Predictive controller with estimation of measurable disturbances. Application to an olive oil mill

This paper describes the application of a predictive controller that deals with measurable disturbances in the extraction process in an olive oil mill. The work focuses on the thermal part of the process, where the raw material is prepared for the mechanical separation. The system under consideration can be viewed as composed of several changing-level stirred tanks. The paper shows the development of the controller based upon a model obtained from first principles combined with experimental results and validated with real data. Strong disturbances and large time delays appear in the process, so predictive control strategies have been tested under linear and nonlinear simulation. Finally, they have been implemented on the real plant. A study about the consideration of different models for the estimation of measurable disturbances along the prediction horizon has been carried out, showing that a good performance can be obtained by the use of an appropriate model. A new idea that can improve periodic disturbance rejection in Model Based Predictive controllers is presented.     

乙苯脱氢反应器系统的广义预测控制

本文详细介绍了在乙苯脱氢反应器计算机控制系统中引人多输入、单输出系统(MISO) 的广义预测控制算法,以及在扰动和负荷变化下在线控制情况.该算法简捷、直观,具有良好 的控制效果.文中还给出了算法的推导过程,讨论了柔化参数α和加权系数λ对控制质量的 影响.     

Constrained control strategies for disturbance rejection in a solar furnaces

This paper addresses the temperature control problem in a solar furnace. In particular, two control strategies for the disturbances rejection (represented by the variation of the input energy provided by the Sun, mainly because of passing clouds and the solar daily cycle) are proposed, based on a two-degrees-of-freedom scheme. The first one is based on generalized predictive control, where a nonlinear model is employed for free response prediction while a linearized model is used for the computation of the forced response. Amplitude and slew-rate constraints on both the control variable and the output of the system are taken into account. The second one is a constrained control strategy where both the process input and output constraints are taken into account explicitly. In both cases an adaptation of the most significant process parameter is performed. Simulation and experimental results show the effectiveness of the methodologies.¹     

Predictive proportional nonlinear control for stable-target tracking of a mobile robot: an experimental study

Two new types of control method have been developed based on model predictive control for stable-target tracking of a nonholonomic mobile robot. One method (Method 1) is a new nonlinear control method. This was developed based on model predictive control (predictive nonlinear control) to predict the next position of a mobile robot using the current velocities of the right and left wheels. This technique uses a tuning guideline in predictive nonlinear control. The other method (Method 2) is a combination of Method 1 and proportional control (predictive proportional nonlinear control). Method 2 involves a tuning guideline not only in a predictive nonlinear controller, but also in a proportional controller. In this technique, the selection of a tuning guideline in the proportional controller is enhanced, and thereby increases the control action in closed-loop responses. In Method 1, the nonlinear controller is derived from Liapunov stability theory, and is used to control the linear and angular velocities for locomotion control. Tuning parameters in the nonlinear controller (in Method 1) are selected to satisfy various design criteria, such as stability, performance, and robustness. Method 1 has certain limitations that result in a decrease of the performance criteria specified. Strong nonlinearities in the mobile robot system result in accumulated errors. To enhance performance further, we developed Method 2 as the solution for decreasing cumulative errors. Hence, the proportional controller is added to Method 1 in the closed-loop form in order to eliminate errors. The advantage of Method 2 is that it can cope with strong nonlinearities in the mobile vehicle system. The results of the performances of Method 1 and Method 2 are shown to demonstrate the effectiveness of both methods, and also the better performance of Method 2. The two new methods are effective in stable-target tracking, yielding an increase in performance and stability.     

一类不确定非线性大系统的分散鲁棒控制器设计

基于Backstepping方法，设计了一类具有不确定性扰动和不确定性关联项的非线性大系统的分散鲁棒稳定控制器。非线性大系统的关联项为时变有界非线性函数且不确定性扰动以仿射非线性方程的形式引入。为了提高系统的控制效果。将：Backstepping递推设计方法与L2增益控制相结合，所设计的分散鲁棒控制器不仅使每个子系统的状态向量跟踪一个指定的期望轨迹。而且还使系统的不确定性干扰具有L2增益控制。     

A probabilistically constrained model predictive controller

We propose a novel control algorithm, probabilistically constrained predictive control, to deal with the uncertainties of system disturbances. The output is to be controlled in the constrained range with a desired probability. Under the assumption of a linear system, the formulated joint probabilistically constrained problem is convex. Thus, it can be solved with a nonlinear programming solver. The probabilities and gradients of the constraints, composed of disturbance sequences with multivariate normal distribution, are computed using an efficient simulation approach. The results of a test problem show the effectiveness of the proposed algorithm.     

