Idle Speed Control Research Articles

Analysis of the impact of misfiring on the throttle body in ayla vehicles using the fast fourier transform method

An essential component of an automobile's air intake system, the throttle body controls airflow during combustion by acting as an idle speed control mechanism. Making ensuring the throttle body is operating properly is crucial to keeping the engine running at its best. The Fast Fourier Transform vibration analysis is one technique used for this (FFT). A an LCGC Ayla 1000 cc vehicle's throttle body and engine underwent vibration testing with rotational rates set to 750 rpm, 1000 rpm, 1500 rpm, and 2000 rpm. Vibration responses were measured using an accelerometer sensor coupled to an FFT analyzer, and Matlab was used for analysis. The throttle body had anomalous frequency readings at 1977 Hz with an amplitude of 0.03171 m/s2, according to the test results. On the other hand, the frequency value at 1410 Hz with an amplitude of 0.03435 m/s2 was observed under normal circumstances. Similar to this, abnormal frequency values with an amplitude of 0.02378 m/s2 were found on the engine at 1493 Hz, whereas normal frequency values with the same amplitude were found at 1712 Hz. These results point to incomplete combustion as the cause of the increased vibration

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.

Idle Speed Control of Gasoline Engine Based on Fuzzy Neural Network

There are non-linear and changeable elements affecting idle speed of gasoline engines, so it is difficult to set up accurate mathematic model. This paper presents the fuzzy neural method for idle speed control of engines, by which BP neural network model is set up. The experimental result shows FNNC can perform excellent control effect, good robustness and improvement of idle running quality of engines.

Idle speed control with low-complexity offset-free explicit model predictive control in presence of system delay

The requirement for continual improvement of idle speed control (ISC) performance is increasing due to the stringent regulation on emission and fuel economy these days. In this regard, a low-complexity offset-free explicit model predictive control with constraint horizon is designed to regulate the idle speed under unmeasured disturbance in presence of system delay with rigorous formulation. Particularly, we developed a high-fidelity 4-stroke gasoline-direct injected spark-ignited engine model based on first-principles and test vehicle driving data, and designed a model predictive ISC system. To handle the delay from intake to torque production, we constructed a control-oriented model with delay augmentation. To reject the influence of torque loss, we implemented the offset-free MPC scheme with disturbance model and estimator. Moreover, to deal with the limited capacity assigned for the controller in the engine control unit and the short sampling interval of the engine system, we formulated a low-complexity multiparametric quadratic program with constraint horizon in presence of system delay in state and input variables, and obtained an explicit solution map. To demonstrate the performance of the designed controller, a series of closed-loop simulations were performed. The developed explicit controller showed proper ISC performance in presence of torque loss and system delay.

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.

Research on Aging of the LNG Powered Ship Based on Computer Technology and Influence on Engine Performance

The ship’s LNG power system is a key part to ensure the normal operation of the ship. Every key part of the ship will be commanded by the LNG power system. In the part that this article focuses on, the main components of the marine engine LNG power technology are the LNG power ignition system, electronic fuel injection system and idle speed control system. The working principle of marine engine LNG power technology first controls the commands issued by the control system through internal sensors, converts the commands into electrical signals through the command input device and transmits them to various electronic control units. Then the control unit processes the signals and finally the actuator Turn each command from the LNG power unit into a specific control operation for the ship. LNG power technology has a huge impact on the entire shipbuilding industry, improving the performance and kinetic energy of ship engines, reducing ship energy consumption, reducing exhaust gas emissions and reducing pollution. This article studies them.

