Feed-forward multilayer neural network model for vehicle lateral guidance control

An advanced vehicle lateral guidance control technology is necessary in order to develop intelligent transportation and manufacturing systems with flexibility and immediate adaptability. PID control, optimal control, and fuzzy control have often been used for designing a vehicle lateral guidance controller; in addition automatic guidance methods by spline curve and inverse dynamics are also used in mobile robots (e.g. differential drive), but they are not sufficient to develop a highly intelligent vehicle lateral guidance controller which can adapt to varying environments, because they lack some behavior like learning ability and adaptability. In this paper, the possibility to apply neural networks for developing a vehicle lateral guidance controller is exposed. A new neuron activation function suitable for vehicle lateral guidance control is suggested, a feed-forward multilayer neural network (FMNN) with the suggested neuron activation function is proposed and a vehicle lateral guidance controller (VLGC) is developed by use of the FMNN. The VLGC can be applied to automobiles of different parameters and roads of various widths. It can be also applied to mobile robots. Its input variables are proposed to be defined as kind of relative quantities by using the road width, automotive parameter, automotive position, and orientation on the corner course as 90°. Its output variable is the automotive steering angle. Its teaching data are collected by automobile driving simulation, and its connection weights and threshold values are tuned through the error back-propagation algorithm. The training process and the result of neural network by different learning rate coefficients and momentum parameters are compared. Four VLGCs are generated through training by using different learning rate coefficients, momentum parameters, and repeat training times. Automated guided automobile simulations and mobile robot experiments for each VLGC are carried out. Good training result as well as automated guided simulation and experimental results are obtained.     

Adaptive neural network tracking control for a class of non-linear systems

This article extends the application of the adaptive neural network control to a new class of uncertain MIMO non-linear systems, which are composed of interconnected subsystems where each interconnected subsystem is in the non-affine pure-feedback form. Because both the variables which are used as virtual controllers and the actual controllers appear non-linearly in unknown functions of the MIMO systems, thus, this class of systems is difficult to control. The radial basis function neural networks are utilised to approximate the desired virtual controllers and the desired actual controllers which are obtained by using implicit function theorem. The salient property of the proposed approach is that the number of the adjustable parameters is less than the numerous alternative approaches existing in the literature. It is proven that, under appropriate assumptions, all the signals in the closed-loop system are uniformly bounded and the tracking errors converge to a small neighbourhood of the origin by appropriately choosing design parameters. The feasibility of the developed approach is verified by two simulation examples.     

Efficient non-linear model based predictive control

For a large number of degrees of freedom and/or large dimension systems, non-linear model based predictive control algorithms based on dual mode control can become intractable. This paper proposes an alternative which deploys the closed-loop paradigm that has proved to be very effective for the case of linear time-varying or uncertain systems. The various attributes and computational advantages of the approach are shown to carry over to the non-linear case.     

A non-linear optimal control approach to central sewer network flow control

The problem of optimal water distribution to several retention reservoirs in an urban sewer network during rainfall is considered. The goal of the control actions is the minimization of overflows and eventually the reduction of their polluting impact on receiving waters. To this end, a non-linear optimal control approach is used and the numerical solution of the control problem is effectuated by use of a feasible direction algorithm. A detailed study of the central control problem for a particular large sewer network using this method is presented. Results demonstrate the efficiency and the real-time feasibility of the developed methodology.     

Approximate model matching for non-linear control systems

Approximate model matching refers to the problem of controlling a non-linear system so as to achieve a response resembling that of a desirable model. The paper presents a family of recursive output feedback controllers that can achieve approximate model matching in all cases where it is possible. The design of these controllers depends on the solution of a set of algebraic inequalities.     

On robust tracking with non-linear model predictive control

In this paper a class of stabilizing model predictive control algorithms based on an optimization problem with two different horizons (control and prediction) is considered. Particular attention is devoted to the tracking problem for reference signals constant beyond a prescribed future horizon. For such a problem, it is pointed out the necessity of an output-feedback scheme to guarantee robust steady-state zero-error regulation. Moreover, an additional constraint is introduced in the optimization problem for the robust fulfilment of the constraints on the control variable. The potentiality of this approach is shown through some simulation experiments on a highly non-linear chemical reactor.     

State-space approach to non-linear model reference adaptive control

A new method is developed for tracking in non-linear delay-differential systems when the exact knowledge of the system parameters are not known. The method is based on securing the stability in the state spaces of plant and reference models. The results obtained are quite general in nature and can be applied to many physical systems.     

Vibration control of vehicle active suspension system using a new robust neural network control system

The main problem of vehicle vibration comes from road roughness. For that reason, it is necessary to control vibration of vehicle’s suspension by using a robust artificial neural network control system scheme. Neural network based robust control system is designed to control vibration of vehicle’s suspensions for full suspension system. Moreover, the full vehicle system has seven degrees of freedom on the vertical direction of vehicle’s chassis, on the angular variation around X-axis and on the angular variation around Y-axis. The proposed control system is consisted of a robust controller, a neural controller, a model neural network of vehicle’s suspension system. On the other hand, standard PID controller is also used to control whole vehicle’s suspension system for comparison.Consequently, random road roughnesses are used as disturbance of control system. The simulation results are indicated that the proposed control system has superior performance at adapting random road disturbance for vehicle’s suspension.     

Enhancements to non-linear multiple model adaptive control schemes

There has been much interest in recent years on neural network based control of non-linear dynamic processes and also on their use in multiple model adaptive control schemes. This paper reports on performance enhancements to one class of such control laws, including their extension to multiple model adaptive control.     

