Robust adaptive control of nonlinearly parametrized multivariable systems with unmatched disturbances

A new robust adaptive control scheme is developed for nonlinearly parametrized multivariable systems in the presence of parameter uncertainties and unmatched disturbances. The developed control scheme employs a new integrated framework of a functional bounding technique for handling nonlinearly parametrized system dynamics, an adaptive parameter estimation algorithm for dealing with parameter uncertainties, a nonlinear feedback controller structure for stabilization of interconnected system states, and a robust adaptive control design for accommodating unmatched disturbances. It is proved that such a new robust adaptive control scheme is capable of ensuring the global boundedness and mean convergence of all closed‐loop system signals. A complete simulation study on an air vehicle system with nonlinear parametrization in the presence of an unmatched wind disturbance is conducted, and its results verify the effectiveness of the proposed robust adaptive control scheme.     

Robust adaptive fault tolerant control for a process with actuator faults

This paper studies design and implementation of an enhanced multivariable adaptive control scheme for an uncertain nonlinear process exposed to actuator faults. For adaptive fault compensation, a model reference adaptive control (MRAC) strategy is utilized as main controller. A new adaptation algorithm making possible to improve transient performance of adaptive control is integrated to the controller. With the help of further modifications, some restrictive conditions on multivariable adaptive design are relaxed so that the system requires less plant information. The resulting controller has a simpler structure than the other matrix factorization based controllers. At the final stage of design, a robust adaptive control scheme is obtained with consideration of practical implementation problems such as sensor noises, external disturbances and unmodeled​ system dynamics. It is proved that the controller guarantees closed-loop signal boundedness and asymptotic output tracking. Real-time experiment results acquired from quadruple tank benchmark system are presented in order to exhibit the effectiveness of the proposed scheme.     

Adaptive control of a class of multivariable nonlinear systems andconvergence analysis

Adaptive control is discussed of a class of multivariable nonlinear systems which can be characterized by a stochastic multivariable Hammerstein model whose linear part possesses an arbitrary interactor matrix. A simple suboptimal control law is derived which provides an efficient way to control a multivariable Hammerstein model whose linear part is not necessarily minimum phase. A direct adaption scheme is presented to implement the control law, and the global convergence of the algorithm is established     

约束非线性系统多变量最优控制研究

近年来，非线性规划算法在最优控制领域中正受到越来越多的关注。该文深人研究并实现了一种新的非线性规划算法——FSQP算法，该算法具有所有迭代点均处于可行域之内、收敛速度较快的特点。提出了一种基于FSQP算法的约束非线性系统最优控制方法。然后，运用该方法解决了带有约束的复杂非线性系统的多变量时间最优控制问题，并通过计算机仿真表明了该控制算法的可行性和良好的控制效果。     

Multivariable predictive control of a pressurized tank using neural networks

The behavior of a multivariable predictive control scheme based on neural networks applied to a model of a nonlinear multivariable real process, consisting of a pressurized tank is investigated in this paper. The neural scheme consists of three neural networks; the first is meant for the identification of plant parameters (identifier), the second one is for the prediction of future control errors (predictor) and the third one, based on the two previous, compute the control input to be applied to the plant (controller). The weights of the neural networks are updated on-line, using standard and dynamic backpropagation. The model of the nonlinear process is driven to an operation point and it is then controlled with the proposed neural control scheme, analyzing the maximum range over the neural control works properly.     

一类未知多变量非线性系统的动态神经网络自适应控制

对一类未知的非线性的多变量系统，提出了用动态神经网络实现直接自适应控制的策略，基于Ｌｙａｐｕｎｏｖ理论，获得一个稳定并且连续的学习律，避免了递归训练过程，闭环系统被证明是鲁棒稳定的，跟踪误差收敛到一个小的残集，这种方法的特点是即不需要离线学习阶段也不要求初始的参数误差足够小，仿真结果验证了提出的动态网络的自适应控制算法的有效性。     

基于神经网络的多变量非线性自适应解耦控制研究

提出神经网络前馈自适应解耦控制算法．该算法将多变量非线性系统在平衡点处利用Taylor公式展开．分为线性部分和高阶非线性部分。这样．将高阶非线性部分的影响视为可测干扰，采用前馈补偿的方法加以消除．就可以借助多变量线性系统的自适应解耦控制算法．实现多变量非线性系统的自适应解耦控制．这种方法可以取消被解耦系统为最小相位的限制。     

结晶器多变量耦合系统的自抗扰控制

主要研究新颖实用非线性自抗扰控制算法,在结晶器多变量耦合系统中的应用.自抗扰控制主要特性是实时估计对象模型摄动和外扰的总和作用量,并在控制信号中补偿掉,实现不确定性强非线性对象的实时动态反馈线性化.结合控制对象,建立了结晶器多变量耦合自抗扰控制系统.数值仿真试验表明自抗扰耦合控制的协调性、自适应跟随性和抗干扰性优于传统的PID解耦控制.     