Engine idle speed control using nonlinear multiparametric model predictive control

SummaryIn this article, a real‐time nonlinear model predictive idle speed controller based on multiparametric programming is designed for an SI engine. Idle speed is a crucial recurring condition in urban vehicles demanding proper control to avoid stall. As will be seen, the nonlinear model predictive control (NMPC) system designed, besides complying with the predefined constraints, demonstrates a far better performance than the prevalent industrial controllers and even conventional linear MPC controllers. More importantly, a new special structure combining offline nonlinear MPC and classical controller is employed to provide both robustness and fast response. Not only is the computational burden of the controller within that of the ordinary ECU controllers, it is also able to readily damp a disturbance of 20 N·m in less than 2.5 seconds and easily deal with parameter uncertainties. The control system also regulates engine gas pedal release, and converges to the set point with a settling time of less than 3 seconds and minimum fluctuations.

A Switching System oriented Modeling and Control Strategy for Idle Speed Control of a Hybrid Powertrain

The market development of partially electrified powertrains in passenger cars motivates the re-consideration of the idle speed control problem. In this paper, a switching system model is first developed to unite the main discrete-event characteristics of the combustion engine and time-continuous characteristics of the electric motor. The presented model is classified as a discrete-time switching system model with linear subsystems.Based on this description, we further perform a model-based controller design using the lifting technique. Although the optimality property of the controller is bound to the assumption of constant turning speed, it still provides several useful properties. These are the inherent control allocation between electric and combustion engine, the consideration of the discontinuous behavior, and the discrete-time description basis which is important for implementation in a common controller architecture.

Optimal design of discrete-time fractional-order PID controller for idle speed control of an IC engine

This paper aims at proposing a discrete-time fractional-order PID (FOPID) controller, which can stabilise the variation of the idle speed of an internal combustion engine due to the occurrence of the external load disturbance. The nonlinear idle speed dynamics is linearised to be approximated by a first order plus dead time (FOPDT) model so that the FOPID controller can be initialised by a Ziegler-Nichols type tuning rule. The initialised FOPID controller can stabilise the linearised model, but it may lose its control capability in nonlinear idle speed dynamics. Therefore, an optimisation problem is solved through genetic algorithm (GA) to minimise a cost function within a small region around the FOPID's initial parameters. The optimal discrete-time FOPID controller are compared to a conventional discrete-time PID controller. The simulation study reveals that the optimal discrete-time FOPID controller secures an excellent control performance to the nonlinear idle speed model.

Optimal design of discrete-time fractional-order PID controller for idle speed control of an IC engine

This paper aims at proposing a discrete-time fractional-order PID (FOPID) controller, which can stabilise the variation of the idle speed of an internal combustion engine due to the occurrence of the external load disturbance. The nonlinear idle speed dynamics is linearised to be approximated by a first order plus dead time (FOPDT) model so that the FOPID controller can be initialised by a Ziegler-Nichols type tuning rule. The initialised FOPID controller can stabilise the linearised model, but it may lose its control capability in nonlinear idle speed dynamics. Therefore, an optimisation problem is solved through genetic algorithm (GA) to minimise a cost function within a small region around the FOPID's initial parameters. The optimal discrete-time FOPID controller are compared to a conventional discrete-time PID controller. The simulation study reveals that the optimal discrete-time FOPID controller secures an excellent control performance to the nonlinear idle speed model.

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.

Fractional Calculus with its Applications in Engineering and Technology

This book aims to provide the basic theory of fractional calculus and its applications based on practical schemes and approaches, illustrated with applicable engineering and technical examples, especially focusing on the fractional-order controller design. In the development of this book, the essential theorems and facts in the first two chapters are proven with rigorous mathematical analyses. In addition, the commonly used definitions of Grunwald-Letnikov, Riemann-Liouville, Caputo, and Miller-Ross fractional derivatives are introduced with their properties proved and linked to fractional-order controller design. The last chapter presents several enlightening scenarios of fractional-order control designs, for example, the suppression of machining chatter, the nonlinear motion control of a multilink robot, the simultaneous tracking and stabilization control of a rotary inverted pendulum, and the idle speed control of an internal combustion engine (ICE).

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.

