Dynamic simulation and nonlinear control of a rigorous batch reactive distillation

This work deals with the dynamics and control of a high-purity batch distillation column with chemical reaction. A heterogeneous esterification reaction between the acetic acid and butanol takes place to produce butyl acetate. The process model is formulated considering variable liquid holdup, UNIQUAC model for thermodynamic property predictions, nonlinear Francis weir formula for tray hydraulics, pseudohomogeneous model to represent the reaction kinetics and rigorous energy balance. A structured and simple iterative approach is devised to compute the vapor flows with the fast convergence, under the rigorous energy balance.The representative column is treated with a distillate policy based on which, the lightest product, water is removed as distillate at the starting of production phase. As a consequence, the column gets progressively richer with the main product, butyl acetate. In addition, almost complete conversion of the limiting reactant is achieved. In order to maintain the product purity at the top, a nonlinear generic model controller (GMC) in two different forms has been proposed. Finally, a comparative closed-loop performance is addressed. It is shown that the control scheme, along with the effective distillate strategy, leads to almost complete conversion of ingredients and high-purity products.     

Receding horizon online optimization for torque control of gasoline engines

This paper proposes a model-based nonlinear receding horizon optimal control scheme for the engine torque tracking problem. The controller design directly employs the nonlinear model exploited based on mean-value modeling principle of engine systems without any linearizing reformation, and the online optimization is achieved by applying the Continuation/GMRES (generalized minimum residual) approach. Several receding horizon control schemes are designed to investigate the effects of the integral action and integral gain selection. Simulation analyses and experimental validations are implemented to demonstrate the real-time optimization performance and control effects of the proposed torque tracking controllers.     

Two-stage robust control of substrate concentration for an activated sludge process

A two-stage robust control scheme improving the performance of an Activated Sludge Process is proposed. In the first stage, asymptotic command following the substrate concentration with simultaneous attenuation of the fluctuations of the dissolved oxygen concentration is assured. The first stage is a pure dynamic controller. The second stage is a PID controller. Good performance of the proposed control scheme on the corresponding nonlinear ASP model is illustrated through extensive simulation experiments. The contribution of the paper can be summarized to the derivation of the following two results: An accurate to a wide range of inputs and disturbances, linearized generic model of the ASP and, most important, a linear robust controller that controls accurately the effluent substrate concentration without using measurements of it.     

一类多缸液压机的分散滑模控制

通过对一类锻造液压机的分析,在考虑多缸耦合情况下为其建立了非线性系统数学模型.将该模型视为非线性关联大系统,且将其转化为可控正则型,提出采用分散滑模控制理论对其进行滑模变结构控制.针对模型转换后控制方程中状态量与控制量同时具有关联性的特点,提出通过模拟求解一个多元一次方程组的方法,得到了基于指数趋近律的分散滑模控制律,有效解决了多缸耦合情况下控制律难于求解的问题.仿真结果表明,所设计的控制器使系统实现了高精度的位置跟踪,获得了较强的抗扰性,控制效果良好,且所提方法思路清晰,为同类模型的控制方案提供了参考.     

Synthesis of nonlinear adaptive controller for a batch distillation

A nonlinear adaptive control strategy is proposed for a binary batch distillation column. The hybrid control algorithm comprises a generic model controller (GMC) and a nonlinear adaptive state estimator (ASE). The adaptive observation scheme mainly estimates the imprecisely known parameters based on the available tray temperature measurements. The sensitivity of the proposed estimator is investigated with respect to the effect of initialization error, unmeasured disturbance and uncertainty. Then, a comparative study is carried out between the derived nonlinear GMC-ASE controller and a traditional proportional integral law in terms of set point tracking and disturbance rejection performance. The study also includes the effect of measurement noise and parametric uncertainty on the closed-loop performance. The proposed adaptive control algorithm is shown to be quite promising due to the exponential error convergence capability of the ASE estimator in addition to the high-quality control action provided by the GMC controller.     

Model predictive control for systems with fast dynamics using inverse neural models

In this work, a novel model predictive control (MPC) scheme is introduced, by integrating direct and indirect neural control methodologies. The proposed approach makes use of a robust inverse radial basis function (RBF) model taking into account the applicability domain criterion, in order to provide a suitable initial starting point for the optimizer, thus helping to solve the optimization problem faster. The performance of the proposed controller is evaluated on the control of a highly nonlinear system with fast dynamics and compared with different control schemes. Results show that the proposed approach outperforms the rivaling schemes in terms of response; moreover, it solves the optimization problem in less than one sampling period, thus effectively rendering MPC-based controllers capable of handling systems with fast dynamics.     

Multivariable robust adaptive sliding mode control of an industrial boiler–turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller

To guarantee the safety and efficient performance of the power plant, a robust controller for the boiler–turbine unit is needed. In this paper, a robust adaptive sliding mode controller (RASMC) is proposed to control a nonlinear multi-input multi-output (MIMO) model of industrial boiler–turbine unit, in the presence of unknown bounded uncertainties and external disturbances. To overcome the coupled nonlinearities and investigate the zero dynamics, input–output linearization is performed, and then the new decoupled inputs are derived. To tackle the uncertainties and external disturbances, appropriate adaption laws are introduced. For constructing the RASMC, suitable sliding surface is considered. To guarantee the sliding motion occurrence, appropriate control laws are constructed. Then the robustness and stability of the proposed RASMC is proved via Lyapunov stability theory. To compare the performance of the purposed RASMC with traditional control schemes, a type-I servo controller is designed. To evaluate the performance of the proposed control schemes, simulation studies on nonlinear MIMO dynamic system in the presence of high frequency bounded uncertainties and external disturbances are conducted and compared. Comparison of the results reveals the superiority of proposed RASMC over the traditional control schemes. RAMSC acts efficiently in disturbance rejection and keeping the system behavior in desirable tracking objectives, without the existence of unstable quasi-periodic solutions.     

Grouped-neural network modeling for model predictive control

A group of feed-forward neural networks (NNs), each providing the prediction of an individual process output at a future step, is used as the dynamic prediction model for the model-based predictive control (MPC) scheme in the proposed work. These NNs are parallel (independent) rather than cascaded--they are trained and implemented in parallel. Therefore, the complexity and effort in the training stage is decreased and compounded error propagation is eliminated from the prediction. A new strategy of compensating for the process-model mismatch under this grouped-NN model structure is also developed. Effectiveness of the scheme as a general nonlinear MPC is demonstrated by simulation results.     

Sliding mode controllers for a tempered glass furnace

This paper investigates the design of two sliding mode controllers (SMCs) applied to a tempered glass furnace system. The main objective of the proposed controllers is to regulate the glass plate temperature, the upper-wall temperature and the lower-wall temperature in the furnace to a common desired temperature. The first controller is a conventional sliding mode controller. The key step in the design of this controller is the introduction of a nonlinear transformation that maps the dynamic model of the tempered glass furnace into the generalized controller canonical form; this step facilitates the design of the sliding mode controller. The second controller is based on a state-dependent coefficient (SDC) factorization of the tempered glass furnace dynamic model. Using an SDC factorization, a simplified sliding mode controller is designed. The simulation results indicate that the two proposed control schemes work very well. Moreover, the robustness of the control schemes to changes in the system׳s parameters as well as to disturbances is investigated. In addition, a comparison of the proposed control schemes with a fuzzy PID controller is performed; the results show that the proposed SDC-based sliding mode controller gave better results.     

基于遗传算法的多自由度非线性振动的模糊控制方法

由于非线性系统的复杂性,难以得到基于线性控制规律的有效控制方法。本文对于复杂的多自由度非线性振动系统提出了基于遗传传算法的主动模糊控制方法。     

Tracking control of cascade systems based on passivity: the nonadaptive and adaptive cases

A new cascade passivity-based control scheme for tracking purposes is proposed in this paper. The proposed scheme is valid for a certain class of nonlinear systems even with unstable zero dynamic, and it is also useful for regulation and stabilization purposes. The cases where all system parameters are assumed to be known (nonadaptive case) and also the case when they are unknown (adaptive case) are considered. Some simulation examples are studied to analyze the behavior of the proposed scheme.     

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies.     

Application of infinite model predictive control methodology to other advanced controllers

This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.     

四轮转向半挂汽车列车直接横摆力矩的模糊控制

引入Gim轮胎模型建立了四轮转向半挂汽车列车的非线性动力学模型.提出了四轮转向直接横摆力矩的集成控制方案,以零侧偏角为控制目标确定了半挂汽车列车牵引车后轮转角,以牵引车横摆角速度为控制变量,基于模糊控制技术设计了直接横摆力矩模糊控制器.借助Matlab/Simulink,对该控制器的有效性进行了验证.仿真结果表明,高速大转向时,四轮转向直接横摆力矩集成控制器能得到较好的输出响应,显著提高了半挂汽车列车的操纵稳定性,使驾驶员能够对半挂汽车列车进行正常操纵.     

