A New Methodology for Tuning PID‐Type Fuzzy Logic Controllers Scaling Factors Using Genetic Algorithm of a Discrete‐Time System

In this chapter, a proportional‐integral derivative (PID)‐type fuzzy logic controller (FLC) is proposed for a discrete‐time system in order to track a desired trajectory generated using the flatness property. In order to improve the performance of the proposed controller, genetic algorithm (GA) based on minimizing the integral of the squared error (ISE) is used for tuning the input and output PID‐type FLC scaling factors online. The considered controller is applied to an electronic throttle valve (ETV). GA tuning shows a better and robust performance compared to Simulink design optimization (SDO) algorithm in terms of tracking a desired trajectory with disturbances rejection

Electronic Throttle Valve Takagi-Sugeno Fuzzy Control Based on Nonlinear Unknown Input Observers

This paper deals with the synthesis of a new fuzzy controller applied to Electronic Throttle Valve (ETV) affected by an unknown input in order to enhance the rapidity and accuracy of trajectory tracking performance. Firstly, the Takagi-Sugeno (T-S) fuzzy model is employed to approximate this nonlinear system. Secondly, a novel Nonlinear Unknown Input Observer (NUIO)-based controller is designed by the use of the concept of Parallel Distributed Compensation (PDC). Then, based on Lyapunov method, asymptotic stability conditions of the error dynamics are given by solving Linear Matrix Inequalities (LMIs). Finally, the effectiveness of the proposed control strategy in terms of tracking trajectory and in the presence of perturbations is verified in comparison with a control strategy based on Unknown Input Observers (UIO) of the ETV described by a switched system for Pulse-Width-Modulated (PWM) reference signal

Modeling of internal combustion engine control processes

The control system has been suggested to convert the nonlinear model to a linearized plant model. Therefore, we characterized the engine model based on certain aspects and analyzed it accurately to fit in the system. Simulations were performed using the PID controller to regulate and maintain the output response while rejecting any input disturbance. An engine model of a control system shows an essential role in defining the correct parameters. Hence, Idle speed control is the principal of the highest confrontations for the automotive industry and developers as they were addressing many issues concerning engine at rest position and fuel-saving economy. We keep many experiments with changing values of the PID control system. Through Simulink graphs, we compared different results and were able to find the correct value for the Idle Speed Control of the engine model. Hence, this system control predicts the control change in the system for stable equilibrium. Via manual tuning of PID control parameters, we were able to linearize the plant model and determine the correct parameters of the plant model. This study also presents an AFR control for the engine model. The main contribution is that AFR regulation is reformulated as a tracking control for the required injected fuel. To obtain a better response, the output measurement is added to a predefined AFR control. The parameter tuning is straightforward, while better AFR control response and reduction can be achieved compared to the PID control. The AFR control has been studied for internal combustion engines based on the mean value model. The control design method based on nonlinear feedback control and their control performance has been investigated for different cases. The simulation results show that the controller applying the nonlinear feedback control could give satisfactory AFR regulation performances for our engine model with specific load disturbance

Optimization and control of a dual-loop EGR system in a modern diesel engine

Focusing on the author's research aspects, the intelligent optimization algorithm and advanced control methods of the diesel engine's air path have been proposed in this work. In addition, the simulation platform and the HIL test platform are established for research activities on engine optimization and control. In this thesis, it presents an intelligent transient calibration method using the chaos-enhanced accelerated particle swarm optimization (CAPSO) algorithm. It is a model-based optimization approach. The test results show that the proposed method could locate the global optimal results of the controller parameters within good speed under various working conditions. The engine dynamic response is improved and a measurable drop of engine fuel consumption is acquired. The model predictive control (MPC) is selected for the controllers of DLEGR and VGT in the air-path of a diesel engine. Two MPC-based controllers are developed in this work, they are categorized into linear MPC and nonlinear MPC. Compared with conventional PIO controller, the MPC-based controllers show better reference trajectory tracking performance. Besides, an improvement of the engine fuel economy is obtained. The HIL test indicates the two controllers could be implemented on the real engine

Modelling and intelligent control of vehicle climatronic systems

The modelling and control method of a vehicle climatronic system, based on MATLAB/SIMULINK, is presented. In order to achieve high modelling accuracy, a developed simulation model library is introduced. The modelling approach is described and the developed models are validated with some of experimental data obtained. The models are nonlinear, independent of fluid type and based on thermo-dynamic principles. Analysis of the cooling circuit modelling and empirical real-time control models are created by using Fuzzy logic controller and Stateflow. Both of control input and output are implemented essentially at original vehicle CAN-Bus system. Feasible digital automatic control strategy basic to fuzzy theory, hardware and software solution are given. The simulation experiment is achieved with the Hardware-in-Loop technology. This control methodology is easily operated and worth applying for any further studies or methods

Design of GA-Fuzzy Controller for Buck DC-DC Converters

Fuzzy logic control (FLC) systems have been tested in many technical and industrial applications as a useful modeling tool that can handle the uncertainties and nonlinearities of modern control systems. The main drawback of the FLC methodologies in the industrial environment is challenging for selecting the number of optimum tuning parameters. In this dissertation, a method has been proposed for finding the optimum membership functions, output gain and inputs gain of a fuzzy system using genetic algorithm (GA). A synthetic algorithm combined from fuzzy logic control and GA is used to design a controller for DC-DC converter. To exhibit the effectiveness of proposed algorithm, it is used to optimize the triangle membership functions of the fuzzy model DC-DC converter system as a case study. It is clearly proved that the optimized fuzzy controllers provided better performance than a fuzzy model for the same system in terms of settling time, steady state error and ripple voltage at the output response. The evaluation of the output has been carried out by software simulation using MATLAB Simulink

Control of a hybrid electric vehicle with predictive journey estimation

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance