Improvement of Dynamic Characteristics of Purpose-Built Vehicles Using Semi-Active Suspension System.

The diversification of mobility into services such as smart stores and conference rooms has accelerated the development of purpose-built vehicles (PBVs)-vehicles designed for specific purposes that utilize an extended electric vehicle chassis and autonomous driving technology. Despite the standards on speed bump dimensions stipulated by the National Land Transportation Act of the Republic of Korea, real-world speed bumps feature varying widths and heights that deviate from these standards. In this study, a velocity equation was derived via regression analysis to achieve the desired dynamic characteristics for a PBV passing over speed bumps with varying shapes through two types of semi-active suspension control: proportional-integral-differential (PID) and linear-quadratic-regulator (LQR). For a cargo-transport PBV, the PID and LQR controllers increased the velocity by 23.74% and 50.74%, respectively, under different speed bump widths and by 19.44% and 38.31%, respectively, under different speed bump heights. Moreover, an analysis of the vibration dose value (VDV), an indicator of ride comfort, revealed that the VDVs calculated using the velocity equation were within an acceptable error range of 10% above the target VDV. These findings provide insights into the speed control required for different types of autonomous PBVs to ensure ride comfort, as well as minimize the driving duration, depending on the specific purpose of the vehicle.

Fast Distributed Model Predictive Control Method for Active Suspension Systems.

In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

Identification of Control-Related Signal Path for Semi-Active Vehicle Suspension with Magnetorheological Dampers.

This paper presents a method for the identification of control-related signal paths dedicated to a semi-active suspension with MR (magnetorheological) dampers, which are installed in place of standard shock absorbers. The main challenge comes from the fact that the semi-active suspension needs to be simultaneously subjected to road-induced excitation and electric currents supplied to the suspension MR dampers, while a response signal needs to be decomposed into road-related and control-related components. During experiments, the front wheels of an all-terrain vehicle were subjected to sinusoidal vibration excitation at a frequency equal to 12 Hz using a dedicated diagnostic station and specialised mechanical exciters. The harmonic type of road-related excitation allowed for its straightforward filtering from identification signals. Additionally, front suspension MR dampers were controlled using a wideband random signal with a 25 Hz bandwidth, different realisations, and several configurations, which differed in the average values and deviations of control currents. The simultaneous control of the right and left suspension MR dampers made it necessary to decompose the vehicle vibration response, i.e., the front vehicle body acceleration signal, into components related to the forces generated by different MR dampers. Measurement signals used for identification were taken from numerous sensors available in the vehicle, e.g., accelerometers, suspension force and deflection sensors, and sensors of electric currents, which control the instantaneous damping parameters of MR dampers. The final identification was carried out for control-related models evaluated in the frequency domain and revealed several resonances of the vehicle response and their dependence on the configurations of control currents. In addition, the parameters of the vehicle model with MR dampers and the diagnostic station were estimated based on the identification results. The analysis of the simulation results of the implemented vehicle model carried out in the frequency domain showed the influence of the vehicle load on the absolute values and phase shifts of control-related signal paths. The potential future application of the identified models lies in the synthesis and implementation of adaptive suspension control algorithms such as FxLMS (filtered-x least mean square). Adaptive vehicle suspensions are especially preferred for their ability to quickly adapt to varying road conditions and vehicle parameters.

Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor.

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle's pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.

Adaptive backstepping-based tracking control design for nonlinear active suspension system with parameter uncertainties and safety constraints.

This paper proposes a novel constraint adaptive backstepping based tracking controller for nonlinear active suspension system with parameter uncertainties and safety constraints. By introducing the virtual control input and reference trajectories, the adaptive control law is developed to stabilize both of the vertical and pitch motions of vehicle body using backstepping technique and Lyapunov stability theory, and further to track the predefined reference trajectories within a finite time, which not only ensure the safety performance requirements, but also achieve improvements in riding comfort and handling stability of vehicle active suspension system. Next, the stability analysis on zero dynamics error system is conducted to ensure that all the safety performance indicators are all bounded and the corresponding upper bounds are estimable. Finally, a numerical simulation is provided to verify the effectiveness of the proposed controller and to address the comparability between the classical Barrier-Lyapunov Function based adaptive tracking controller and the proposed controller.

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance.

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes.