Engine Idle Speed Control Research Articles

Deep reinforcement learning implementation on IC engine idle speed control

Efficient control of automotive engine idle speed is crucial for achieving better fuel economy and smoother engine running. This paper presents a comparison between proportional-integral-derivative (PID) control and Reinforcement Learning (RL) using the Deep Q-Network (DQN) algorithm as a high-level control method for minimizing idle speed fluctuations caused by changes in engine irregularities, and the response time and accuracy of the throttle control mechanism. In addition to low-level PID control for the throttle valve position, MATLAB/Simulink was employed to build the simulation environment, incorporating an engine model and an electronic throttle body model, and observing the engine's current speed. The results demonstrated the superiority of RL-based control over PID in reducing idle speed fluctuations and enhancing engine performance in simulations and real-world experiments. This study advances automotive engine control strategies.

Automotive engine idle speed controller: Nonlinear model predictive control utilizing the firefly algorithm

This paper developed an automotive engine idle speed controller using nonlinear model predictive control and the Firefly Algorithm for Idle Speed Engine (NMPC-FA-ISE). The designed NMPC-FA is implemented on a field-programmable gate array (FPGA). The Vivado HLS tool performs the complete design flow for FPGA platforms. It was adopted and used where different optimization techniques are applied to achieve the best performance. Moreover, the Firefly swarm optimization algorithm (FA) was employed to handle the nonlinearity of NMPC instead of traditional techniques. An Engine Idle Speed Controller (ISC) is used to demonstrate the suggested NMPC and FA implementation solution on Python Productivity for the ZYNQ platform (PYNQ). The experimental results of the proposed approach proved that the FPGA implementation of the proposed NMPC-FA achieved satisfactory control performance for the engine idle speed control with a fast response time and acceptable power consumption according to the area occupied on the board.

Active disturbance rejection for time-varying state-delay systems based on equivalent-input-disturbance approach

Exogenous disturbances largely affect the control performance of systems with time delays. This study considers a control problem of rejecting a disturbance in a PI control system for a time-varying state-delay plant. The equivalent-input-disturbance (EID) approach is integrated in a PI control system. An EID estimator estimates the overall effects of a time-varying delay and a disturbance. An EID estimate is combined into a PI control law to improve control performance. A less-conservative stability condition of the control system is derived using a Lyapunov–Krasovskii functional together with the Jensen’s integral inequality and the reciprocally convex combination lemma. Parameters of the controllers in the system are calculated using the condition. Engine idle speed control is used to verify the effectiveness of this approach. Compared with the generalized extended-state observer and the sliding-mode control methods, our method reduced the tracking error to about one third and one sixth, respectively. This demonstrates the validity and superiority of our method.

Design of a PI-Controller Based on Time-Domain Specification Utilizing the Parameter Space Approach

In automotive application PI and PID controllers are widely used. Commonly the controller parametrization is performed in a heuristic manner in the vehicle at different operating points. Model-based approaches offer many advantages like a reduced effort of the design process and a more systematically investigation of the parameter set. Circumventing experiments at the vehicle is not feasible, however the goal is to achieve a significant reduction of this part of work. The aim of this paper is to find the controller parameter region, that ensures compliance with defined measures of the controller performance in time domain. On the basis of the parameter space approach these measures need to be transferred into the s-domain, which is shown exemplary for a second order system and a PI-controlled integrator system. The latter serves as a simple vehicle drive train model for the design of the engine idle speed controller.

Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine

The automotive engine idle speed control problem is a compromise among low engine speed for fuel saving, minimum emissions and disturbance rejection ability to prevent engine stall. However, idle speed regulation is very challenging due to the presence of high nonlinearity and aging-caused uncertainties in the engine dynamics. Therefore, the engine idle speed system is a typical uncertain nonlinear system. To address the problems of inherent nonlinearity and uncertainties in idle speed regulation, an extreme learning machine (ELM)-based adaptive neural control algorithm is proposed for tracking the target idle speed adaptively. The purpose of ELM is to rapidly deal with the uncertain nonlinear engine system. Since the original ELM is not designed for adaptive control, a new adaptation law is designed to update the weights of ELM in the sense of Lyapunov stability. Experiment is conducted to validate the performance of the proposed control method. Experimental result indicates that the ELM-based adaptive neural control outperforms the classical proportional–integral–derivative (PID), fuzzy-PID and back-propagation-neural-network-based controllers in terms of tracking performance under the variation of engine load.