Composite antidisturbance control for nonlinear systems via nonlinear disturbance observer and dissipative control

This paper investigates the design problem of composite antidisturbance control for a class of nonlinear systems with multiple disturbances. First, a novel nonlinear disturbance observer‐based control scheme is constructed to estimate and compensate the disturbance modeled by the nonlinear exosystem. Then, by combining the dissipative control theory, a linear matrix inequality‐based design method of composite antidisturbance control is developed such that the augmented system is exponentially stable in the absence of unmodeled disturbances, and is dissipative in the presence of unmodeled disturbances. In this case, the original closed‐loop system is exponentially stable in the presence of modeled disturbances. Subsequently, two special cases of composite antidisturbance control are derived with H_∞ performance and passivity, respectively. Finally, the proposed method is applied to control A4D aircraft to show its effectiveness.     

Inherent robustness properties of quasi-infinite horizon nonlinear model predictive control

We consider inherent robustness properties of model predictive control (MPC) for continuous-time nonlinear systems with input constraints and terminal constraints. We show that MPC with a nominal prediction model and persistent but bounded disturbances has some degree of inherent robustness when the terminal control law and the terminal penalty matrix are chosen as the linear quadratic control law and the related Lyapunov matrix, respectively. We emphasize that the input constraint sets can be any compact set rather than convex sets, and our results do not depend on the continuity of the optimal cost function or of the control law in the interior of the feasible region.     

Optimal tracking control for nonlinear large-scale systems with persistent disturbances

This paper studies the optimal control with zero steady-state error problem for nonlinear large-scale systems affected by external persistent disturbances. The nonlinear large-scale system is transformed into N nonlinear subsystems with interconnect terms. Based on the internal model principle, a disturbance compensator is constructed such that the ith subsystem with external persistent disturbances is transformed into an augmented subsystem without disturbances. According to the sensitivity approach, the optimal tracking control law for the ith nonlinear subsystem can be obtained. The optimal tracking control law for the nonlinear large-scale systems can be obtained. A numerical simulation shows that the method is effective.     

Input-to-state stability of robust receding horizon control with an expected value cost

This paper is concerned with the stability of a class of receding horizon control (RHC) laws for constrained linear discrete-time systems subject to bounded state disturbances and convex state and input constraints. The paper considers the class of finite horizon feedback control policies parameterized as affine functions of the system state, calculation of which can be shown to be tractable via a convex reparameterization. When minimizing the expected value of a finite horizon quadratic cost, we show that the value function is convex. When solving this optimal control problem at each time step and implementing the result in a receding horizon fashion, we provide sufficient conditions under which the closed-loop system is input-to-state stable (ISS).     

Nonlinear tracking and input disturbance rejection with application to pH control

This paper presents a nonlinear observer-based controller configuration for a class of nonlinear single-input single-output systems with certain observability and stability properties. Particular attention is paid to the effect of input disturbances typically present in chemical processes. It is pointed out that conventional feedback linearizing internal model controllers reject output disturbances linearly, but that they can experience significant performance degradation in the presence of input disturbances. This problem is shown to originate in the lack of the linear superposition property. It is proven that the proposed control architecture rejects constant input disturbances from the control signal and then recovers linear reference tracking. The convenient tuning and improved performance are illustrated for pH neutralization.     

倒立摆系统稳定控制之研究

多级倒立摆系统作为一个典型的非线性、多变量、高阶次、强耦合和自然不稳定的复杂被控系统,一直是人们检验、比较各种控制理论和方法的理想实验平台,半个多世纪以来,国内外许多机构对它进行了广泛的研究,积累了丰富的资料。这里对多级倒立摆稳定控制的研究现状进行总结,探讨了它的发展趋势。目前主要的控制方法有线性控制、预测控制和智能控制三类,智能控制是当前研究的主流,它包括模糊控制、拟人控制、计算智能控制、云模型控制等。继续深入研究各种智能控制方法及其组合应用是今后的发展方向。     

