Path-Following Based on Nonlinear Model Predictive Control with Adaptive Path Preview

This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.

糯扎渡水电站掺砾土击实特性及填筑质量检测方法研究

糯扎渡水电站防渗心墙土料采用由土料场开采的混合土料掺入人工碎石组成的掺砾土料,大规模采用人工掺砾技术在国内属首次。在对掺砾土进行一系列击实试验研究以获得掺砾土的击实特性的基础上,进一步研究了全料压实度预控线法及细料(<20 mm)压实度控制法两种质量检测方法。结果表明,现场使用细料三点击实法进行掺砾土填筑质量检测具有高效、结果可靠等优点。

糯扎渡水电站掺砾土料压实质量控制标准和控制方法研究与应用

对糯扎渡水电站心墙堆石坝掺砾土料的压实度检测方法进行对比碾压试验分析,找出快速的检测方法。

NEURAL NETWORK PREDICTIVE CONTROL WITH HIERARCHICAL GENETIC ALGORITHM

A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.

The Application and Design of the Economical Nonlinear Controller

This anticle gives a design method of the economical nonlinear controller. The controller is composed of an expert intelligent coordination controller, a fuzzy prediction controller, a fuzzy feedforward controller, a nonlinear controller and so on. The consistence of a distributed control system based on this controller is also shown briefly.

Improved Generalized Predictive Control Algorithm with Offline and Online Identification and Its Application to Fixed Bed Reactor

An improved generalized predictive control algorithm is presented in thispaper by incorporating offline identification into online identification. Unlike the existinggeneralized predictive control algorithms, the proposed approach divides parameters of a predictivemodel into the time invariant and time-varying ones, which are treated respectively by offline andonline identification algorithms. Therefore, both the reliability and accuracy of the predictivemodel are improved. Two simulation examples of control of a fixed bed reactor show that this newalgorithm is not only reliable and stable in the case of uncertainties and abnormal disturbances,but also adaptable to slow time varying processes.

Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel

Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.

Robustly stable model predictive control based on parallel support vector machines with linear kernel

Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

Synthesis of 4,4＇-MDC in the Presence of Sulfonic Acid-functionalized Ionic Liquids

The synthesis of methylene diphenyl dimethylcarbamate （4,4＇-MDC） from methyl N-phenyl carbonate （MPC） and formaldehyde （HCHO） was conducted in the presence of sulfonic acid-functionalized ionic liquids （ILs） as dual solvent-catalyst. The influences of the kind of anion in the ionic liquids, reaction conditions and the recycle of the ionic liquid on 4,4＇-MDC synthesis reaction were investigated. In addition, the acid strength of ILs was de-termined by the Hammett method with UV-visible spectroscopy, and the acid strength-catalytic activity relationship was correlated. The activity estimation results showed that [HSO3-bmim]CF3SO3 was the optimal dual solvent-catalyst. Under the suitable reaction conditions of 70℃, 40 min, molar ratio of nMPC/nHCHO= 10/1 and mass ratio of WILs/WMPC = 4.5/1, the yield of 4,4＇-MDC based on HCHO was 89.9 % and the selectivity of 4,4＇-MDC with respect to MPC was 74.9%. Besides, [HSO3-bmim]CF3SO3 was reused four times after being purified and no significant loss in the catalytic activity was observed.

Predictive current control system of PMSM based on LADRC

For a permanent magnet synchronous motor(PMSM)model predictive current control(MPCC)system,when the speed loop adopts proportional-integral(PI)control,speed regulation is easily affected by motor parameters,resulting in the inability to balance the system robustness and dynamic performance.A PMSM optimal control strategy combining linear active disturbance rejection control(LADRC)and two-vector MPCC(TV-MPCC)is proposed.Firstly,a mathematical model of a PMSM is presented,and the PMSM TV-MPCC model is developed in the synchronous rotation coordinate system.Secondly,a first-order LADRC controller composed of a linear extended state observer and linear state error feedback is designed to reduce the complexity of parameter tuning while linearly simplifying the traditional active disturbance rejection control(ADRC)structure.Finally,the conventional PI speed regulator in the motor speed control system is replaced by the designed LADRC controller.The simulation results show that the speed control system using LADRC can effectively deal with the changes in motor parameters and has better robustness and dynamic performance than PI control and similar methods.The system has a fast motor speed response,small overshoot,strong anti-interference,and no steady-state error,and the total harmonic distortion is reduced.

Predictive control of a class of bilinear systems based on global off-line models

A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.

A Composite Model Predictive Control Strategy for Furnaces

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

Neural Network Nonlinear Predictive Control Based on Tent-map Chaos Optimization

With the unique erggdicity, i rregularity, and.special ability to avoid being trapped in local optima, chaos optimization has been a novel global optimization technique and has attracted considerable attention for application in various fields, such as nonlinear programming problems. In this article, a novel neural network nonlinear predic-tive control （NNPC） strategy baseed on the new Tent-map chaos optimization algorithm （TCOA） is presented. Thefeedforward neural network＇is used as the multi-step predictive model. In addition, the TCOA is applied to perform the nonlinear rolling optimization to enhance the convergence and accuracy in the NNPC. Simulation on a labora-tory-scale liquid-level system is given to illustrate the effectiveness of the proposed method.

Neuro-fuzzy predictive control for nonlinear application

Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to reduce the model errors caused by changes of the process under control. To cope with the difficult problem of nonlinear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.

Control-oriented dynamic fuzzy model and predictive control for proton exchange membrane fuel cell stack

Proton exchange membrane fuel cell (PEMFC) stack temperature and cathode stoichiometric oxygen are very important control parameters. The performance and lifespan of PEMFC stack are greatly dependent on the parameters. So, in order to improve the performance index, tight control of two parameters within a given range and reducing their fluctuation are indispensable. However, control-oriented models and control strategies are very weak junctures in the PEMFC development. A predictive control algorithm was presented based on their model established by input-output data and operating experiences. It adjusts the operating temperature to 80 ℃. At the same time, the optimized region of stoichiometric oxygen is kept between 1.8?2.2. Furthermore, the control algorithm adjusts the variants quickly to the destination value and makes the fluctuation of the variants the least. According to the test results, compared with traditional fuzzy and PID controllers, the designed controller shows much better performance.

Nonlinear model predictive control with guaranteed stability based on pseudolinear neural networks

A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor.It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.

Robust model predictive control with randomly occurred networked packet loss in industrial cyber physical systems

For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.

Nonlinear model predictive control based on hyper chaotic diagonal recurrent neural network

Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.