A recent electronic control circuit to a throttle device

The main objective of this paper is to conceive a recent electronic control circuit to the throttle device. The throttle mechanical actuator is the most important part in an automotive gasoline engine. Among the different control strategies recently reported, an easy to implement control scheme is an open research topic in the analog electronic engineering field. Hence, by using the nonlinear dwell switching control theory, an analog electronic control unit is proposed to manipulate an automotive throttle plate. Due to the switching mechanism is commuting between a stable and an unstable controllers, the resultant closed-loop system is enough robust to the control objective This fact is experimentally evidenced. The proposed electronic controller uses operational amplifiers along with an Arduino unit. This unit is just employed to generate the related switching signal that can be replaced by using, for instance, the timer IC555. Thus, this study is a contribution on design and realization of an electronic control circuit to the throttle device.Peer ReviewedPostprint (published version

Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions

Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

Transient engine model for calibration using two-stage regression approach

Engine mapping is the process of empirically modelling engine behaviour as a function of adjustable engine parameters, predicting the output of the engine. The aim is to calibrate the electronic engine controller to meet decreasing emission requirements and increasing fuel economy demands. Modern engines have an increasing number of control parameters that are having a dramatic impact on time and e ort required to obtain optimal engine calibrations. These are further complicated due to transient engine operating mode. A new model-based transient calibration method has been built on the application of hierarchical statistical modelling methods, and analysis of repeated experiments for the application of engine mapping. The methodology is based on two-stage regression approach, which organise the engine data for the mapping process in sweeps. The introduction of time-dependent covariates in the hierarchy of the modelling led to the development of a new approach for the problem of transient engine calibration. This new approach for transient engine modelling is analysed using a small designed data set for a throttle body inferred air ow phenomenon. The data collection for the model was performed on a transient engine test bed as a part of this work, with sophisticated software and hardware installed on it. Models and their associated experimental design protocols have been identi ed that permits the models capable of accurately predicting the desired response features over the whole region of operability. Further, during the course of the work, the utility of multi-layer perceptron (MLP) neural network based model for the multi-covariate case has been demonstrated. The MLP neural network performs slightly better than the radial basis function (RBF) model. The basis of this comparison is made on assessing relevant model selection criteria, as well as internal and external validation ts. Finally, the general ability of the model was demonstrated through the implementation of this methodology for use in the calibration process, for populating the electronic engine control module lookup tables

Fault Tolerant Control of Electronic Throttles with Friction Changes

To enhance the reliability of the electronic throttle and consequently the vehicles driven by the internal combustion engines, a fault tolerant control strategy is developed in this paper. The proposed method employs a full-order terminal sliding mode control in conjunction with an adaptive radial basis function network to estimate change rate of the fault. Fault tolerant control to abrupt and incipient changes in the throttle viscous friction torque coefficient and the throttle coulomb friction torque coefficient is achieved. Whilst the throttle position is driven to track the reference signal, the post-fault dynamics are guaranteed to converge to the equilibrium point in finite time, and the control is smooth without chattering. A nonlinear Simulink model of an electronic throttle is developed with real physical parameters and is used for evaluation of the developed method. A significant change of the throttle friction torque is simulated, and the fault tolerant control system keeps system stability and tracking the reference signal in the presence of the fault

