Abstract:Accurate pressure control and fast dynamic response are vital to the pneumatic electric braking system (PEBS) for those commercial vehicles that require higher regulation precision of braking force on four wheels when braking force distribution is carried out under some conditions. Due to the lagging information acquisition, most feedback-based control algorithms are difficult to further improve the dynamic response of PEBS. Meanwhile, feedforward-based control algorithms like predictive control perform well i… Show more
“…The _ m p signal from the ''New flow model'' block is sent to the ''Pressure dynamics'' block, and the theoretical pressure change rate ( _ P t ) is derived with the help of equation (9). Then, the integral of the pressure change rate signal is taken to obtain absolute theoretical pressure value ([P a ] t ).…”
Section: Validation Tests Of New Flow Modelmentioning
confidence: 99%
“…In these systems, the control of pressure, position, or velocity parameters is usually performed by controlling the mass flow of the air via a control valve. [6][7][8][9] Thus, the system of interest must be known to some extent, especially for the model-based control strategies, wherein the correct implementation of system model is crucial to meet the desired control performance. It is to be noted that the nonlinear relationship between the mass flow rate of air and the pressure dynamics makes the modeling issue a tedious task.…”
In this study, a new compressible flow model for small orifice openings in pneumatic proportional directional control valves has been proposed. It is crucial to precisely control pneumatic valves over all control ranges; yet, conventional flow models fail around the closed position of the valve. The main deficit of the existing studies in the literature is to assume constant values for the parameters of the flow model over changing operating conditions. It has been demonstrated that these rough assumptions are insufficient in precisely predicting the mass flow rate, particularly for small orifice openings. An enhanced experimental setup has been introduced to improve the effectiveness of the proposed model. The cracking pressure ratio and parameters of the model have been identified with experimental method. In the proposed model, new empirical coefficients have been established after a thorough investigation of the impact of supply pressure on the flow behavior of the valve. Validation studies of the model in both the filling and exhausting states of the valve have been carried out at various supply pressures and orifice openings, yielding rather promising modeling performances. In validation tests, the real pressure and the pressure produced by new model have been compared, and good agreement has been achieved with 0.0039% absolute error. According to the findings, the proposed improved flow model can be selected in precision pneumatic control applications.
“…The _ m p signal from the ''New flow model'' block is sent to the ''Pressure dynamics'' block, and the theoretical pressure change rate ( _ P t ) is derived with the help of equation (9). Then, the integral of the pressure change rate signal is taken to obtain absolute theoretical pressure value ([P a ] t ).…”
Section: Validation Tests Of New Flow Modelmentioning
confidence: 99%
“…In these systems, the control of pressure, position, or velocity parameters is usually performed by controlling the mass flow of the air via a control valve. [6][7][8][9] Thus, the system of interest must be known to some extent, especially for the model-based control strategies, wherein the correct implementation of system model is crucial to meet the desired control performance. It is to be noted that the nonlinear relationship between the mass flow rate of air and the pressure dynamics makes the modeling issue a tedious task.…”
In this study, a new compressible flow model for small orifice openings in pneumatic proportional directional control valves has been proposed. It is crucial to precisely control pneumatic valves over all control ranges; yet, conventional flow models fail around the closed position of the valve. The main deficit of the existing studies in the literature is to assume constant values for the parameters of the flow model over changing operating conditions. It has been demonstrated that these rough assumptions are insufficient in precisely predicting the mass flow rate, particularly for small orifice openings. An enhanced experimental setup has been introduced to improve the effectiveness of the proposed model. The cracking pressure ratio and parameters of the model have been identified with experimental method. In the proposed model, new empirical coefficients have been established after a thorough investigation of the impact of supply pressure on the flow behavior of the valve. Validation studies of the model in both the filling and exhausting states of the valve have been carried out at various supply pressures and orifice openings, yielding rather promising modeling performances. In validation tests, the real pressure and the pressure produced by new model have been compared, and good agreement has been achieved with 0.0039% absolute error. According to the findings, the proposed improved flow model can be selected in precision pneumatic control applications.
“…The controller is based on a simplified expression of system dynamics and considers the pressure variation caused by the switching state of the solenoid valve at the current and the last sampling time. Yang et al [2] proposed a logic threshold control scheme based on an EBS that combines analog model predictive control and proportional control. Hamada et al [3] conducted a comprehensive discussion on the basic principles of regenerative braking systems.…”
In the electronic brake system (EBS) of commercial vehicles, due to the compressibility of gas, it is difficult to achieve accurate control in the pneumatic pipeline. To address this issue, a vertical load estimator based on unscented particle filtering (UPF) was designed, which can estimate vertical load during the running of the vehicle. Then, the EBS dynamics model was established based on software, including a brake signal sensor, single-channel bridge control module, ABS solenoid valve, and dual-channel bridge control module. Finally, based on the characteristics of the EBS valve, the control algorithm of the valve was studied, and the algorithm was tested using a hardware-in-the-loop experiment. The experiment results showed that the designed algorithm could improve braking performance.
Accurate and rapid braking pressure regulation in electric pneumatic braking systems (EPBS) is vital to vehicle safety. Due to the switching behaviors of the on-off solenoid valves, the operation of the EPBS shows a hybrid nature with both continuous variables and discrete events, which raises the hybrid control problem. One of the possible solutions is to employ the hybrid model predictive controller with the mixed logical dynamical (MLD) model based on the linear approximation of the system dynamics. However, the nonlinearity and complexity of the EPBS make the MLD model obtained by linearizing the system equations directly require high storage and computing capacity. To address these issues, this article presents a practical hybrid model predictive controller based on the system dynamics simplified expressions considering the EPBS pressure variations caused by on-off solenoid valve states at the current sampling time and the last sampling time. The relationship between the pressure variations and the on-off solenoid valve states is first studied by the system mathematical model, followed by applying the mixed logic dynamical modeling approach to establish the hybrid model of the pressure continuous dynamics with discrete features of on-off solenoid valves. Based on these, a hybrid model predictive controller is formulated to solve the EPBS pressure control problem. The simulations and bench experiments are carried out to verify the controller. Besides, an existing model predictive control (MPC) controller is compared with the proposed controller. All the results demonstrate the effectiveness of the hybrid model predictive controller.
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