具有死区非线性补偿的预测控制

通过在死区非线性系统中引入非线性补偿器,讨论了具有死区非线性补偿器非线性系统预测控制问题。仿真结果表明该方法的有效性。     

空天飞空天飞行器的自适应变论域模糊预测控制行器的自适应变论域模糊预测控制

考虑一类非线性不确定系统的变论域模糊预测控制问题.根据跟踪误差在线调整伸缩因子,使变论域模糊系统一致逼近被控对象中的未知干扰和不确定因素.通过引入鲁棒自适应控制器,消除了模糊建模误差,提高了系统的动态性能.基于泰勒展开的非线性预测控制律,避免了繁重的计算负担.基于Lyapunov理论,给出了伸缩因子的σ调整律,并证明了闭环系统一致最终有界.最后,将该算法用于空天飞行器(ASV)姿态控制系统的设计,仿真结果表明了该算法的有效性.     

Receding horizon flight control for trajectory tracking of autonomous aerial vehicles

This paper addresses the implementation of a predictive control strategy for Unmanned Air Vehicles in the presence of bounded disturbances. The goal is to prove the feasibility of such a real-time optimization-based control design and to demonstrate its tracking capabilities for the nonlinear dynamics with respect to a reference trajectory which is pre-specified via differential flatness. In order to benefit from the computational advantages of the linear predictive control formulations, an off-line linearization strategy of the nonlinear model of the vehicle along the flat trajectory is employed. The proposed method exhibits effective performance validated through software-in-the-loop simulations and real flight tests on different Unmanned Aerial Vehicles (UAVs).     

Robust control approach for input–output linearizable nonlinear systems using high‐gain disturbance observer

This paper addresses a robust control approach for a class of input–output linearizable nonlinear systems with uncertainties and modeling errors considered as unknown inputs. As known, the exact feedback linearization method can be applied to control input–output linearizable nonlinear systems, if all the states are available and modeling errors are negligible. The mentioned two prerequisites denote important problems in the field of classical nonlinear control. The solution approach developed in this contribution is using disturbance rejection by applying feedback of the uncertainties and modeling errors estimated by a specific high‐gain disturbance observer as unknown inputs. At the same time, the nonmeasured states can be calculated from the estimation of the transformed system states. The feasibility and conditions for the application of the approach on mechanical systems are discussed. A nonlinear multi‐input multi‐output mechanical system is taken as a simulation example to illustrate the application. The results show the robustness of the control design and plausible estimations of full‐rank disturbances.Copyright © 2012 John Wiley & Sons, Ltd.     

非线性系统高阶迭代学习算法

结合迭代学习控制算法中的开环和闭环方案，本文针对更一般的非线性系统，讨论高阶算法的广泛适用性。理论和仿真结果表明了高阶算法在输出跟踪和干扰抑制方面的有效性。     

Explicit nonlinear predictive control for a magnetic levitation system

The paper presents a methodology for the construction of an explicit nonlinear control law via approximation of the nonlinear constrained finite‐time optimal control (CFTOC). This is achieved through an approximate mapping of a general nonlinear system in a set of linear piecewise affine (PWA) systems. The key advantages of this methodology are two‐fold. First, the construction of an analytic solution of the CFTOC problem leads to an efficient explicit implementation. Second, by taking advantage of model predictive control's systematic fashion to handle constraints, an improved performance can be obtained for the closed‐loop system. The proposed theory is applied in real‐time for a system with fast dynamics: a magnetic levitation benchmark. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

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.     

Continuous Robust Control for Series ElasticActuator With Unknown Payload Parametersand External Disturbances

In this paper, the torque tracking control problem for a class of series elastic actuators (SEAs) in the presence of unknown payload parameters and external disturbances is investigated. The uncertainties/disturbances rejection problem for SEAs is addressed from the view of a continuous nonlinear robust control development. Specifically, based on the analysis of a nonlinear SEA, the generic dynamics of SEA systems is described and a novel nonlinear control framework for SEAs is constructed. Then a RISE (robust integral of the sign of the error)-based second-order filter is introduced to synthesize the control law. Moreover, the control performance is theoretically ensured by Lyapunov analysis. Finally, some experimental results are included to demonstrate the superior performance of the proposed control method, in terms of transient response and robustness.