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排序方式: 共有127条查询结果,搜索用时 15 毫秒
1.
In this paper trajectory tracking algorithms for gasoline engines are devised. Specifically, precise reference tracking in engine speed and air-to-fuel ratio is enabled while satisfying initial and final conditions on the center of combustion. Such a tracking of multiple reference trajectories requires a coordinated control action for the air path, the fuel path, and the ignition timing actuators. Combining a dedicated feedforward and feedback controller structure and multivariable model-based norm-optimal parallel iterative learning control strategies, feedforward control trajectories are generated that enable a precise tracking of desired reference trajectories. Experimental results focusing on the termination of the catalyst heating mode show the effectiveness of the proposed methodology, resulting in a control error reduction above 85%.  相似文献   
2.
This paper explores the problem of random data loss at both input and output sides and proposes a compensation‐based data‐driven iterative learning control (cDDILC) to refrain from deteriorating of the control performance due to the data loss. A linear data model is first established to describe the input‐output dynamics of a repetitive control system in the iteration domain. The linear data model, which only virtually exists in the computer without any physical backgrounds, is employed as a predictive model to estimate and compensate the lost output data. Meanwhile, the lost input data is replaced by the corresponding input of the same time instant in the latest previous iterations. Then, a cDDILC is proposed by introducing two Bernoulli random variables to describe the stochastic data loss at both input and output sides. The proposed cDDILC method is data driven and independent of a precise plant model. Although the design and analysis of the cDDILC start from a MIMO linear repetitive system, one can easily extend the results to a MIMO nonlinear nonaffine one. Theoretical analysis and simulations confirm the efficiency of the proposed cDDILC method.  相似文献   
3.
Batch process, working as a best choice for low‐volume and high‐value products in manufacturing, has been widely used in chemical industries. The actuator faults and time delays often occur in practical production. This paper develops an iterative learning control (ILC) design for a batch process described by two‐dimensional (2D) Roesser system with packet dropouts and time‐varying delays. The phenomenon of actuator faults is regarded as an arbitrary stochastic sequence satisfying the Bernoulli random binary distribution. Firstly, the ILC design for a batch process is transformed into stability analysis for a 2D stochastic system with time‐varying delays. Secondly, for analyzing the stability of 2D stochastic systems, we derive the stability condition in terms of linear matrix inequality. Then, we give a procedure to get the control gain for the ILC design. An injection modeling process as an example with simulations in different cases of data dropout is given to demonstrate the validity of the proposed method. Furthermore, the proposed method has a better result by comparing the existing methods.  相似文献   
4.
5.
Channel noise, including sensor‐to‐controller(SC) noise and controller‐to‐actuator(CA) noise, impacts the convergence of wireless remote iterative learning control (ILC) system significantly. In this paper, the relationship between output error, SC noise and CA noise is obtained firstly by super‐vector formulation, and then the norm of output error vector covariance matrix is employed to analyze the convergence of the system in presence of SC noise and CA noise. Upper bound of the norm at any sample time reveals that the SC noise is accumulated only in iteration domain, while the CA noise is accumulated not only in iteration domain but also in time domain. Furthermore, the accumulated effect of the CA noise in time domain is ruled by system matrices, so the values of which determine the effect of the CA noise is greater or less than that of the SC noise on convergence of the system. Finally, some simulation results are given to illustrate correctness of the result.  相似文献   
6.
考虑数据丢失下非线性多智能体系统的一致性跟踪问题。假设多智能体系统使用固定网络通信拓扑结构,由于通信网络自身限制导致多智能体系统中存在数据丢失现象。将数据丢失现象描述为取值0/1的随机伯努利序列,设计分布式一致性跟踪误差,提出该系统在数据丢失下的P型迭代学习控制算法。采用压缩映射的方法给出收敛性条件,并在理论上分析了跟踪误差的收敛性。仿真结果表明,提出的算法可以实现该系统在有限时间区间上对期望轨迹的完全跟踪,验证了算法的有效性。  相似文献   
7.
针对永磁直线同步电动机直接驱动XY平台系统中存在不确定扰动的问题,单轴上设计了基于干扰观测器的迭代学习控制器.迭代学习控制器可以抑制重复扰动,干扰观测器可以消除非重复扰动的影响,两者结合起来可增强系统对重复和非重复扰动的抑制能力.由于轮廓误差计算模型要求跟踪误差远小于期望轮廓曲率半径,所以设计了混合误差轮廓控制器,跟踪误差较大时只控制各轴位置,跟踪误差足够小时进行轮廓控制.仿真结果表明,该控制方法使直接驱动XY平台具有较强的鲁棒性和较高的轮廓精度.  相似文献   
8.
李向阳 《自动化学报》2014,40(7):1366-1375
针对迭代学习控制(Iterative learning control,ILC)中的初始状态问题,提出了采用有限时间跟踪微分器安排过渡过程方法,根据迭代学习控制中期望轨迹已知的特点,设计了其参数有明显物理意义并且调节方便的有限时间跟踪微分器. 在此基础上,针对一类具有不确定性的非线性时变系统的迭代学习控制问题,提出了具有对不确定项进行估计的迭代学习控制算法,并应用类Lyapunov方法给出了相关定理证明. 仿真结果表明所提出的方法是有效的.  相似文献   
9.
批次过程是一类重要的化工过程.因其本身的灵活性及高效性,被广泛应用于半导体制造、塑料加工、生物制药等领域.针对批次过程控制算法的研究也得到了大批学者的关注.在近三十年中,批次过程控制理论得到了长足的发展.但由于过程本身复杂的动态特性,以及对控制精度要求的提高,现有的理论和方法仍面临着挑战.本文从批次过程的特性出发,分析了算法设计的难点,对几种重要的控制算法进行总结分析,同时讨论了未来可能的发展方向.  相似文献   
10.
基于2维性能参考模型的2维模型预测迭代学习控制策略   总被引:1,自引:0,他引:1  
将迭代学习控制(Iterative learning control, ILC)系统看作一类具有2维动态特性的控制系统,根据模型预测控制(Model predictive control, MPC)和性能参考模型控制思想, 提出了一种基于2维性能参考模型的2维模型预测迭代学习控制系统设计方案.在该控制系统设计方案中,可以通过选择适当的2 维性能参考模型来构造2 维动态变化的设定值信号和预测控制信号,从而引导迭代学习控制系统收敛到合理的控制性能,并有效避 免系统性能收敛过程中控制输入可能发生的剧烈波动.通过对控制系统的结构分析可知,所得的迭代学习控制器本质上是由沿时 间指标的参考模型预测控制器和沿周期指标的迭代学习控制器组成,闭环系统的收敛性等价于一个2维滤波系统的稳定性.数值仿 真结果证明了该设计方案的有效性和鲁棒性.  相似文献   
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