高超声速飞行器的调度离线预测控制

本文将调度预测控制的思想应用于离线鲁棒预测控制,设计了高超声速飞行器计算有效的调度离线预测控制器.首先在不同的平衡点离线设计一系列控制规则,实际实施时只需要在不同的控制器之间进行切换,避免进行在线优化,大幅度减少了在线计算时间.通过估计局部控制器的稳定域,保证了切换控制器的稳定性.另外在确保良好控制品质的同时,还能够保证所有输入和状态均在给定约束范围.仿真试验表明,提出的方法能实现速度和高度较大范围的指令跟踪,所有输入和状态均在给定约束范围内;相比于在线鲁棒预测控制方法,仿真运行时间减少,可以实现高超声速飞行器的实时控制.     

基于非线性奇异子系统模型的同步发电机分散非线性控制

针对基于非线性奇异子系统模型的同步发电机,本文利用反推控制方法,研究了其非线性分散控制问题.同步发电机本质上是电力大系统中的一个非线性奇异子系统,与系统其余部分存存相互约束和影响.首先利用微分同胚变换和状态反馈,实现了同步发电机模型的等价转化.然后在等价系统的楚础上,在充分考虑系统其余部分对于同步发电机影响的前提下,利用反推控制方法设计了同步发电机组的非线性镇定控制器,使得闭环系统是渐近稳定的.最后的仿真结果农明了本文所提控制方法的有效性.     

Adaptive control of a CSTR with a neural network model

An adaptive control algorithm with a neural network model, previously proposed in the literature for the control of mechanical manipulators, is applied to a CSTR (Continuous Stirred Tank Reactor). The neural network model uses either radial Gaussian or “Mexican hat” wavelets as basis functions. This work shows that the addition of linear functions to the networks significantly improves the error convergence when the CSTR is operated for long periods of time in a neighborhood of one operating point, a common scenario in chemical process control. Then, a quantitative comparative study based on output errors and control efforts is conducted where adaptive controllers using wavelets or Gaussian basis functions and PID controllers (IMC tuning with fixed parameters and self tuning PID) are compared. From this comparative study, the practicality and advantages of the adaptive controllers over fixed or adaptive PID control is assessed.     

Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator

This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.     

GARCH非线性时间序列模型的网络流量预测

网络流量预测在拥塞控制、网络管理与诊断、路由器设计等领域都具有重要意义。根据当今网络流量的特点,传统的ARMA模型在描述网络流量数据特性时有一定的局限性,从而影响网络流量预测的精度。针对这个问题,研究了使用广义自回归条件异方差模型(GARCH)对网络流量数据进行建模的方法,通过仿真实验表明,该模型可以较好地描述网络流量数据的异方差性,同时其预测精度较之传统的ARMA模型的预测精度也得到了大幅提升。     

Pinning control of a generalized complex dynamical network model

This paper investigates the local and global synchronization of a generalized complex dynamical network model with constant and delayed coupling. Without assuming symmetry of the couplings, we proved that a single controller can pin the generalized complex network to a homogenous solution. Some previous synchronization results are generalized. In this paper, we first discuss how to pin an array of delayed neural networks to the synchronous solution by adding only one controller. Next, by using the Lyapunov functional method, some sufficient conditions are derived for the local and global synchronization of the coupled systems. The obtained results are expressed in terms of LMIs, which can be efficiently checked by the Matlab LMI toolbox. Finally, an example is given to illustrate the theoretical results.     

A new real-time perspective on non-linear model predictive control

This work presents a new formulation of continuous-time non-linear model predictive control (NMPC) in which the parameters defining the input trajectory are adapted continuously in real time. Continuous implementation of the control as the input parameterization is being optimized reduces the impact of computational delay, in particular in response to process disturbances. By eliminating the typical correspondence between the time partitions used for input parameterization and implementation, and instead parameterizing the input over arbitrary intervals of variable length, a means is provided to reduce the overall number of optimization parameters (and hence the dimension of the required gradient and Hessian calculations) without adversely affecting stability or optimality.     

Iterative learning control for non-linear systems described by a blended multiple model representation

This paper deals with the design of gain-scheduling-based iterative learning controllers for continuous-time non-linear systems described by a blended multiple model representation. Sufficient conditions guaranteeing the convergence of the infinity norm as well as the u -norm of the tracking error are derived. The effectiveness of the proposed control scheme is illustrated on an example of a non-affine-in-input system.     

Dissolved oxygen control through an adaptive non-linear model approach: a simulation study

Several exact linearization methods were applied to a simplified non-linear model for the concentration of dissolved oxygen in a waste water treatment plant, and digital control algorithms were derived based on these linearizations. A non-linear adaptive control algorithm is proposed and compared with a well tuned PID, a linear adaptive controller, and a non-adaptive non-linear controller. The proposed algorithm shows a better performance under a variety of perturbations. However, users must be careful in the choice of the appropriate model parameters to be estimated.     

基于分层模糊控制的智能车辆的横向控制模型

车辆的横向控制是智能交通中智能车辆自主导航技术的关键技术。在总结以往研究成果的基础上,提出了动态目标位置概念,以便更加真实地描述车道变换的特性;为了减少模糊控制器的规则数量,实现实时控制的目的,采用了分层模糊控制的控制器设计方案;在车辆的横向控制中,以三次样条曲线作为车道变换的路径拟合曲线,较为灵活地表现了车道变换的特性。仿真结果表明,被控车辆能够沿着虚拟的路线平滑地变换车道,较为理想地模拟实际交通环境中车辆横向运动的特性。