模型跟踪广义预测鲁棒自适应控制器

本文采用滤波CARMA模型,基于内模原理,提出了一种新的广义预测鲁棒自适应控制器,并分析了闭环系统性能,在新的控制器中,引入适当的前馈作用,使得跟踪和调节问题解耦,利用部分状态跟踪、模型参考以及极点配置方法解决跟踪问题,利用多步预测滚动优化方法解决调节问题;适当选择滤波器可以保证对平稳随机扰动有满意的响应,减少可调参数对闭环系统响应的影响,增强系统对未建模动态的鲁棒性,仿真结果表明:该控制器对确定性和非平稳随机扰动具有不变性,对系统时延和阶次变化具有鲁棒性,适用于非最小相位和开环不稳定系统。     

基于CARMA模型的水下滑翔机自适应控制

水下滑翔机在设计和海中实际应用过程中存在模型参数的不确定性和外部环境的复杂性,要求控制器具有良好的自适应性,在水下滑翔机三维定常运动模型基础上,以俯仰回路模型为例设计了基于CARMA模型的改进广义预测自适应控制器.上述控制器应用遗忘因子递推最小二乘法对系统参数进行实时辨识.在对控制器进行了仿真后,对CARMA模型参数全部突变这一恶劣情况进行仿真并与PID控制对比,结果表明改进控制算法具有良好的快速性、稳定性和自适应性.     

焦炉荒煤气传输系统多模型PI-GPC的仿真研究

充分考虑大多数焦炉荒煤气传输系统的对象特性与运行工况密切相关的实际特点,利用PI控制和广义预测控制的优点推导出一种改进型的广义预测控制策略。同时,在不同工况点下建立相应的控制器,并给出一种改进的控制器切换方法和切换之后的控制量补偿方法,以解决因为控制器切换和工艺设定值的改变而导致的切换扰动和系统的震荡、超调。通过利用改进的多模型广义预测控制,使得系统更具有鲁棒性、快速性。仿真结果证实了该控制策略的优越性。     

一种基于未知模型的广义预测控制算法

研究未知被控对象具有动态调整、参数时变以及受到外界干扰时,采用广义自适应预测控制算法(GPC)对系统进行控制。运用基于CARMA模型的参数在线动态辨识,在线时实算出未知对象的参数模型,再运用预测控制算法,计算出当前的控制量;再滚动优化,使未知系统具有良好的控制性能。并且通过Matlab分析仿真证明了广义预测控制的全局稳定性、收敛性和鲁棒性。     

基于神经网络与多模型的非线性自适应广义预测控制

针对一类不确定非线性离散时间动态系统, 提出了基于神经网络与多模型的非线性广义预测自适应控制方法. 该自适应控制方法由线性鲁棒广义预测自适应控制器, 神经网络非线性广义预测自适应控制器和切换机制三部分构成. 线性鲁棒广义预测自适应控制器保证闭环系统的输入输出信号有界, 神经网络非线性广义预测自适应控制器能够改善系统的性能. 切换策略通过对上述两种控制器的切换, 保证系统稳定的同时, 改善系统性能. 给出了所提自适应方法的稳定性和收敛性分析. 最后通过仿真实例验证了所提方法的有效性.     

柔性关节微操作机器人自适应模糊预测控制

对一种柔性关节微操作机器人系统提出了多输入多输出直接自适应模糊广义预测控制方法,此方法先基于机器人理论模型设计出广义预测控制器,再构造直接自适应模糊控制器逼近广义预测控制器,并用机器人视觉误差信息对控制器参数和广义误差向量估计值中的未知向量进行自适应调整,以增强对建模误差的鲁棒性,并证明了所设计的控制器可使微操作机器人跟踪时变参考轨迹时的广义误差估计值收敛到原点的小邻域内,以达到控制要求,仿真结果验证了此方法的有效性.     

