This paper presents a polytopic linear parameter varying (LPV) controller design methodology for nonlinear anti-vibration suspension based on stochastic road identification. As the main nonlinear ...component in semi-active suspension, the continuous damping control (CDC) damper value is mapped by damping velocity and control current through a bench experiment. linear parameter varying gain-scheduled method is applied for dealing with the nonlinear deterministic model. As the uncertainties caused by road stochastic excitation to suspension cannot be ignored, it is essential to identify the excitation signal as a measurable variable parameter. Empirical-mode-decomposition (EMD) and support vector machine (SVM) are utilized to identify and classify the road stochastic signal input into standard level, so polytopic LPV controller can be designed with CDC damper velocity, control current, and road level as variable parameters. Meanwhile the exponential increase of high-order plant elements in multi-parameter LPV model makes it too complicated for real-time computing. Hence the tensor product model transformation is utilized to identify the orthogonal basis of system plant so that low-rank approximation of it can be implemented by truncated high-order singular value decomposition (T−HOSVD) method. Real-time simulation shows the performance and feasibility of controller, which can efficaciously identify the road stochastic excitation as measurable variable parameters for nonlinear suspension polytopic LPV control.
Visual-Inertial Odometry (VIO) is subjected to additional unobservable directions under the special motions of ground vehicles, resulting in larger pose estimation errors. To address this problem, a ...tightly-coupled Ackermann visual-inertial odometry (ACK-MSCKF) is proposed to fuse Ackermann error state measurements and the Stereo Multi-State Constraint Kalman Filter (S-MSCKF) with a tightly-coupled filter-based mechanism. In contrast with S-MSCKF, in which the inertial measurement unit (IMU) propagates the vehicle motion and then the propagation is corrected by stereo visual measurements, we successively update the propagation with Ackermann error state measurements and visual measurements after the process model and state augmentation. This way, additional constraints from the Ackermann measurements are exploited to improve the pose estimation accuracy. Both qualitative and quantitative experimental results evaluated under real-world datasets from an Ackermann steering vehicle lead to the following demonstration: ACK-MSCKF can significantly improve the pose estimation accuracy of S-MSCKF under the special motions of autonomous vehicles, and keep accurate and robust pose estimation available under different vehicle driving cycles and environmental conditions. This paper accompanies the source code for the robotics community.
Multi-strut platform is widely used for precise instrument vibration isolation. In this paper, a Newton-Euler based 6-DOF 12-strut platform model is proposed. Nonlinearity of platform dynamic ...component is derived by establishing polytopic linear parameter varying (LPV) system. To guarantee the linearization accuracy of LPV system while reducing model elements to a real-time computing level. Tensor product(TP) model transformation and truncated high-order singular value decomposition(HOSVD) are used to decompose LPV system high-order tensor into unique principle basis. Then low-rank approximation of system is implemented by discarding minor singular basis vectors, for the sake of minimizing storage space and computing complexity. And then the parameter varying system is represented by convex combination of discretized system vertexes. So quadratic regulator method can be applied to vertex linear time-invariant subsystem controller to construct the global controller. Performance of the proposed multi-strut platform is demonstrated through hardware in loop simulation.
The observability of the scale direction in visual–inertial odometry (VIO) under degenerate motions of intelligent and connected vehicles can be improved by fusing Ackermann error state measurements. ...However, the relative kinematic error measurement model assumes that the vehicle velocity is constant between two consecutive camera states, which degrades the positioning accuracy. To address this problem, a consistent monocular Ackermann VIO, termed MAVIO, is proposed to combine the vehicle velocity and yaw angular rate error measurements, taking into account the lever arm effect between the vehicle and inertial measurement unit (IMU) coordinates with a tightly coupled filter-based mechanism. The lever arm effect is firstly introduced to improve the reliability for information exchange between the vehicle and IMU coordinates. Then, the process model and monocular visual measurement model are presented. Subsequently, the vehicle velocity and yaw angular rate error measurements are directly used to refine the estimator after visual observation. To obtain a global position for the vehicle, the raw Global Navigation Satellite System (GNSS) error measurement model, termed MAVIO-GNSS, is introduced to further improve the performance of MAVIO. The observability, consistency and positioning accuracy were comprehensively compared using real-world datasets. The experimental results demonstrated that MAVIO not only improved the observability of the VIO scale direction under the degenerate motions of ground vehicles, but also resolved the inconsistency problem of the relative kinematic error measurement model of the vehicle to further improve the positioning accuracy. Moreover, MAVIO-GNSS further improved the vehicle positioning accuracy under a long-distance driving state. The source code is publicly available for the benefit of the robotics community.
As a significant technology in the automotive manufacturing industry, weight reduction in vehicle design has attracted much attention. Its effect on energy saving and emission reduction is prominent. ...The application of lightweight material is commonly adopted as a primary way of weight reduction. However, material selection is often subject to multi-perspective performance characteristics, e.g., mechanical and societal properties, and therefore, an effective multi-criteria decision-making (MCDM) method is needed. This paper presents a systematic hierarchical structure of multi-perspective indices for optimal lightweight material selection, including mechanical, durability, societal, and technical properties. A hybrid evaluation approach (G-TOPSIS) integrating grey relation analysis and technique for order performance by similarity to ideal solution (TOPSIS) is applied to evaluate lightweight material alternatives and obtain an optimal one. A case study, i.e., 17 kinds of lightweight materials, is conducted to verify the hierarchical structure and the MCDM method. In addition, a sensitivity analysis is conducted to monitor the robustness of solution ranking to changes. The results show that this method provides an effective decision-making tool for optimal lightweight material selection for automobile applications.
