Analyzing the performance characteristics and applicable environments of intelligent vehicle lateral control algorithms, this paper establishes the Model Predictive Control (MPC), Pure Pursuit (PP), and linear Quadrat...
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Analyzing the performance characteristics and applicable environments of intelligent vehicle lateral control algorithms, this paper establishes the Model Predictive Control (MPC), Pure Pursuit (PP), and linearquadraticregulator (LQR) algorithms and uses 2022b MATLAB software to simulate the lateral error, heading error, and algorithm execution time at speeds of 3 m/s, 7 m/s, and 10 m/s. Urban low-speed scenarios (3 m/s) require high-precision control (such as obstacle avoidance), while high-speed scenarios (10 m/s) require strong stability. Existing research mostly focuses on a single speed and lacks a quantitative comparison across multiple operating conditions. Although MPC has high accuracy, its time consumption fluctuates greatly. LQR has strong real-time performance but a wide range of heading errors. PP has poor low-speed performance but controllable high-speed time consumption growth. It is necessary to define the applicable scenarios of each algorithm through quantitative data. In response to the lack of multi-speed domain quantitative comparison in existing research, this paper conducts multi-condition simulations using MPC, PP, and LQR algorithms and finds that at a low speed of 3 m/s, the peak lateral error of PP (0.45 m) is 55% and 156% higher than MPC (0.29 m) and LQR (0.176 m), respectively. At a speed of 10 m/s, the lateral error standard deviation of MPC (0.08 m) is reduced by 68% compared to PP (0.25 m). In terms of algorithm time consumption, LQR maintains full-speed domain stability (0.11-0.44 ms), while PP time increases by 95% with speed from 3 m/s to 10 m/s. The results show that in terms of lateral error, the MPC and LQR algorithms perform more stably, while the PP algorithm has a larger error at low speeds. Regarding heading error, all algorithms have a relatively large error range, but the MPC and LQR algorithms perform slightly better than the PP algorithm at high speeds. In terms of algorithm execution time, the LQR algorithm has the sho
Variable stiffness composite laminates(VSCLs)are promising in aerospace engineering due to their designable material properties through changing fiber angles and stacking *** to control the thermal postbuckling and no...
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Variable stiffness composite laminates(VSCLs)are promising in aerospace engineering due to their designable material properties through changing fiber angles and stacking *** to control the thermal postbuckling and nonlinear panel flutter motions of VSCLs,a full-order numerical model is developed based on the linearquadraticregulator(LQR)algorithm in control theory,the classical laminate plate theory(CLPT)considering von Kármán geometrical nonlinearity,and the first-order Piston *** critical buckling temperature and the critical aerodynamic pressure of VSCLs are parametrically *** location and shape of piezoelectric actuators for optimal control of the dynamic responses of VSCLs are determined through comparing the norms of feedback control gain(NFCG).Numerical simulations show that the temperature field has a great effect on aeroelastic tailoring of VSCLs;the curvilinear fiber path of VSCLs can significantly affect the optimal location and shape of piezoelectric actuator for flutter suppression;the unstable panel flutter and the thermal postbuckling deflection can be suppressed effectively through optimal design of piezoelectric patches.
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