Electronic Throttle Research Articles

Reduced ultimate-bound of tracking error convergence for ETV system with unknown upper-bound mismatched perturbation

The electronic throttle valve (ETV) is widely used in automobile engines to obtain efficient fuel combustion, improved fuel economy, and reduced emissions. The control of the ETV system is a challenging problem due to the unmatched structure of perturbation and its unknown upper bound. As such, deep control concerns are required. For such a system, recent control approaches could partially solve the ultimate boundedness of convergence error, but they have failed to find a solution in the presence of unknown upper-bound perturbation. The contribution of this study is to develop a novel backstepping-based barrier function integral sliding mode controller (BS-BFISMC) to find a solution for the ETV system without the pre-knowledge of the upper bound of perturbation. The proposed control scheme gets the benefits of the backstepping algorithm (BS) and barrier function integral sliding mode control (BFISMC), and the combined scheme could lead to reduced ultimate-bound convergence of tracking error and result in chattering-free control action. A rigorous stability analysis has been conducted to prove the finding reached by the proposed control approach. The efficacy and effectiveness of the proposed controller have been shown by conducting a comparison study with other existing control methods; namely, conventional backstepping controller, robust continuous backstepping (CRBS), and backstepping-based quasi-integral sliding mode controller (BS-QISMC). The numerical results showed that the proposed BS-BFISMC yields the lowest level of ultimate bound of tracking errors and the least control efforts as compared to other controllers. Also, it has been shown that the proposed controller does not require the upper bound of perturbation, which is a pre-requisition of all compared controllers.

Analyzing psychological information using lattice hydrodynamic model coupled with electronic throttle control

Analyzing psychological information using lattice hydrodynamic model coupled with electronic throttle control

Design of an Adaptive Integral Sliding Mode Controller for Position Control of Electronic Throttle Valve

Design of an Adaptive Integral Sliding Mode Controller for Position Control of Electronic Throttle Valve

Adaptive synergetic control for electronic throttle valve system

AbstractThis study has developed adaptive synergetic control (ASC) algorithm to control the angular position of moving plate in the electronic throttle valve (ETV) system. This control approach is inspired by synergetic control theory. The adaptive controller has addressed the problem of variation in systems parameters. The control design includes two elements: the control law and adaptive law. The adaptive law is developed based on Lyupunov stability analysis of the controlled system, and it is responsible for estimating the potential uncertainties in the system. The effectiveness of the proposed adaptive synergetic control has been verified by numerical simulation using MATLAB/Simulink. The results showed that the ASC algorithm could give good tracking performance in the presence of uncertainty perturbations. In addition, a comparison study has been made to compare the tracking performance of ASC and that based on conventional synergetic control (CSC) for the ETV system. The simulated results showed that the performance of ASC outperforms that based on CSC. Moreover, the results showed that the estimation errors between the actual and estimated uncertainties are bounded and there is no drift in the developed adaptive law of ASC.

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Aiming at the networked cruise control scenario of CAV (connected autonomous vehicle) queue, we propose a new networked cruise control strategy for CAV by introducing the average information of ET (electronic throttle) opening of the downstream vehicle group as a feedback signal. By performing linear stability analysis on the new model, we derive its linear stability conditions. Further, we design exhaustive numerical simulation experiments aiming to systematically investigate the effect of the multi-vehicle ahead electronic throttle opening average feedback signal on CAV traffic stability, fuel consumption, and key emission factors, such as CO, HC, and NOx, during the cruise control process. The results show that the feedback signal can not only significantly improve the operational stability of the CAV traffic flow but also significantly improve its fuel consumption and the emission levels of CO, HC, and NOx. When the number of CAV vehicles in the feedback signal is set to three, the levels of CO, HC, and NOx emissions as well as fuel consumption in the road system can reach a stable and optimized state.

Signal Compensation Control With DDPG-Based Tuning Strategy for Electronic Throttle System

The electronic throttle system (ETS) is one of the important components of an automobile engine that adjust the engine air intake to directly affect the combustion power of the engine. In practical scenarios, Proportional-Integral-Derivative (PID) is a common and effective method. However, the traditional PID is difficult to satisfy the ETS performance requirements, e.g., the system response overshoot is not allowed to exist. Moreover, the controller gains are also difficult to tune manually. To tackle the above-mentioned issues, we propose a signal compensation control framework with an adaptive gain tuning strategy for the electronic throttle system, which consists of an upper-level to tune controller gains and a lower-level signal compensation controller to eliminate the nonlinear terms. The main feature of the signal compensation controller relies on two nonlinear compensators to remove the strong nonlinearity and uncertainty in the actual throttle system. Furthermore, we develop an adaptive gain tunning strategy based on Deep Deterministic Policy Gradient (DDPG) algorithm, and we analyze the corresponding stability and convergence properties of the proposed method. Finally, we illustrate the proposed algorithm on benchmarks and the tracking control problem using the real-world experiment platform of the ETS to show the effectiveness of the proposed algorithm.