Advanced Neural Network Based Control for Automotive Engines

This thesis investigates the application of artificial neural networks (NN) in air/fuel ratio (AFR) control of spark ignition(SI) engines. Three advanced neural network based control schemes are proposed: radial basis function(RBF) neural network based feedforward-feedback control scheme, RBF based model predictive control scheme, and diagonal recurrent neural network (DRNN) - based model predictive control scheme. The major objective of these control schemes is to maintain the air/fuel ratio at the stoichiometric value of 14.7 , under varying disturbance and system uncertainty. All the developed methods have been assessed using an engine simulation model built based on a widely used engine model benchmark, mean value engine model (MVEM). Satisfactory control performance in terms of effective regulation and robustness to disturbance and system component change have been achieved. In the feedforward-feedback control scheme, a neural network model is used to predict air mass flow from system measurements. Then, the injected fuel is estimated by an inverse NN controller. The simulation results have shown that much improved control performance has been achieved compared with conventional PID control in both transient and steady-state response. A nonlinear model predictive control is developed for AFR control in this re- . search using RBF model. A one-dimensional optimization method, the secant method is employed to obtain optimal control variable in the MPC scheme, so that the computation load and consequently the computation time is greatly reduced. This feature significantly enhances the applicability of the MPC to industrial systems with fast dynamics. Moreover, the RBF model is on-line adapted to model engine time-varying dynamics and parameter uncertainty. As such, the developed control scheme is more robust and this is approved in the evaluation. The MPC strategy is further developed with the RBF model replaced by a DRNN model. The DRNN has structure including a information-storing neurons and is therefore more appropriate for dynamics system modelling than the RBF, a static network. In this research, the dynamic back-propagation algorithm (DBP) is adopted to train the DRNN and is realized by automatic differentiation (AD) technique. This greatly reduces the computation load and time in the model training. The MPC using the DRNN model is found in the simulation evaluation having better control performance than the RBF -based model predictive control. The main contribution of this research lies in the following aspects. A neural network based feedforward-feedback control scheme is developed for AFR of SI engines, which is performed better than traditional look-up table with PI control method. This new method needs moderate computation and therefore has strong potential to be applied in production engines in automotive industry. Furthermore, two adaptive neural network models, a RBF model and a DRNN model, are developed for engine and incorporated into the MPC scheme. Such developed two MPC schemes are proved by simulations having advanced features of low computation load, better regulation performance in both transient and steady state, and stronger robustness to engine time-varying dynamics and parameter uncertainty. Finally, the developed schemes are considered to suit the limited hardware capacity of engine control and have feasibility and strong potential to be practically implemented in the production engines

Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.EThOS - Electronic Theses Online ServiceBiruni Remote Sensing Centre, LibyaGBUnited Kingdo

Modeling and Inverse Controller Design for an Unmanned Aerial Vehicle Based on the Self-Organizing Map

The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a SOM-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV

NDI-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling.

The success of Unmanned Combat Aerial Vehicles (UCAVs) requires further developments in the field of automated aerial refuelling (AAR) and control systems. AAR aircraft models identified thus far do not take the centre of gravity (cg) position movement into account during refuelling. A six-degree-of-freedom aircraft model was combined with a moving cg model for refuelling. The equations of motion for the aircraft in flight refuelling showed the aircraft dynamics to be coupled in the longitudinal and lateral-directional planes when the cg had moved away from the reference point. Applying assumptions specific to the flight conditions, simplified equations of motion were derived. Modal analysis of four cases for the linearised aircraft model during aerial refuelling was conducted. This revealed that the increase in mass was favourable to the stability of the Dutch Roll mode, but the mode did become more oscillatory initially as mass was increased, but as the cg moved forward, the mode became less oscillatory. The opposite was observed with the Phugoid mode. The Short Period Oscillation (SPO) decomposed into two first order modes during refuelling and these remained unchanged during the refuelling process. Three radial basis function (RBF) neural networks (RBFNN) were developed and trained to approximate the inverse plant dynamics and predicted commanded deflections of the elevator, aileron and rudder. Training data required for the network was randomly generated and the desired rates and commanded control surface deflections were computed. The training error was the smallest in the elevator deflection required during refuelling. A basic nonlinear dynamic inversion (NDI) controller without a neural network (NN) was designed for the aircraft. The performance of this controller was not satisfactory. The RBF was combined with the NDI to form a RBFNN-based controller. The longitudinal NDI RBFNN-based controller was less sensitive to modelling errors than the base NDI controller. The lateral NDI RBFNN-based controller’s performance was worse than the longitudinal controller, but showed potential as a technique for future consideration. Including the variation of aircraft inertia in the model has been recommended as further work, as well as exploring other neural network topologies in the NDI NN controller