Research on Accurate Modeling and Control for Pneumatic Electric Braking System of Commercial Vehicle Based on Multi-Dynamic Parameters Measurement

2020 ◽  
Author(s):  
Yongtao Zhao ◽  
Yiyong Yang ◽  
Xiuheng Wu ◽  
Xingjun Tao
Keyword(s):  
Dynamic Response ◽  
Real Time ◽  
Predictive Control ◽  
Control Algorithm ◽  
Control Algorithms ◽  
Dynamic Parameters ◽  
Braking System ◽  
Control Scheme ◽  
Braking Force ◽  
Electric Braking

Abstract Accurate pressure control and fast dynamic response are vital to the pneumatic electric braking system (PEBS) for that commercial vehicles 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 in improving dynamic performance. but because of the large amount of computation and complexity of this kind of control algorithm, it cannot be applied in real-time on single-chip microcomputer, and it is still in the stage of theoretical research at present. To address this issue and for the sake of engineering reliability, this article presents a logic threshold control scheme combining analogous model predictive control (AMPC) and proportional control. In addition, an experimental device for real-time measuring PEBS multi-dynamic parameters is built. After correcting the key parameters, the precise model is determined and the influence of switching solenoid valve on its dynamic response characteristics is studied. For the control scheme, numerical and physical validation are executed to demonstrate the feasibility of the strategy and for the performance of the controller design. The experimental results show that the dynamic model of PEBS can accurately reflect its pressure characteristics. Furthermore, under different air source pressures, the designed controller can stably control the pressure output of PEBS and ensure that the error is within 8KPa. Compared with the traditional control algorithm, the rapidity is improved by 32.5%.

Real-Time Control Algorithms for a Hybrid Electric Race Car Using a Two-Level Model Predictive Control Scheme

2017 ◽  
Vol 66 (12) ◽  
pp. 10911-10922 ◽  
Author(s):  
Mauro Salazar ◽  
Camillo Balerna ◽  
Philipp Elbert ◽  
Fernando P. Grando ◽  
Christopher H. Onder
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Control Algorithms ◽  
Real Time Control ◽  
Race Car ◽  
Time Control ◽  
Control Scheme ◽  
Level Model ◽  
Two Level Model

Study of a Method for Improving the Anti-Lock Brake System of Electric Vehicle

2012 ◽  
Vol 157-158 ◽  
pp. 542-545 ◽  
Author(s):  
Liang Chu ◽  
Liang Yao ◽  
Zi Liang Zhao ◽  
Wen Ruo Wei ◽  
Yong Sheng Zhang
Keyword(s):  
Electric Vehicle ◽  
Energy Recovery ◽  
Control Algorithm ◽  
System Structure ◽  
Regenerative Braking ◽  
Threshold Control ◽  
Braking System ◽  
Simulink Model ◽  
Braking Force ◽  
The Stability

The Anti-lock Braking System (ABS) of Electric Vehicle (EV) is improved in this paper. Based on the research of system structure and motor, a new method is proposed to adjust the threshold and coordinate the motor braking force with the friction braking force. So the traditional threshold control algorithm of ABS is improved for the EV. The simulation results based on the MATLAB/Simulink model indicate that the improved ABS can keep the wheels in the stability region and decrease the motor regenerative braking force as soon as possible. The balance between brake safety and energy recovery is achieved through this method.

scholarly journals Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8493
Author(s):  
Adnan Khalid ◽  
Mujtaba Hussain Jaffery ◽  
Muhammad Yaqoob Javed ◽  
Adnan Yousaf ◽  
Jehangir Arshad ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Constrained Optimization ◽  
Predictive Control ◽  
Control Algorithm ◽  
Computational Time ◽  
Control Algorithms ◽  
Control Framework ◽  
Output Constraints ◽  
Powered Descent ◽  
Stated Problem

It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling’s ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