Adaptive terminal sliding mode control for hypersonic flight vehicles with strictly lower convex function based nonlinear disturbance observer

In this paper, the altitude and velocity tracking control of a generic hypersonic flight vehicle (HFV) is considered. A novel adaptive terminal sliding mode controller (ATSMC) with strictly lower convex function based nonlinear disturbance observer (SDOB) is proposed for the longitudinal dynamics of HFV in presence of both parametric uncertainties and external disturbances. First, for the sake of enhancing the anti-interference capability, SDOB is presented to estimate and compensate the equivalent disturbances by introducing a strictly lower convex function. Next, the SDOB based ATSMC (SDOB-ATSMC) is proposed to guarantee the system outputs track the reference trajectory. Then, stability of the proposed control scheme is analyzed by the Lyapunov function method. Compared with other HFV control approaches, key novelties of SDOB-ATSMC are that a novel SDOB is proposed and drawn into the (virtual) control laws to compensate the disturbances and that several adaptive laws are used to deal with the differential explosion problem. Finally, it is illustrated by the simulation results that the new method exhibits an excellent robustness and a better disturbance rejection performance than the convention approach.     

Adaptive super-twisting fractional-order nonsingular terminal sliding mode control of cable-driven manipulators

This paper proposes a novel adaptive super-twisting fractional-order nonsingular terminal sliding mode (AST–FONTSM) control scheme using time delay estimation (TDE) for the cable-driven manipulators. The designed control scheme utilizes TDE to obtain the estimation of system dynamics, and therefore no system dynamic model information will be required. Afterwards, AST and FONTSM schemes are applied to ensure good control performance in both reaching and sliding mode phases. Due to the adoption of AST scheme, good robustness and high control precision are obtained in the reaching phase, while the boundary information of the lumped uncertainties will be no longer required. Thanks to the utilization of FONTSM error dynamics, fast convergence and accurate tracking and strong robustness can be simultaneously ensured in the sliding mode phase. Corresponding comparative simulation and experimental results demonstrate the effectiveness and superiorities of our proposed method over the existing control methods.     

Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input constraints

This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi–Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler–turbine system demonstrate the effectiveness of the proposed control scheme.     

Adaptive Control of Neutralization Process Using Recurrent Neural Networks

Abstract

This paper presents Recurrent neural Network (RNN) based adaptive control scheme for a pH neutralization process which is difficult to control due to its nonlinear dynamics with uncertainties. The proposed design comprises of both RNN estimator which adapts online and a RNN controller. Desired performance of the system is ensured by the parallel operation of both. The estimator weights are updated recursively by back propagation algorithm and controller weights are modified by steepest descent approach. Stability and convergence of proposed controller is guaranteed by Lyapunov stability analysis. Servo and regulatory performance of the system thus obtained by simulation is compared with a model based IMC controller. The RNN based controller is exhibits better performance as shown by the control simulation of a nonlinear pH neutralization process.     

Dynamic Friction Control Using Dynamic Structured RFNN and Friction Parameter Estimator

A nonlinear dynamic friction control is dealt with using dynamic friction observer and intelligent control.The adaptive dynamic friction observer based on the LuGre friction is proposed to estimate the friction parameters and a directly immeasurable friction state variable.The dynamic structured Recurrent Fuzzy Neural Network(RFNN)is designed to give additional robustness to the control system under the presence of the friction model uncertainty.A proposed composite control scheme is applied to the position tracking control of the servo system.The performances of the proposed friction observer and the friction controller are demonstrated by simulation.     

一类非线性系统的广义预测控制研究

提出一种针对双线性Hamm erste in模型的预测控制策略。该策略将双线性Hamm erste in模型中的无记忆非线性静态增益环节,改进成易于由中间变量求取控制量的环节,避免求解高阶方程根的困难,又对双线性环节采用双线性系统的广义预测控制。避免解非线性优化问题,使得到的中间变量的表达式具有解析形式。由于引入广义预测控制中多步预测的思想,抗噪声的能力显著提高。仿真结果验证了该策略的有效性。