基于蛇算法的主动悬架线性二次型调节器优化设计

针对车辆主动悬架系统的线性二次型调节器(LQR控制器)在设定权重系数矩阵Q和R时具有主观性、效率低的缺点，提出一种基于蛇算法(SO)优化LQR控制器权重系数矩阵的策略。通过对1/4车辆主动悬架系统的动力学分析，设计了LQR控制器；将主动悬架与被动悬架各性能指标的积分比值进行加权求和构建了目标函数L；模仿蛇群生活习性的SO算法在搜索空间中求解出了函数L的最小值和LQR控制器的最优权重系数矩阵。为验证该策略的有效性，分别以C级路面、正弦冲击路面为激励，在车身加速度(SMA)、轮胎动载荷(DTL)、悬架动行程(SWS)三方面将SO优化LQR控制的主动悬架与被动悬架、传统LQR控制的主动悬架、遗传算法(GA)优化LQR控制的主动悬架、粒子群算法(PSO)优化LQR控制的主动悬架进行了仿真对比。结果表明:SO优化LQR控制的主动悬架可在C级路面上分别对SMA、DTL、SWS的均方根优化达59.47%、37.89%、42.12%；在正弦冲击路面上稳定时间为1.4s，分别对SMA、DTL、SWS的超调优化达79.21%、59.22%、16.33%，提升了车辆的行驶平顺性、路面附着性和操作安全性。

Optimized Design of Active Suspension LQR Controller based on Snake Optimizer

Aiming at the shortcomings of subjectivity and low efficiency in setting the weight coefficient matrices Q and R of Linear Quadratic Regulator (LQR controller) for vehicle active suspension systems, a strategy based on Snake Optimizer (SO) to optimize the weight coefficient matrices of LQR controller is proposed. The LQR controller was designed by analyzing the dynamics of the 1/4 vehicle active suspension system. The objective function L was constructed by weighting the integral ratios of each performance index of active suspension and passive suspension. SO solved the minimum value of the function L and the best weight coefficient matrices of the LQR controller in the search space by imitating the living habits of the snake group. In order to verify the effectiveness of this strategy, SO optimized LQR-controlled active suspension was simulated and compared with passive suspension, traditional LQR-controlled active suspension, Genetic Algorithm optimized LQR-controlled active suspension and Particle Swarm Algorithm optimized LQR-controlled active suspension in terms of Sprung Mass Acceleration (SMA), Dynamic Tire Load(DTL) and Suspension Working Space(SWS). The results show that the active suspension controlled by SO-optimized LQR can optimize the rms of SMA, DTL and SWS by 59.45%, 37.89% and 42.11% on the C-class road, respectively; the stability time on sinusoidal impact road is 1.4s, and the overshoot optimization of SMA, DTL and SWS is 79.22%, 59.22% and 16.33%, respectively, which improves the driving smoothness, road adhesion and operational safety of vehicles.