Buoyancy Control for Low-Speed Under-Actuated AUVs

2014 ◽  
Vol 644-650 ◽  
pp. 735-740
Author(s):  
Yu Ling Ye
Keyword(s):  
Real Time ◽  
Control Algorithm ◽  
Sea Water ◽  
Water Tank ◽  
Low Speed ◽  
The Real ◽  
Control Approach ◽  
Control Scheme ◽  
Control Devices ◽  
Buoyancy Control

An engineering buoyancy control scheme was proposed to compensate the change of residual buoyancy for low-speed under-actuated AUVs. The buoyancy control system is made up of buoyancy controller and two sets of buoyancy control devices include: water tank, sea water pump, valve grouping, pipeline etc. Buoyancy control devices were configured to both head and tail part of the AUV symmetrical to its buoyant center. Depth control algorithm and buoyancy control algorithm were proposed separately. In the process of voyage at a constant depth, the real time speed, pitch angle and depth error were detected to evaluate the residual buoyancy indirectly, and then the real time buoyancy control was executed by pumping the water into or out of the tanks. The buoyancy control scheme was applied to the type of low-speed under-actuated AUV and simulation and experiments results show that the buoyancy control approach and the control laws are feasible and effective.

scholarly journals Stair-like Multivariable Generalized Predictive Control of Pulverizing System in Thermal Power Plants

2018 ◽  
Vol 3 (1) ◽  
pp. 21
Author(s):  
Jiansheng ZHANG ◽  
Gang ZhANG ◽  
Yaokui GAO ◽  
Yong HU
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Power Plants ◽  
Thermal Power ◽  
Engineering Application ◽  
Generalized Predictive Control ◽  
Practical Application ◽  
Efficient Utilization ◽  
Control Scheme ◽  
Control Schemes

Pulverizing system is an important part in the clean and efficient utilization of coal in thermal power plant, and the optimal control of the system is an important way to achieve this goal. This paper presents a stair-like multivariable generalized predictive control scheme for a pulverizing system. This control scheme focuses on the problem of predictive control algorithm in practical application, and integrates the feedforward experience in traditional control schemes of pulverizing system. Simulation results showed that the scheme are able to realize the decoupling control of the pulverizing system, avoid the problem of matrix inversion, reduce the amount of calculation, and has certain engineering application value.

scholarly journals Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

2021 ◽  
Author(s):  
Nikolaos Tsokanas ◽  
Roland Pastorino ◽  
Bozidar Stojadinovic
Keyword(s):  
Dynamic Response ◽  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Hybrid Simulation ◽  
Adaptive Model ◽  
Actuation System ◽  
Tracking Controller ◽  
Adaptive Model Predictive Control ◽  
Hybrid Simulations

Real-time hybrid simulation is an experimental method used to obtain the dynamic response of a system whose components consist of loading-rate-sensitive physical and numerical substructures. The coupling of these substructures is achieved by actuation systems, i.e., an arrangement of motors or actuators, which are responsible for continuously synchronizing the interfaces of the substructures and are commanded in closed-loop control setting. To ensure high fidelity of such hybrid simulations, performing them in real-time is necessary. However, real-time hybrid simulation poses challenges as the inherent dynamics of the actuation system introduce time delays, thus modifying the dynamic response of the investigated system and hence compromising the simulation's fidelity and trust in the obtained response quantities. Therefore, a reference tracking controller is required to adequately compensate for such time delays.In this study, a novel tracking controller is proposed for dynamics compensation in real-time hybrid simulations. It is based on an adaptive model predictive control approach, a linear time-varying Kalman filter, and a real-time model identification algorithm. Within the latter, auto-regressive exogenous polynomial models are identified in real-time to estimate the changing plant dynamics and used to update the prediction model of the tracking controller. A parametric virtual real-time hybrid simulation case study is used to validate the performance and robustness of the proposed control scheme. Results demonstrate the effectiveness of the proposed controller for real-time hybrid simulations.

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