LMI-Based Design of Distributed Controllers to Achieve Component Swapping Modularity

Component swapping modularity (CSM) refers to distributed modular control design in networks of swappable smart components. CSM reduces control design effort and complexity in platform-based systems. Existing CSM methods achieve promising results for low order multi-input-multi-output systems. However, the lack of generalization, heavy computational burden, and to a lower extent, designer’s involvement limit the applications of the existing CSM methods. Thus, this brief utilizes linear matrix inequalities (LMIs) to present an almost automatic CSM algorithm which converges rapidly and is easy to generalize to an arbitrary linear system. The LMI-based CSM is designed to maintain both disturbance attenuation and quadratic stability. Also, it is desired to satisfy specific time response criteria. Thus, the proposed algorithm combines $\mathcal {H}_{2}$ optimization and robust $\mathcal {H}_\infty $ optimization to obtain a distributed control composed of parts with prescribed orders and partially tunable parameters. The designer’s involvement is dramatically reduced to the iterative tuning of two scalar parameters to optimize the fixed part of the controller. The proposed algorithm incorporates reference tracking. Also, stability measures and design criteria are checked numerically at each step. The LMI-based CSM algorithm has been numerically verified using an engine idle speed control example.

Comparison of Model-based Vs. Data-driven Methods for Fault Detection and Isolation in Engine Idle Speed Control System

An internal combustion engine operating at idle is regulated by a feedback controller so that it runs at a preset idle speed without stalling when no acceleration is requested from the driver. Idle speed control is affected by numerous disturbances, ranging from accessory loads to environmental conditions. Because of the regulating behavior of the controller, faults, especially actuator faults, may affect sensor measurements in a way very similar to disturbances, system uncertainty or noise. This poses a challenge to the fault detection and isolation (FDI) problem for this system. In this paper, two fundamentally different fault diagnoses approaches are used to detect and isolate faults. A model-based residual generation scheme as well as a data-driven linear discriminant analysis scheme is developed to solve the FDI problem even when faults are concurring in addition to system uncertainty, disturbance and noise. Their performances are compared side by side using data gathered from an experimentally validated simulator for an engine idle system that considers an actuator fault, a sensor fault, several system uncertainties and disturbance (operating conditions), and sensor noise. The results show that comparable performance can be achieved with both schemes and some comments are made about each approach.

Fast Nonlinear Model Predictive Control on FPGA Using Particle Swarm Optimization

Nonlinear model predictive control (NMPC) requires a repeated online solution of a nonlinear optimal control problem. The computation load remains the main challenge for the real-time practical application of the NMPC technique, particularly for fast systems. This paper presents a fast NMPC algorithm implemented on a field-programmable gate array (FPGA) that employs a particle swarm optimization (PSO) algorithm to handle nonlinear optimization. The FPGA is used to explore the possibilities of parallel architecture for the substantial acceleration of NMPC. PSO is employed to achieve real-time operation due to its naturally parallel capabilities. The proposed FPGA-based NMPC-PSO controller consists of a random-number generator, a fixed-point arithmetic, a PSO solver, and a universal asynchronous receiver/transmitter communication interface. Then, this controller is applied to an engine idle speed control problem and demonstrated with an FPGA-in-the-loop testbench. The experimental results indicate that the NMPC-on-FPGA-chip strategy has good computational performance and achieves satisfactory control performance.

Design of Engine Idle Speed Control System Based on CAN Bus

In order to reduce the fuel consumption and emissions of automobile, the engine idle speed control method was studied. The basic concept of engine idle speed and the requirements and strategies of idle speed control were introduced, and the features and structures of CAN bus technology were analyzed. An idle speed control system of automobile engine based on CAN bus was designed which included main control module, front control module and rear control module. The overall structure, hardware design and software implementation of the system were given. The system can realize the desired functions, and it has certain application value.

