出改进自抗扰控制器设计

当电机存在低频大力矩扰动时,使用经典方法控制测试转台很难达到期望的测量精度,为了有效抑制扰动达到期望精度,提出一种输出改进的自抗扰控制器设计方法。在控制的起始阶段采用普通自抗扰控制器,系统进入稳态后引入非线性饱和环节限定自抗扰控制器的输出幅值。仿真结果表明,系统既具有良好的动态特性,又有很好的抗扰性,达到了较好的输出精度。最后与经典控制方法相比较进一步验证了其良好的控制效果。     

测试转台的自抗扰控制器设计

工程上通常采用经典控制方法对测试转台进行控制,但往往很难保测量结果的高精度和强抗扰性。本文用自抗扰控制器来控制测试转台。仿真结果表明系统响应快,超调小,精度高,抗扰能力强,最后与经典控制的对比,进一步证明了自抗扰控制器是适用于测试转台控制的理想控制器。     

基于卡尔曼滤波的两轮自平衡车姿态检测

建立了两轮自平衡车的动力学模型;设计了一种采用卡尔曼滤波的两轮自平衡车姿态检测控制算法的控制器;搭建了SIMULINK仿真模型,仿真分析了控制器中各参数对系统的影响;并将仿真程序移植到16位Freescale单片机中对两轮自平衡车进行控制;通过实测数据验证了所设计控制算法的合理性和设计电路的正确性。     

自适应模糊自抗扰控制器的研究与设计

针对普通PID在实际工程控制中参数整定难,抗干扰能力差的问题,设计了一种基于模糊控制原理的改进型自抗扰控制器.该控制器充分结合了模糊控制器和自抗扰控制器的各自优势,并对其进行了随动系统的仿真.仿真结果表明,该控制器可设计成为一个响应速度快、静差小的控制系统,与经典PID控制器在同样的系统中比较,自适应模糊自抗扰控制有较好的控制性能.     

基于互补滤波的两轮自平衡车姿态控制

利用飞思卡尔XS128控制器、MMA7260三轴加速度计、ENC-03系列陀螺仪设计制作了两轮自平衡车.通过软件编程对两种传感器的检测值进行互补滤波融合,得到了可稳定、准确地反映自平衡车真实情况的角度值.并结合增量式PID算法,很好地实现了两轮自平衡车的动态平衡,验证了互补滤波算法应用于两轮自平衡车姿态控制的有效性.     

独轮机器人的建模与自抗扰控制算法

独轮机器人前后平衡由一车轮保持并驱动其前后运动, 侧向平衡则由一基于空气阻力的风轮保持, 以此结构为被控对象建立该系统动力学模型. 以一种非线性的控制方法—–自抗扰控制方法控制其平衡运动, 在系统的纵向和侧向上分别设计一个自抗扰控制器, 系统的内扰和外扰被视为自抗扰控制器的总扰动. 以PID 控制方法作对比实验, 仿真结果表明了自抗扰控制算法的强鲁棒性和有效性.

     

云模型控制器在两轮自平衡机器人中的应用

两轮自平衡机器人控制系统具有高阶次、多变量、非线性且强耦合的特性,因此难以建立精准的数学模型。针对两轮自平衡机器人系统的复杂性,对其平衡控制系统进行了研究,提出了一维云模型控制器的设计方法。运用该方法,成功地实现了两轮自平衡机器人的平衡控制,并比较了一维云模型控制器在三规则和五规则下对系统性能的影响。试验结果表明:一维云模型控制器在两轮自平衡机器人平衡控制系统中具有良好的控制性能和强抗干扰性,五规则控制器具有更加优越的控制效果。云模型控制器成功应用在两轮自平衡机器人平衡系统中,并在试验样机平台体现了良好的平衡性能,为今后云模型控制器的设计提供参考,也推进了云模型控制器在硬件平台实现的进程。     

自抗扰控制器在液位控制中的研究

经典PID控制器由于其简单实用,在液位控制中广泛的应用。但对于涉及到工业过程控制中的一阶时滞系统来说:PID控制存在着诸多不理想因素,如环境变化等容易导致工业模型参数随之变化造成的控制不理想。为了改善其液位的控制效果并保留经典PID控制的简单实用性。提出了一种比较新型的控制方案:即自抗扰控制器(ADRC)水箱液位控制。自抗扰控制器具有模型简单,收敛速度快,精度高,参数易于调节等特点,通过建立控制系统的仿真模型,将用Z-N整定方法的PID控制与自抗扰控制进行比较,并进行了仿真对比研究。结论表明:自抗扰算法在液位控制等控制工程中是一种优秀的控制方案。     

Adaptive decentralized control under non-uniqueness of the optimal control

We study the problem of decentralization of flow control in packet-switching networks under the isarithmic scheme. An incoming packet enters the network only if there are permits available at the entry port when it arrives. The actions of the controllers refer to the routing of permits in the network and the control variables are the corresponding probabilities. We study the behavior of adaptive algorithms implemented at the controllers to update these probabilities and seek optimal performance. This problem can be stated as a routing problem in a closed queueing network. The centralized version of a learning automation is a general framework presented along with the proof of asymptotic optimality. Decentralization of the controller gives rise to non-uniqueness of the optimal control parameters. Non-uniqueness can be exploited to construct asymptotically optimal learning algorithms that exhibit different behavior. We implement two different algorithms for the parallel operation and discuss their differences. Convergence is established using the weak convergence methodology. In addition to our theoretical results, we illustrate the main results using the flow control problem as a model example and verify the predicted behavior of the two proposed algorithms through computer simulations, including an example of tracking.The work of this author was partially supported by a grant from the Canadian Institute for Telecommunications Research under the NCE program of the Government of Canada, and partially supported by NSERC grant WFA 0139015 and FCAR-Québec grant 95-NC-1375.The work of this author was supported by a grant from the CITR under the NCE program of the Government of Canada.     