Constrained nonlinear multivariable control of a fluid catalytic cracking process

In this paper, a nonlinear constrained optimization strategy is proposed and applied to the reactor-regenerator section of a fluid catalytic cracking (FCC) unit. A nonlinear dynamic model of the fluid catalytic cracking process was used for the dynamic analysis of the plant and nonlinear multivariable control system. The model realistically simulates the riser-reactor and the one stage regenerator by assembling the mass and energy balances on the system of reactions. The model results were tested in a real-time application and the results were used to provide the initial values for the nonlinear control system design. A dynamic parameter update algorithm was used to reduce the effect of large modelling errors by regularly updating the model parameters. The constrained nonlinear optimization algorithm and strategies were tested in real-time on the fluid catalytic cracking reactor-regenerator. The results compared favourably to those from a linear multivariable controller.     

Unscented Kalman filter based nonlinear model predictive control of a LDPE autoclave reactor

This paper presents a multivariable nonlinear model predictive control (NMPC) scheme for the regulation of a low-density polyethylene (LDPE) autoclave reactor. A detailed mechanistic process model developed previously was used to describe the dynamics of the LDPE reactor and the properties of the polymer product. Closed-loop simulations are used to demonstrate the disturbance rejection and tracking performance of the NMPC algorithm for control of reactor temperature and weight-averaged molecular weight (WAMW). In addition, the effect of parametric uncertainty in the kinetic rate constants of the LDPE reactor model on closed-loop performance is discussed. The unscented Kalman filtering (UKF) algorithm is employed to estimate plant states and disturbances. All control simulations were performed under conditions of noisy process measurements and structural plant–model mismatch. Where appropriate, the performance of the NMPC algorithm is contrasted with that of linear model predictive control (LMPC). It is shown that for this application the closed-loop performance of the UKF based NMPC scheme is very good and is superior to that of the linear predictive controller.     

A multivariable MRAC scheme with application to a nonlinear aircraft model

This paper revisits the multivariable model reference adaptive control (MRAC) problem, by studying adaptive state feedback control for output tracking of multi-input multi-output (MIMO) systems. With such a control scheme, the plant-model matching conditions are much less restrictive than those for state tracking, while the controller has a simpler structure than that of an output feedback design. Such a control scheme is useful when the plant-model matching conditions for state tracking cannot be satisfied. A stable adaptive control scheme is developed based on LDS decomposition of the high-frequency gain matrix, which ensures closed-loop stability and asymptotic output tracking. A simulation study of a linearized lateral-directional dynamics model of a realistic nonlinear aircraft system model is conducted to demonstrate the scheme. This linear design based MRAC scheme is subsequently applied to a nonlinear aircraft system, and the results indicate that this linearization-based adaptive scheme can provide acceptable system performance for the nonlinear systems in a neighborhood of an operating point.     

1000MW超超临界机组的多变量约束预测控制的研究

针对1000MW超超临界机组,分析了机组协调控制的3输入3输出系统,研究了机组系统的双层结构多变量约束预测控制(multivariable constrained predictive control,MCPC),给出了基于阶跃响应的多变量约束预测控制方法和具体算法,并给出了仿真效果.最后与传统协调控制进行了对比,仿真结果表明了算法的有效性.     

一类多变量非线性动态系统的模糊自适应控制

对一类非线性多变量未知动态系统，提出了一种模糊处在适应控制策略。证明了该控制算法能保证闭环系统稳定，跟踪误差收敛。     

Multivariable extension of global finite‐time HOSM based differentiator for output‐feedback unit vector and smooth binary control