Direct Optimal Design for Component Swapping Modularity in Control Systems

This paper presents a new method for the design of distributed controllers that achieves component swapping modularity (CSM) in control systems. Our approach, referred to as the direct method, is based on solving a bilevel optimization problem to obtain the distributed controller gains directly. In the previously proposed three-step method, the distributed controllers were obtained by matching with a precomputed centralized controller. To illustrate our approach, we consider an engine idle speed control problem, where CSM is achieved with respect to the throttle actuator. The results indicate that the direct method can significantly improve the CSM metric compared to the three-step method. In addition, for a certain distributed control structure, the direct method enables the designer to tradeoff between desired system performance and CSM.

Design of EFL Control System for 950 Motorcycle Based on MCU

With requirements to air quality for human’s living, energy conservation and emission reduction, motorcycle emission in our country is in strict accordance with Euro III standard, while use of EFI (Electronic Fuel Injection) control system is one of the main measures to achieve the emission requirements. In this paper, 51-series MCU is applied as the core control unit for EFI control system of 950 (ml) large displacement motorcycles, which improves the dynamics and fuel economy of 950 (ml) gasoline engines. Hardware and software designs are proposed. Hardware design covers input signal processing circuit and driver circuit for EFL and software design analyzes the software control strategy in four aspects: engine work modes, fuel injection, ignition and idle speed control. The design in this paper has advantages of low cost and optimal control system performance. It not only fulfills the requirement of energy conservation and emission reduction, but also improves the performance of control system. Simulations carried out are presented to demonstrate that air-fuel ratio is controlled around ideal value, which is of great significance to large displacement motorcycle industry.

Observer-based robust control of time-delay uncertain systems with application to engine idle speed control

A combined controller-observer design procedure for a class of timedelay uncertain systems is proposed. First, a sufficient condition for robust asymptotical stability of the closed-loop systems is presented based on Lyapunov function theory. Next, by using matrix’s Singular Value Decomposition (SVD) and Linear Matrix Inequality (LMI) techniques, the existence condition and method of designing an observer-based feedback controller is derived. The result is obtained in terms of LMI, which make all the gain matrices can be easily obtained by Matlab’s LMI toolbox. Finally, the developed design techniques are applied to an engine idle speed control problem. The simulation results demonstrate the validity of this approach.

Gasoline Engine Idle Speed Control Based on PID Fuzzy Algorithm

According to the characteristics of operating procedures of gasoline engine idle speed; a fuzzy control method is developed to control idle speed of gasoline engine. A novel controller is designed. The controller, which combines fuzzy logic algorithm with traditional PID algorithm, improves steady and dynamic performances of idle speed control. The method has the advantage of not requiring a precise mathematical model of the controlled object. By using SIMULINK simulation software of MATLAB, the simulation results obtained with the PID fuzzy controller show that the PID fuzzy controller has better controlled performances and robustness. It provides some reference values for further practical application.

Delay-dependent guaranteed cost control for state-delayed system with disturbance and its application to engine idle speed control

This paper proposes a delay-dependent guaranteed cost control scheme for engine idle speed control (ISC) with induction-to-torque delay and external load disturbance. An augmented linearization model of engine at idle speed operating mode was developed based on physical principle and experiment data. To provide a compromise between disturbance rejection and other performance requirements of ISC, a multi-objective cost function upper bound was given, which can help us to take into account the fuel economy and disturbance rejection performance together in ISC. Poles constraint was added to the closed-loop system to guarantee convergence rates of state. The whole optimization solution to ISC can be solved under the framework of LMI. A commercial engine model was utilized to assess the performance of the controller. Simulation results on this model show us that designed controller can achieve desired performance.

