Precise braking pressure control of a pneumatic electric braking system (PEBS) poses challenging non-linear control problems, since it can operate in several distinct discrete processes (the pressure increasing, holding and decreasing) by switching the on-off solenoid valves and the piston of relay valve moves irregularly. This article describes the development and experimental validation of a viable controller for an electronically controlled braking pressure based on the theory of model predictive control (MPC). The controller design consists of two parts. The non-linear characteristic is first studied to ensure the pressure response of PEBS when the opening time is 4-10 ms in a fixed period, followed by calculation and prediction of the optimal on-off time of inlet valve and release valve based on the current braking pressure to realize steady state. A logic rule is designed to ensure that the MPC controller and feedback control work coordinate to guarantee engineering reliability. A comprehensive system model is derived to help characterize system non-linearities and design the MPC predictive model. Simulations and experimental results are presented finally to show how the MPC and feedback control strategy can be successfully applied to solve the braking pressure control of a PEBS in a systematic way.

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 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 the 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 is 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 0.08 bar. Compared with the traditional control algorithm, the rapidity is improved by 32.5%.

An adaptive cruise control (ACC) system can improve safety and comfort during driving by taking over longitudinal control of the vehicle. It requires the coordination between the upper-layer controller and the lower-layer actuators. In this paper, a hierarchical anti-disturbance cruise control architecture based on electronic stability control (ESC) system is proposed. The upper-layer controller outputs the desired longitudinal acceleration or deceleration to the lower-layer actuators. In order to improve the accuracy of model prediction and achieve the coordinated control of multiple objectives, an upper-layer model prediction cruise controller is established based on feedback control and disturbance compensation. In addition, based on the hydraulic control unit (HCU) model and the vehicle longitudinal dynamics model, a lower-layer nonlinear model predictive deceleration controller is proposed in order to solve the problems of pressure fluctuations and the low accuracy of small decelerations when ESC is used as the actuator for the ACC system. Finally, the simulation and experimental tests were carried out. The results show that the proposed control architecture can improve the stability and comfort of the cruise control process. Moreover, compared with the traditional PID deceleration controller, it effectively improves the deceleration control accuracy.

Motor skill learning of dental implantation surgery is difficult for novices because it involves fine manipulation of different dental tools to fulfill a strictly pre-defined procedure. Haptics-enabled virtual reality training systems provide a promising tool for surgical skill learning. In this paper, we introduce a haptic rendering algorithm for simulating diverse tool-tissue contact constraints during dental implantation. Motion forms of an implant tool can be summarized as the high degree of freedom (H-DoF) motion and the low degree of freedom (L-DoF) motion. During the H-DoF state, the tool can move freely on bone surface and in free space with 6 DoF. While during the L-DoF state, the motion degrees are restrained due to the constraints imposed by the implant bed. We propose a state switching framework to simplify the simulation workload by rendering the H-DoF motion state and the L-DoF motion state separately, and seamless switch between the two states by defining an implant criteria as the switching judgment. We also propose the virtual constraint method to render the L-DoF motion, which are different from ordinary drilling procedures as the tools should obey different axial constraint forms including sliding, drilling, screwing and perforating. The virtual constraint method shows efficiency and accuracy in adapting to different kinds of constraint forms, and consists of three core steps, including defining the movement axis, projecting the configuration difference, and deriving the movement control ratio. The H-DoF motion on bone surface and in free space is simulated through the previously proposed virtual coupling method. Experimental results illustrated that the proposed method could simulate the 16 different phases of the complete implant procedures of the Straumann ® Bone Level(BL) Implants 4.8-L12 mm. According to the output force curve, different contact constraints could be rendered with steady and continuous output force during the operation procedures.

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%.