高空作业平台广义预测自适应控制及联合仿真*

为减少工程车辆控制系统开发周期和成本,以某型54m高空作业平台电液比例调平系统为研究对象,利用ADAMS软件建立作业机构多体动力学模型;采用AMESim软件建立电液比例调平系统模型;通过MATLAB/Simulink设计,采用改进的广义预测自适应控制的闭环控制器;以AMESim作为主仿真环境,通过软件接口将多体动力学模型和控制系统模型集成到AMESim中进行联合仿真。仿真结果表明,闭环控制器较常规PID控制器具有良好的动态特性,对模型失配和负载扰动表现出更强的适应性和鲁棒性,同时也证明了联合仿真的有效性。     

基于扰动补偿的永磁同步电机预测位置控制

为了提高永磁同步电机伺服系统的位置跟踪精度和抗扰性,设计了一种新的预测控制策略。首先,建立了永磁同步电机的数学模型,并在电机模型中建立了扰动的集总项;其次,设计了广义预测控制器,在控制器中考虑了扰动的影响,增加了控制器的抗干扰能力;最后,引入了非线性扰动观测器,对扰动进行估计并补偿。仿真与实验结果表明,所提出的预测控制方法即使在不同的运行情况下,依然能使电机保持良好的位置控制精度和抗干扰能力。     

高速列车自动驾驶前馈自适应广义预测控制方法研究

高速列车自动驾驶（ATO）系统本质上是强非线性和不确定性的系统,针对高速列车模型参数非线性和时变性等特点,文章提出了一种前馈自适应广义预测控制（FA-GPC）的方法对ATO系统进行动态优化控制,并设计了一种带约束的多目标预测控制器。首先,基于列车多质点模型,分析附加阻力的改变对列车运行的影响;然后,结合列车运行过程中的速度跟踪精度、停车精度及运行舒适性等关键指标,构建包含控制输入约束的多目标性能指标函数,设计基于多目标函数的前馈广义预测速度跟踪控制算法,解决了由于附加阻力变化导致控制器超调的问题并加快了控制收敛速度。由于列车运行过程中受外界环境、乘客流动等因素影响,阻力变化大,难以建立精确的数学模型,因此采用带约束的变遗忘因子递推最小二乘法来辨识出列车控制系统在不同工况下受控自回归积分滑动平均模型（CARIMA）,进而提高控制系统的鲁棒性。仿真结果表明,相比传统的无前馈GPC和PID控制器,前馈广义控制器在不同线路条件下巡航控速速度跟踪精度在±0.5 km/h范围内,具有良好的跟踪性;在强扰动情况下通过引入自适应改进的前馈广义预测控制算法,具有较强的鲁棒性。     

基于参数辨识的自适应解耦控制算法的研究

多变量模型的复杂结构、强耦合性、被控对象参数的未知、慢时变等问题要求控制器必须具有良好的自适应性,针对以上问题提出了一种基于改进的广义最小方差闭环自适应解耦控制器实现更好的自适应,其由参数可调的控制器和自适应控制律组成,此控制器通过将闭环系统方程的传递函数矩阵等于期望的对角矩阵来实现解耦,同时改进的辨识算法可进行在线辨识控制器的参数实现同步自适应解耦。通过以CARMA为多变量控制模型,采用该方法进行仿真有效的解决了多变量之间的耦合性。结果表明该方法能够适应相应的变化,跟踪性能较好,且具备良好的解耦能力,进而保证了闭环系统的稳定性,从而验证了此方法能够效提高控制系统的稳定性和鲁棒性。     

Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control

The performance of modern control methods, such as model predictive control, depends significantly on the accuracy of the system model. In practice, however, stochastic uncertainties are commonly present, resulting from inaccuracies in the modeling or external disturbances, which can have a negative impact on the control performance. This article reviews the literature on methods for predicting probabilistic uncertainties for nonlinear systems. Since a precise prediction of probability density functions comes along with a high computational effort in the nonlinear case, the focus of this article is on approximating methods, which are of particular relevance in control engineering practice. The methods are classified with respect to their approximation type and with respect to the assumptions about the input and output distribution. Furthermore, the application of these prediction methods to stochastic model predictive control is discussed including a literature review for nonlinear systems. Finally, the most important probabilistic prediction methods are evaluated numerically. For this purpose, the estimation accuracies of the methods are investigated first and the performance of a stochastic model predictive controller with different prediction methods is examined subsequently using multiple nonlinear systems, including the dynamics of an autonomous vehicle.     