The testing and evaluation system has been the key technology and security with its necessity in the development and deployment of maturing automated vehicles. In this research, the ...physics–intelligence hybrid theory-based dynamic scenario library generation method is proposed to improve system performance, in particular, the testing efficiency and accuracy for automated vehicles. A general framework of the dynamic scenario library generation is established. Then, the parameterized scenario based on the dimension optimization method is specified to obtain the effective scenario element set. Long-tail functions for performance testing of specific ODD are constructed as optimization boundaries and critical scenario searching methods are proposed based on the node optimization and sample expansion methods for the low-dimensional scenario library generation and the reinforcement learning for the high-dimensional one, respectively. The scenario library generation method is evaluated with the naturalistic driving data (NDD) of the intelligent electric vehicle in the field test. Results show better efficient and accuracy performances compared with the ideal testing library and the NDD, respectively, in both low- and high-dimensional scenarios.
The steady-state error problem of autonomous vehicle MPC-based motion control has not been effectively solved for a long time. This problem is more serious for lateral and longitudinal coupling ...control problems of vehicles with over-actuated configurations. Based on our newly designed general offset free MPC (OF-MPC) solver and the TMeasy tire model, a steady-state error free control strategy for simultaneous stability and path following control of four-wheel steering and four-wheel drive vehicles is proposed. OF-MPC uses the disturbances term to describe the model mismatch and external disturbances, then uses the Kalman filter to observe the disturbances, and finally considers the disturbances in the optimization stage to realize the control without steady-state error. Realtime simulation results show that OF-MPC can solve model mismatch and external disturbances problems, and the steady-state error free control is realized. The simulation results of the double lane change maneuver show that the OF-MPC dynamic control performance is also better than the traditional MPC (TRA-MPC), which is more obvious when the vehicle is at the stability boundary and under various constant or time-varying disturbances. Regardless of the dimensions and complex constraints of this problem, real-time performance is still guaranteed, thanks to the proposed OF-MPC. When the horizon length is 100, the average time consumption is only about 15 milliseconds.
The low-velocity impact properties and the optimal hybrid ratio range for improving the property of hybrid composites are studied, and the application of hybrid composites in automobile engine hoods ...is discussed in this paper. The low-velocity impact properties of the hybrid composite material are simulated under different stacking sequences and hybrid ratios by finite element simulation, and the accuracy of the finite element model (FEM) is verified through experiments. Increasing the proportion of carbon fiber (CF) in the hybrid layer and placing the basalt fiber (BF) on the compression side can improve the energy absorption capacity under low-velocity impact loads. CF/BF hybrid composite hoods are optimized based on the steel hood and the low-velocity impact performance of the hybrid composite. The BCCC layer absorbs the most energy under low-velocity impact loads. Compared with CFRP, the energy absorbed under 10 J and 20 J impact energy is increased by 26.1% and 14.2%, respectively. Through the low-velocity impact properties of hybrid composites, we found that placing BF on the side of the load and keep the ratio below 50%, while increasing the proportion of CF in the hybrid laminate can significantly improve the property of the hybrid laminate. The results show that the stiffness and modal properties of the hybrid composite can meet the design index requirements, and the pedestrian protection capability of the hood will also increase with the increase in the proportion of BF.
When vehicles with traditional passive suspension systems are driving in complex terrain, large swing and vibrations of the car body make passengers and goods uncomfortable and unstable, even at very ...low-speed conditions. Considering the actual need for intelligent resource exploration in the sustainable economy, visual-based perception and localization systems of unmanned vehicles still cannot handle the sensor noise coursed by large body motions. In order to improve the stability and safety of vehicles in complex terrain, an attitude control system is proposed for mainly eliminating the external body motions of the vehicle by using series active suspensions. A model predictive control method considered the differences between the simulated and real vehicle, and the performance restrictions of actuators are used to design the attitude controller for reducing the heaving, pitching, and rolling motions of the vehicle. After simulations and real car tests, the results show that the proposed attitude controller can significantly improve the attitude stability of vehicles in harsh terrain.
The energy crisis and environmental concerns worldwide have helped usher in the age of electric vehicles (EVs) and hybrid EVs (HEVs). The interior permanent magnet motors (IPMMs) are widely used in ...these vehicles. The analysis of the armature reaction field is the most critical issue in the study of IPMMs since it determines the characters of torque, efficiency, vibration, and the radiated acoustic noise. This paper provides a calculation method of the armature reaction magnetic field (ARMF) of an IPMM. First, the formulas of ARMF without magnetic barrier are derived. Second, the relative permeance function of an IPMM is calculated. Third, the analytical solution of the ARMF of an IPMM is derived by applying the armature reaction magnetic field with unsaturated rotor multiplied by relative permeance function. Finally, several results of comparisons between the calculation method proposed in this paper and the finite element method are presented. Based on the calculation method proposed in this paper, the magnetic barrier’s influence on the ARMF is studied. The spatial harmonic orders and time harmonic orders of the ARMF of IPMM are revealed respectively.
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