In this paper, we propose a unit vector control law by output feedback to solve the problem of global exact output tracking for a class of multivariable uncertain plants with nonlinear disturbances. In order to face the nonuniform arbitrary relative degree obstacle, we extend our earlier estimation scheme based on global finite‐time differentiators using dynamic gains to a multivariable architecture. A diagonally stable condition over the system high‐frequency gain (HFG) matrix has to be assumed. Preserving the simplicity of its mono variable framework, variable gain super‐twisting algorithm (STA) is employed to obtain the robust and exact multivariable differentiator. Moreover, state‐norm observers for the unmeasured state variables are constructed to upper bound the disturbances as well as to update the differentiator gains, being both state dependent. Uniform global exponential stability and ultimate exact tracking are proved. As an alternative to chattering alleviation, we appeal to the Emelyanov's concept of binary control in order to obtain a continuous control signal replacing the unit vector function in the controller by a high‐gain gradient adaptive law with parameter projection. As shown in the existing literature for mono variable systems, the proposed multiparameter binary‐adaptive formulation tends to the unit vector control as the adaptation gain increases to infinity, also smoothing the transition from adaptive to sliding mode control. A numerical example is portrayed to illustrate the potentialities of the developed multivariable techniques.     

一种基于PSO的自适应神经网络预测控制

针对非线性系统,提出了一种基于微粒群优化(PSO)的自适应神经网络预测控制方法.采用对角递归网络(DRNN)对非线性系统进行建模,并利用扩展卡尔曼滤波(EKF)递推估计算法在线计算网络模型参数的Jacobian矩阵以实现模型参数的自适应.利用PSO算法在线优化求解非线性系统的预测控制律,以克服传统基于梯度法的非线性规划方法求解预测控制律时对初始条件非常敏感的缺点.生化发酵过程的仿真结果表明,所提出的控制方法具有良好的跟踪能力和抗干扰能力.     

基于并行支持向量机的多变量非线性模型预测控制

提出一种基于并行支持向量机的多变量系统非线性模型预测控制算法．首先，通过考虑输入、输出间的耦合，建立基于并行支持向量机的多步预测模型；然后，将该模型用于非线性预测控制,提出新的适用于并行预测模型的反馈校正策略，得到最优控制律．连续搅拌槽式反应器（CSTR）的控制仿真结果表明,该算法的性能优于基于并行神经网络的非线性模型预测控制和基于集成模型的非线性模型预测控制．     

Nonlinear multivariable adaptive control using multiple models and neural networks

In this paper, a multivariable adaptive control approach is proposed for a class of unknown nonlinear multivariable discrete-time dynamical systems. By introducing a k-difference operator, the nonlinear terms of the system are not required to be globally bounded. The proposed adaptive control scheme is composed of a linear adaptive controller, a neural-network-based nonlinear adaptive controller and a switching mechanism. The linear controller can assure boundedness of the input and output signals, and the neural network nonlinear controller can improve performance of the system. By using the switching scheme between the linear and nonlinear controllers, it is demonstrated that improved performance and stability can be achieved simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.     

Active fault tolerant control using state-space self-tuning control approach

This paper presents a new fault tolerant control scheme for unknown multivariable stochastic systems by modifying the conventional state-space self-tuning control approach. For the detection of faults, a quantitative criterion is developed by comparing the innovation process errors occurring in the Kalman filter estimation algorithm, which, for faulty system recovery, a weighting matrix resetting technique is developed by adjusting and resetting the covariance matrices of the parameter estimate obtained in the Kalman filter estimation algorithm to improve the parameter estimation of the faulty systems. The proposed method can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection. The modified state-space self-tuning control scheme can be applied to the multivariable stochastic faulty system without requiring prior knowledge of system parameters and noise properties.     

Nonlinear model predictive control using parameter varying BP-ARX combination model

A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.     

An algorithm for non-linear space—Time nuclear reactor control

A novel multivariable control algorithm for non-linear space-time nuclear reactor dynamics is proposed in this paper. The multivariable control algorithm is based on a mathematical model of the nuclear reactor which includes: a single energy group of neutrons, delayed neutron precursors, iodine, xenon and thermal-hydraulic feedback. The multivariable control algorithm is composed of non-linear time-varying feedforward and feedback control signals, a reference model of the nuclear reactor and a dynamic observer. The non-linear proportional plus integral feedback controller forces the nuclear reactor to follow the response of the reference model. The dynamic observer estimates the unmeasurable state variables. The feedforward and feedback control signals are determined in a novel approach by specifying the form of the closed-loop response of the neutron density variables. By virtue of the multivariable control algorithm the closed-loop differential equations are linear and time-varying. A linear stability analysis for base-load and load-cycle operation indicates that the closed-loop system is stable provided that the thermal-hydraulic subsystem is inherently stable. The simulated dynamic response indicates that the multivariable control algorithm provides excellent response characteristics.