Behaviour model control for cascaded processes: Application to an electrical drive

Behaviour model control (BMC) is known to increase the robustness of a process control. It has already been applied to electrical drives for single-loop controls. This paper introduces the BMC among others model control strategies and extends its single-loop structure to more complex controls, which need several loops. This extension is applied to a double loop control of a DC machine drive. Simulations and experimental results show that the suggested double-loop BMC yields better results than a classical control, increasing robustness against parameter variations and external disturbances.     

Control structure selection and model predictive control of the Weyerhaeuser digester problem

The Weyerhaeuser digester problem (WDP) is a simplified digester model which captures the major dynamic characteristics of a continuous pulp digester and has been offered to the research community as an industrial challenge problem. The control objective is to minimize variations in the Kappa # for unmeasured disturbances. A systematic approach to selecting the manipulated inputs and secondary measurements using robust control analysis tools has been successfully applied to the WDP. A multi-rate data-sampling linear model predictive controller using the selected input/measurement pairings provided robust closed-loop performance for the rejection of both deterministic and stochastic unmeasured disturbances.     

Composite predictive flight control for airbreathing hypersonic vehicles

The robust optimised tracking control problem for a generic airbreathing hypersonic vehicle (AHV) subject to nonvanishing mismatched disturbances/uncertainties is investigated in this paper. A baseline nonlinear model predictive control (MPC) method is firstly introduced for optimised tracking control of the nominal dynamics. A nonlinear-disturbance-observer-based control law is then developed for robustness enhancement in the presence of both external disturbances and uncertainties. Compared with the existing robust tracking control methods for AHVs, the proposed composite nonlinear MPC method obtains not only promising robustness and disturbance rejection performance but also optimised nominal tracking control performance. The merits of the proposed method are validated by implementing simulation studies on the AHV system.     

An efficient method for stochastic optimal control with joint chance constraints for nonlinear systems

Stochastic model predictive control hinges on the online solution of a stochastic optimal control problem. This paper presents a computationally efficient solution method for stochastic optimal control for nonlinear systems subject to (time‐varying) stochastic disturbances and (time‐invariant) probabilistic model uncertainty in initial conditions and parameters. To this end, new methods are presented for joint propagation of time‐varying and time‐invariant probabilistic uncertainty and the nonconservative approximation of joint chance constraint (JCC) on the system state. The proposed uncertainty propagation method relies on generalized polynomial chaos and conditional probability rules to obtain tractable expressions for the state mean and covariance matrix. A moment‐based surrogate is presented for JCC approximation to circumvent construction of the full probability distribution of the state or the use of integer variables as required when using the sample average approximation. The proposed solution method for stochastic optimal control is illustrated on a nonlinear semibatch reactor case study in the presence of probabilistic model uncertainty and stochastic disturbances. It is shown that the proposed solution method is significantly superior to a standard random sampling method for stochastic optimal control in terms of computational requirements. Furthermore, the moment‐based surrogate for the JCC is shown to be substantially less conservative than the widely used distributionally robust Cantelli‐Chebyshev inequality for chance constraint approximation.     

A robust economic MPC for changing economic criterion

This note presents a robust economic model predictive control controller suitable for changing economic criterion. The proposal ensures feasibility under any change of the economic criterion, thanks to the use of artificial variables and a relaxed terminal constraint, and robustness in presence of additive bounded disturbances. The resulting robust formulation considers a nominal prediction model and restricted constraints (in order to account for the effect of additive disturbances). The controlled system under the proposed controller is shown to be input‐to‐state stable in the sense that it is asymptotically steered to an invariant region around the best admissible steady state. An illustrative example shows the benefits and the properties of the proposed controller.     

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems, it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances. In this paper, to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control(ILC), a novel real-time iterative compensation(RIC) control framework is proposed for precision motion systems wit...     

CARMA模型在液位控制系统中的应用

以比例阀的输出为系统输入,液位值为系统输出,对液位控制系统进行CARMA建模研究.选用AIC准则作为系统模型阶次的选择原则,以最小二乘法来辨识模型参数,辨识了系统的CARMA模型.模型的预测输出和实际输出的比较结果证实了CARMA建模在液位控制系统中的有效性.