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A 3-degree of freedom (DOF) nonlinear model including yaw, lateral, and roll motions was constructed, and a numerical simulation of chaotic behavior was performed using the Lyapunov exponent method. The vehicle motion is complex, manifesting double-periodic, quasi-periodic, and chaotic phases, which negatively affects the vehicle lateral stability. To control this chaotic behavior, a controller was designed based on the sliding mode variable structure control (SM-VSC) method. To decrease chattering and further improve lateral stability of the vehicle under extreme operating conditions, the adaptive power reaching law was realized by using a fuzzy control method. The performance of the SM-VSC system was simulated by using Matlab/simulink. The simulation results including the uncontrol, SM-VSC control, and adaptive-reaching SM-VSC control were compared, which demonstrated that the adaptive-reaching SM-VSC control method is more effective in suppressing the chaotic phase of the vehicle lateral motion. The approach proposed in this paper can significantly improve a vehicle’s lateral stability under extreme operating conditions.

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In this paper, a new curve-lane recognition algorithm is proposed. The algorithm uses edge point curvature voting to determine the region of interest based on near-vision straight-lane information. First, information is detected in the near-vision area regarding the straight lines to the left and right of the current lane. Near-vision lane-line extraction includes lane image filtering, as well as edge detection of the region of interest below the vanishing line. The vanishing point is positioned by determining the position of the edge point and distribution of the direction angle. In addition, the straight line is extracted based on the position of the vanishing point. The straight lines that are constructed for the current lane in this way are selected and used as supplementation, in combination with the lane model. Next, the road curvature range isometry is divided into multiple subdivision regions. The near-vision lane straight-line curvature parameters extending from each edge point in the region of interest are computed by combining the straight-line near-vision lane information with the curve lane model in the pixel coordinate system. Subsequently, voting and counting are carried out for the curvature regions of each edge point to which the corresponding curvature computing values belong. Finally, the counting maximum from the corresponding curvature regions of the straight lines located to the left and right of the current lane are searched for, and the curvature region is converted, to obtain the lane line corresponding to the curvature parameter values. Experimental results indicate that the proposed curve-lane recognition algorithm can effectively detect the curve lanes of different curvatures. The results also indicate that the proposed curve detection method is highly accurate, and the algorithm is very robust in different environments.

Hyperoside, 3'-O-methylquercetin 3-O-β-D-galactopyranoside, astragalin and 3'-O-methylquercetin 3-O-β-D-glucopyranoside from an invasive weed Solanum rostratum Dunal were separated and purified successfully by high-speed counter-current chromatography (HSCCC) with a solvent system composed of n-hexane-ethyl acetate-methanol-water (1:7:1:7, v/v) and gradient elution mode preparative high-performance liquid chromatography (prep-HPLC) with low column temperature. In the sample pretreatment section, target compounds in aqueous extract of the weed were concentrated using solvent sublation. Two target fractions with purities of 93.75% and 93.68% were obtained from HSCCC. Their chemical structures were identified. The fraction 1 is a pure compound hyperoside and the fraction 2 is the mixture of astragalin, 3'-O-methylquercetin 3-O-β-D-galactopyranoside and 3'-O-methylquercetin 3-O-β-D-glucopyranoside by nuclear magnetic resonance and liquid chromatography-mass spectra. Then, the three flavonol glycosides in the fraction 2 were separated and purified successfully by prep-HPLC with low column temperature.