Adaptive Kalman Filter-Based Load Torque Compensator for Improved SI Engine Idle Speed Control

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents design of an SI engine load torque estimator based on an adaptive Kalman filter. The adaptive estimator is characterized by a fast response and good noise suppression ability. The estimator is used to establish a fast load torque compensation path within a proportiona-plus-integral (PI) controller-based idle speed control system. The proposed adaptive controller has been verified by means of experiments, and compared with PI, PID and polynomial controllers. The experimental results point out to favorable performance of the adaptive controller for a wide engine operating range. </para>

Automotive engine idle speed control optimization using least squares support vector machine and genetic algorithm

PurposeNowadays, automotive engines are controlled by electronic control units (ECUs), and the engine idle speed performance is significantly affected by the setup of control parameters in the ECU. The engine ECU tune‐up is done empirically through tests on a dynamometer (dyno). In this way, a lot of time, fuel and human resources are consumed, while the optimal control parameters may not be obtained. The purpose of this paper is to propose a novel ECU setup optimization approach for engine idle speed control.Design/methodology/approachIn the first phase of the approach, Latin hypercube sampling (LHS) and a multi‐input/output least squares support vector machine (LS‐SVM) is proposed to build up an engine idle speed model based on dyno test data, and then a genetic algorithm (GA) is applied to obtain optimal ECU setting automatically subject to various user‐defined constraints.FindingsThe study shows that the predicted results using the estimated model from LS‐SVM are in good agreement with the actual test results. Moreover, the optimization results show a significant improvement on idle speed performance in a test engine.Practical implicationsAs the methodology is generic it can be applied to different vehicle control optimization problems.Originality/valueThe research is the first attempt to integrate a couple of paradigms (LHS, multi‐input/output LS‐SVM and GA) into a general framework for constrained multivariable optimization problems under insufficient system information. The proposed multi‐input/output LS‐SVM for modelling of multi‐input/output systems is original, because the traditional LS‐SVM modelling approach is suitable for multi‐input, but single output systems. Finally, this is the first use of the novel integrated framework for automotive engine idle‐speed control optimization.

Combined Controller-Observer Design for Uncertain Time Delay Systems With Application to Engine Idle Speed Control

This paper sets forth two approaches for a combined controller-observer design procedure for a class of uncertain time delay systems. The first procedure uses a delay-independent LMI (linear matrix inequality) formulation that sets forth sufficient conditions for the existence of appropriate feedback and observer matrices that guarantee asymptotic stability of the system-error dynamic combination. The second procedure generates a similar delay-dependent LMI. Standard toolboxes can be used to generate solutions to the LMIs when solutions exist. The observer-control design techniques are applied to an internal combustion (IC) engine idle speed control problem; simulation results demonstrate the effectiveness of the proposed techniques.

Linear Robust Control of Identified Nonlinear Inverse Compensated SI Engine

A technique based on Horowitz’s nonlinear quantitative feedback theory is presented, in which the size of the uncertainty is reduced by cancellation of the plant nonlinearity through nonlinear inverse compensation. Linear identification of the open loop nonlinear inverse compensated plant is used to obtain an unstructured uncertainty representation, however, rather than computing QFT templates based on parametric uncertainty. An increase in achievable robust performance with any linear robust performance control design technique may then be obtained. The method is experimentally validated by application to a SISO automotive SI engine idle speed control problem using an electric dynamometer.

Inverse-NARMA: a robust control method applied to SI engine idle-speed regulation

This paper presents a control technique based on a directly identified non-linear inverse of a stable system. A NARMA model of the inverse of the uncontrolled system is identified using reversed input–output data collected experimentally from the plant. Linear identification is carried out on the plant which is pre-compensated by the identified inverse, sufficiently time delayed to be causal. This achieves a reduction in the uncertainty representation necessary to model the non-linearity. Subsequent robust linear controller design results in an improvement in robust performance when using the inverse pre-compensator. The method is applied to the automotive SI engine idle-speed control problem using a parameter space linear robust control design technique. The result is experimentally validated using an electric dynamometer.

IDLE SPEED CONTROL FOR AUTOMOTIVE ENGINES WITH AN INTEGRATED STARTER ALTERNATOR

This paper describes a model-based control design methodology for engine idle speed control in a mild hybrid-electric vehicle based on an integrated starter-alternator architecture. The paper describes physically based models of the engine and integrated starter-alternator, describes the simulation environment, outlines the control design and presents simulations results. The results presented in the paper are in a form suitable for rapid control prototyping and vehicle implementation.