Asynchronous Sliding-Mode Control for Discrete-Time Networked Hidden Stochastic Jump Systems With Cyber Attacks.

In this study, asynchronous sliding-mode control (SMC) for discrete-time networked hidden stochastic jump systems subjected to the semi-Markov kernel (SMK) and cyber attacks is investigated. Considering the statistical characteristic of the SMK, which is challenging to acquire in engineering, this study recognizes the SMK to be incomplete. Due to the mode mismatch between the original system and the control law in the operating process, a hidden semi-Markov model is proposed to describe the considered asynchronous situation. The main aim of this study is to construct an asynchronous SMC mechanism based on an incomplete SMK framework under the condition of random denial-of-service attacks so that the resulting closed-loop system can realize the mean-square stability. By virtue of the upper bound of the sojourn time in each mode, innovative techniques are developed for mean-square stability analysis under an incomplete SMK. Furthermore, an asynchronous SMC scheme is designed to achieve the reachability of the quasi-sliding mode. Finally, the effectiveness is verified using an electronic throttle model.

Modeling dedicated lanes for connected autonomous vehicles with poly-information uncertainties and electronic throttle dynamics

Numerous studies have demonstrated that connected autonomous vehicles and human-driven vehicles are now coexisting throughout a transitional phase. Traffic flow can be improved, the system can be stabilized, and less energy will be used with dedicated lanes for connected autonomous vehicles. Additionally, with few communication resources, no communication delivery is ever perfect, leading to issues with poly-information uncertainty. First, this paper provides a macroscopic model of heterogeneous traffic flow from the perspective of vehicle dynamics employing electronic throttle dynamics and poly-information uncertainty with/without dedicated lanes. Second, the mixed traffic flow linear stability criterion is derived using the linear stability theory. The third portion, which was based on the theoretical analysis, focused on the consequences of dedicated lane configurations on traffic flow as well as a discussion of the effects of various parameters on the stability of mixed traffic flow and energy consumption emissions. Finally, we modeled the Huanshan Road in Jinan, China using the experimental VISSIM platform. The analysis and demonstration of a two-way, four-lane road with or without dedicated lanes. The findings demonstrate that increasing connected autonomous vehicles penetration and dedicated lanes construction can increase traffic capacity, enhance the stability of traffic flow, and lower energy use and additional emissions. It is important to keep in mind that dedicated lanes must be built at an appropriate connected autonomous vehicle penetration rate to boost traffic flow without squandering resources.

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.

Extended state observer design for uncertainty estimation in electronic throttle valve system

AbstractThe Electronic Throttle Valve (ETV) is the core part of automotive engines which are recently used in control-by-wire cars. The estimation of its states and uncertainty is instructive for control applications. This study presents the design of Extended State Observer (ESO) for estimating the states and uncertainties of Electronic Throttle Valve (ETV). Two versions of ESOs have been proposed for estimation: Linear ESO (LESO) and Nonlinear ESO (NESO). The model of ETV is firstly developed and extended in state variable form such that the extended state stands for the uncertainty in system parameters. The design of both structures of ESOs are developed and a comparison study has been conducted to show the effectiveness of the proposed observers. Numerical simulation has been conducted to assess the performance of observers in estimating the states and uncertainties of ETV. The simulated results showed that both full order and reduced order models of ETV have the same transient characteristics. Moreover, the effectiveness of two versions of observers has been examined based on Root Mean Square of Error (RMSE) indicator. The results showed that the NESO has less estimation errors for both states and uncertainties than LESO.