Design of a Model-Based Fuzzy-PID Controller with Self-Tuning Scaling Factor for Idle Speed Control of Automotive Engine

Automotive engines spend a considerable portion of their life in idle state, where their characteristics are highly nonlinear and time varying. Efficient control of the engine in idle state significantly improves fuel economy, emission level and drivability. In this work, we proposed a combined fuzzy-PID idle speed controller which improves stability, fuel consumption and emission level, during normal operation as well as transient loads. In order to study the performance of control algorithm, XU7-1761cc gasoline port fuel injection engine has been modeled in idle state. This model calculates engine speed as a function of idle air valve position and ignition angle. This model demonstrated maximum of 8% error during parallel model in the loop and engine in the loop operation. After tuning the membership functions, performance of fuzzy-PID controller has been compared with baseline PID controller of engine control unit. Model in the loop simulation demonstrated that fuzzy-PID controller is more accurate than baseline PID controller, unconditionally stable and is able to reduce fuel consumption in the order of 14% compared to baseline controller. Finally, an experimental setup for real-time control of idle air valve based on proposed controller has also been developed.

Novel Automatic Idle Speed Control System with Hydraulic Accumulator and Control Strategy for Construction Machinery

To reduce the energy consumption and emissions of the hydraulic excavator, a two-level idle speed control system with a hydraulic accumulator for the construction machinery is proposed to reduce the energy consumption and improve the control performance of the actuator when the idle mode is cancelled. The structure and working principle are analyzed. The hydraulic accumulator (HA) is used to store the energy, which can provide backup pressured fluid when the idle mode is cancelled. Then, a method of how to set the pressure differential between the hydraulic accumulator and the load is proposed and the control law is discussed. The test rig is built. The experimental result shows that the idle speed can be switched among the first idle speed, the second idle speed and the normal speed automatically. Though the idle speed in the novel system can be reduced more than that in the conventional automatic idle speed control system (AISCS), the proposed system can still build the actuator pressure more quickly when the idle mode is cancelled. When compared to the system without the idle speed control, the energy saving of the proposed system is about 67%. The proposed two-level idle speed control system with a HA can achieve a high energy efficiency and a good control performance.

Performance analysis of an automatic idle speed control system with a hydraulic accumulator for pure electric construction machinery

Abstract To reduce the energy consumption and emissions of a hydraulic excavator (HE), an electric motor (EM) is employed to replace the internal combustion engine (ICE) that powers the hydraulic pumps. Owing to the excellent control characteristics and high efficiency of the EM, a two-level idle speed control system with a hydraulic accumulator (HA) for a HE is proposed to reduce energy consumption and improve the control performance of the actuator when the idle speed control (ISC) is switched off. A mathematical model is established and key parameters are analyzed and optimized. A simulation is performed using AMESim, and a control strategy for the two-level idle speed control is developed by using a co-simulation between AMESim and Simulink. A test rig is built based on the optimized parameters and simulation results. Experimental results show that the EM speed can be automatically switched between the first idle speed, second idle speed, and normal operating speed. Although the idle speed of the EM in the novel ISC system can be reduced more than that in a conventional ISC system, the proposed ISC system can still build actuator pressure more quickly in a working mode when the ISC is switched off. Compared to a system without idle speed control, the energy saving of the proposed system is approximately 36.06%. The proposed two-level idle speed control system with a HA can achieve high energy efficiency and excellent control performance, and it can be also applied to engine-driven construction machinery.

Adaptive idling control scheme and its experimental validation for gasoline engines

In this paper, the idle speed control problem is investigated for spark-ignition (SI) engines. To scope with physical model parameter-free control scheme, a nonlinear adaptive control law is proposed for the speed regulation loop. The stability analysis result shows that the idle speed converges to the set value and the estimated parameter converges to an equivalent static value of the intake manifold. Furthermore, the proposed fuel injection control law consists of a feedforward and a simple feedback loop. Finally, the proposed control scheme is validated based on a full-scaled six-cylinder gasoline engine test bench.

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.