模糊PID控制在工业锅炉控制系统中的应用

针对工业锅炉控制系统的特点．采用将常规PID控制与模糊控制相结合的控制策略．在常规PID调节器的基础上，采用模糊推理思想，根据不同的E、EC对PID参数Kp、Kl、Kd进行自校正。对锅炉汽包液位的控制表明这种模糊PID控制器可明显提高系统的性能。     

Optimizing placement of the control points at synthesis of the heating process control

Consideration was given to the problem of optimizing the locations of the control points over the object state within the framework of the optimal feedback control problems of the distributed parameter objects. For certainty, considered was heating of a rod in furnace where the temperature of controlled vs. the temperature measured at certain points of the rod. The problem is reduced to that of parametric optimal control of an initial-boundary value problem where unknown current values of temperature at the measurement points occur at the right side of its equation and the boundary conditions. Formulas were derived for the components of the objective functional gradient with respect to the coordinates of the control point locations and the parameters of the measurement-dependent current control. The results of the carried out numerical experiments were given.     

PID控制在反应釜温度控制中的应用

常规PID的控制,不但其参数难以整定,而且还依赖于对象的精确数学模型,适应性较差,对复杂过程不能保证其控制精度.根据反应釜温度时间滞后具有非线性、强耦合、不确定性过程的控制需要,提出了一种基于BP神经网络的PID控制方法.并介绍了神经网络HD控制器的算法,对经典PID参数选取进行了分析.仿真结果表明,与传统PID算法相比,该控制方法可实现有效的控制,具有实现简单、控制效果好的特点.     

基于自适应模糊控制的冷气控制系统设计

冷气系统如保鲜库、冷库等具有较大的时变性、非线性和时滞性,经典控制理论和现代控制理论的最优控制难以达到精确的控制。本文以某厂冷库为控制对象,采用变斜率规则自调整的自适应模糊控制方法,设计了基于I2C总线的单片机冷气控制系统,并给出了详细的控制算法和接口电路。     

Spreading the control complexity in decentralized control of interconnected systems

The problem is considered for large scale interconnected systems of designing decentralized dynamic controllers so that the sum of the orders of the local controllers is approximately the system order, while the separate controller orders are more evenly spread than permitted by the procedure of Corfmat and Morse, which generally leads to all memoriless controllers, save one of high complexity. The paper shows that under certain circumstances, it is possible to design dynamic feedback controllers for each channel which introduce stable uncontrollable or unobservable modes to the overall system, thus lowering the effective order of that system. This result is then used to spread the controller complexity.     

Plantwide control: the search for the self-optimizing control structure

Plantwide control is concerned with the structural decisions involved in the control system design of a chemical plant (C.S. Foss, Critique of chemical process control theory, AIChE Journal 19(2), 1973) 209–214; “Which variables should be controlled, which variables should be measured, which inputs should be manipulated, and which links should be made between them?” In particular, the first issue about which variables to control has received little attention. It is argued that the answer is related to finding a simple and robust way of implementing the economically optimal operating policy. The goal is to find a set of controlled variables which, when kept at constant setpoints, indirectly lead to near-optimal operation with acceptable loss. This is denoted “self-optimizing” control. Since the economics are determined by the overall plant behavior, it is necessary to take a plantwide perspective. A systematic procedure for finding suitable controlled variables based on only steady-state information is presented. Important steps are degree of freedom analysis, definition of optimal operation (cost and constraints), and evaluation of the loss when the controlled variables are kept constant rather than optimally adjusted. A case study yields very interesting insights into the control and maximum throughput of distillation columns.     

采用TD3100变频器实现的高精度定位控制的几种方法

本文提出采用TD3100电梯专用矢量控制变频器实现的高精度定位控制的几种方法,重点论述了该系统的原理及应用实例。     

Understanding the final-state control from the standpoint of the model predictive control and its application to a three-dimensional trajectory control problem

Microsystem Technologies - In many motion control problems of mechatronic equipment, the control performance of the final-state of the control period is strictly important for positioning or...     

Adaptive neuro-genetic control of chaos applied to the attitude control problem

Conventional adaptive control techniques have, for the most part, been based on methods for linear or weakly non-linear systems. More recently, neural network and genetic algorithm controllers have started to be applied to complex, non-linear dynamic systems. The control of chaotic dynamic systems poses a series of especially challenging problems. In this paper, an adaptive control architecture using neural networks and genetic algorithms is applied to a complex, highly nonlinear, chaotic dynamic system: the adaptive attitude control problem (for a satellite), in the presence of large, external forces (which left to themselves led the system into a chaotic motion). In contrast to the OGY method, which uses small control adjustments to stabilize a chaotic system in an otherwise unstable but natural periodic orbit of the system, the neuro-genetic controller may use large control adjustments and proves capable of effectively attaining any specified system state, with no a prioriknowledge of the dynamics, even in the presence of significant noise.This work was partly supported by SERC grant 90800355.