Fixed-time Adaptive Sliding Mode Control for Vehicular Electronic Throttle with Actuator Saturation Using Extreme Learning Machine

Fixed-time Adaptive Sliding Mode Control for Vehicular Electronic Throttle with Actuator Saturation Using Extreme Learning Machine

Modeling and Tuning of Electronic Throttle Control System in Formula Student Car

The Lookprabida formula student team uses an electronic throttle control (ETC) system in the racecar. Unfortunately, the ETC system cannot regulate the throttle valve to closely pursue angular setpoints. Thus, this paper endeavors to improve the response of the ETC system in racing cars. The ETC system is first examined using frequency response analysis. Parameter identification is carried out to obtain a mathematical model for the electronic throttle body. The Ziegler-Nichols’ second method is then applied to preliminarily select the proportional integral derivative (PID) gains using MATLAB/Simulink®. Then, the ETC system fine-tuning on the experimental set is carried out. The final PID controller design results in a reduction in the settling time from 0.35 to 0.20 s, overshoot from 29.6 to 19.33%, and steady-state error from 2.00 to 0.22%. The racecar with the finally designed PID gains is tested on a training track; the root mean square error (RMSE) shows that the throttle valve approaches much closer to angular setpoints.

Chimp optimization‐based fuzzy controller for hybrid electric vehicle speed control using electronic throttle plate

AbstractToday's concern about climate change and the country's economic growth have made adopting hybrid electric vehicles (HEV) a worthy solution. Since the gas emissions of HEVs are closely related to their motor performance, developing an effective HEV motor control technique is essential. The performance and acceleration of an HEV motor are determined by the internal combustion engine (ICE) and the electronic throttle plate (ETP), which employ an air‐fuel mixture for vehicle propulsion. In order to deal with this problem, this article presented an optimal fuzzy controller that controls the throttle plate's angular position and motor speed in HEVs. However, the efficiency and reliability of fuzzy controllers depend upon their gains factor. So, a different heuristic algorithm is employed to self‐tuning the fuzzy logic controller (FLC) gain factor. The performance of the suggested ChOA‐fuzzy‐based controller was evaluated utilizing various control error indices and time‐domain stability. A comprehensive controller performance analysis using different metaheuristic techniques has been carried out to validate the proposed scheme. The findings show that the suggested ChOA‐fuzzy‐based controller performs better than other techniques.

Adaptive Second-Order Fixed-Time Sliding Mode Controller with a Disturbance Observer for Electronic Throttle Valves.

In order to enhance the precision and speed of control for electronic throttle valves (ETVs) in the face of disturbance and parameter uncertainties, an adaptive second-order fixed-time sliding mode (ASOFxTSM) controller is developed, along with disturbance observer compensation techniques. Initially, a control-oriented model specifically considering lumped disturbances within the ETV is established. Secondly, to address the contradiction between fast response and heavy chattering of conventional fixed-time sliding mode, a hierarchical sliding surface approach is introduced. This approach proficiently alleviates chattering effects while preserving the fixed convergence properties of the controller. Furthermore, to enhance the anti-disturbance performance of the ETV control system, an innovative fixed-time sliding mode observer is incorporated to estimate lumped disturbances and apply them as a feed-forward compensation term to the ASOFxTSM controller output. Building upon this, a parameter adaptive mechanism is introduced to optimize control gains. Subsequently, a rigorous stability proof is conducted, accompanied by the derivation of the expression for system convergence time. Finally, a comparison is drawn between the proposed controller and fixed-time sliding mode and super-twisting controllers through simulations and experiments. The results demonstrate the superiority of the proposed method in terms of chattering suppression, rapid dynamic response, and disturbance rejection capability.

RST Switching Bi-controller Based Flatness for an Electronic Throttle Valve

In this paper, an RST switching bi-controller, based on flatness and on Luenberger observers, is designed to control the opening angle change of an Electronic Throttle Valve (ETV), to compensate unexpected external disturbances and to detect sensor faults. Two identified mathematical linear models are established to simulate the ETV for two different positions of the throttle plate. The use of robust RST switching bi-controller based-flatness approach by the development of closedloop control is proposed, in order to obtain a stable system tracking a desired flat trajectory. The switching between the two models using stateflow tool is based on residual values generated by using the Luenberger observers in order to detect and to localize sensor faults occurrence. The observer’s gains are determined using Linear Matrix Inequalities (LMIs) taking into account the stability of the system based on Lyapunov theory. The simulation results show the efficiency of the developed robust switching RST bi-controller based-flatness in terms of tracking the desired angle’s reference trajectory, rejecting disturbances and detecting sensor faults.

Self-Tuning Backstepping Control with Kalman-like Filter for High-Precision Control of Automotive Electronic Throttle

The automotive electronic throttle (AET) control system has been widely applied in modern automotive engines, and accurate control of AET can improve engine performance as well as reduce pollution emissions. However, the noise in the sensor circuit and the variation in automotive driving conditions seriously affect the control performance of the AET system, making controller designing challenging. This paper proposes a self-tuning backstepping control with a Kalman-like filter (SBCKLF) strategy. First, the noise affecting the position sensor measurement is verified to be non-Gaussian by acquiring and processing the noise signal. To eliminate its influence on control precision, a Kalman-like filter is introduced to estimate the real position of the valve. Then, a self-tuning backstepping controller is designed to automatically adapt to the variation in vehicle driving conditions. A self-tuning algorithm based on fuzzy control is used to tune the parameters of the backstepping controller online, so as to optimize the controller performance. Finally, an experimental platform based on dSPACE for the AET control system is built to perform the controller comprehensive test in a real-time environment. Experimental results and performance analysis demonstrate the effectiveness of the proposed SBCKLF strategy. Compared to the best results of other methods, the proposed method reduces the maximum and root mean square tracking errors by 21.65% and the average error by 12.89%. The steady-state and tracking error bounds are controlled to 0.9° and 2.3°, respectively. It also shows that the SBCKLF strategy has the strongest robustness as well as the best response speed.

Adaptive Event-Triggered Asynchronous Fault Detection for Nonlinear Markov Jump Systems With Its Application: A Zonotopic Residual Evaluation Approach

In this paper, the event-triggered asynchronous fault detection (FD) problem is investigated for a class of nonlinear Markov jump systems (NMJSs) via zonotopic residual evaluation (ZRE). An adaptive event-triggered scheme (AETS) is utilized to reduce the redundant data releasing frequency such that both communication resources and network bandwidth can be saved as much as possible. The hidden Markov model (HMM) with generally hybrid probabilities is introduced into the FD framework to fully characterize the asynchronization phenomenon between the plant and the FD filter as well as the event generator and to address both the general transition and mode detection information issues simultaneously. By applying the double variables-based decoupling principle and variable substitution principle, a co-design criterion of optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}/H_{\infty }$</tex-math></inline-formula> asynchronous reduced-order FD filter and AETS is derived fully considering accessible mode information, such that the generated residual signal is sensitive to system faults while being robust to amplitude-bounded exogenous disturbances and measurement noise. Furthermore, a novel ZRE strategy with dynamic thresholds is developed for FD in NMJSs. Finally, an application of automotive electronic throttle body is used to illustrate the effectiveness of the proposed approach.

A Novel Adaptive Control Scheme for Automotive Electronic Throttle Based on Extremum Seeking

To achieve rapid and high-precision servo control of an electronic throttle, an adaptive control scheme is proposed based on the extremum seeking (ES), which consists of a variable-gain adaptive proportional-integral (ES-API) controller and an adaptive compensator (ES-ACP). The two gains ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K_p}$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K_i}$</tex-math> </inline-formula> ) of the ES-API controller are designed as maps with respect to the tracking error, and the parameters of these maps are learned by ES. Additionally, the ES-ACP is applied to compensate for the strong nonlinearity inherent in an electronic throttle control (ETC) system, whose parameters are also learned by ES. During parameter learning, an objective function is utilized to quantify the tracking error of the opening angle of the electronic throttle plate, and then the parameters are learned using a step reference signal and a ramp reference signal. ES optimizes the above parameters by reducing the objective function to achieve a more favorable tracking response. Five reference signals are used to evaluate the learned controller after the parameter learning process is completed. Experiments were performed on a test bench equipped with an electronic throttle, and the experimental results show that the control scheme is capable of tracking multiple reference trajectories quickly and accurately.

Non-lane-discipline-based continuum model considering the effect of lateral gaps and electronic throttle dynamics

Non-lane-discipline-based continuum model considering the effect of lateral gaps and electronic throttle dynamics

SMC for Discrete-Time Networked Semi-Markovian Switching Systems With Random DoS Attacks and Applications

This article concentrates on the discrete-time sliding mode control (DTSMC) problem for uncertain networked semi-Markovian switching systems (S-MSSs) under random denial-of-service (DoS) attacks. The semi-Markovian kernel (SMK) approach is utilized such that the switching among different modes is mutually governed by the transition probability and the sojourn-time distribution function. Considering randomly occurring DoS attacks, a sliding mode function related to the attack probability is constructed to analyze the impact of malicious attacks. Then, sufficient conditions under the equivalent DTSMC law are derived in view of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> -error mean square stability criterion. Furthermore, the synthesis problem of the proposed DTSMC law ensures that the resulting closed-loop system dynamics can be driven onto the prespecified sliding region within a limited time. Finally, an electronic throttle control system is shown to validate the proposed algorithm.