LPV时滞系统的准Min-Max鲁棒模型预测控制

对于输人有约束的LPV时滞系统，提出了准Min—Max鲁棒模型预测控制．将无限时域“最坏情况”的目标函数分解为当前性能指标和终端代价项，给出了新的鲁棒性能指标上界和系统稳定的充分条件，通过求解LMI的凸优化获得状态反馈控制律，而LMI的可行性保证了闭环系统的稳定性．最后通过truck—trailer系统仿真验证了所提方法的有效性．     

鲁棒极点约束的船舶减摇控制研究

针对存在随机海浪干扰、未建模动态和模型参数不确定性的船舶,兼顾控制器输出约束,提出采用LMI(Linear Matrix Inequality)区域进行闭环极点配置,考虑混合灵敏度S/R/T问题,设计了具有鲁棒极点约束的船舶减摇鳍H∞控制系统,对混合灵敏度S/R/T问题、权函数的选择等问题进行了研究,通过仿真验证了这一设计的可行性,所设计的船舶减摇鳍系统不但具有抗干扰性能,而且具有鲁棒稳定性.     

不确定系统的保性能可靠控制

针对不确定线性连续系统,研究了执行器失效情况下的保性能可靠控制问题.利用LMI给出了不确定线性系统对执行器故障具有渐近稳定性及可靠保性能的充分条件,进而讨论了参数不确定线性系统的保性能可靠控制器的设计问题.根据凸优化理论,给出了最优保性能可靠控制器的线性凸优化设计算法和设计步骤.采用所设计的可靠状态反馈控制器,当任意执行器出现故障时,闭环系统仍保持渐近稳定且保持原有的性能指标.     

一类输入时滞系统的鲁棒预测控制器的设计

针对一类不确定离散输入时滞系统,研究了使得闭环系统稳定,在预测时域内性能指标在线最小化的鲁棒预测控制器的设计问题.将预测时域后的性能指标函数用终端惩罚项近似,将无限时域性能指标函数转换成有限时域性能指标函数;基于滚动优化原理,将预测时域内的控制项用自由项代替,直接求取当前时刻的控制量,扩大可行解的范围,提高控制性能.通过求解线性矩阵不等式方法求解控制器.最后的仿真实例证明了该算法的有效性.     

Road curvature estimation for vehicle lane departure detection using a robust Takagi–Sugeno fuzzy observer

In this paper, a lane departure detection method is studied and evaluated via a professional vehicle dynamics software. Based on a robust fuzzy observer designed with unmeasurable premise variables with unknown inputs, the road curvature is estimated and compared with the vehicle trajectory curvature. The difference between the two curvatures is used by the proposed algorithm as the first driving risk indicator. To reduce false alarms and take into account the driver corrections, a second driving risk indicator is considered, which is based on the steering dynamics, and it gives the time to the lane keeping. The used nonlinear model deduced from the vehicle lateral dynamics and a vision system is represented by an uncertain Takagi–Sugeno fuzzy model. Taking into account the unmeasured variables, an unknown input fuzzy observer is then proposed. Synthesis conditions of the proposed fuzzy observer are formulated in terms of linear matrix inequalities using Lyapunov method. The proposed approach is evaluated under different driving scenarios using a software simulator. Simulation results show good efficiency of the proposed method.     

Robust control of anti-lock brake system

A robust control algorithm for an anti-lock brake system is proposed. The method used is based on static-state feedback of longitudinal slip and does not involve controller scheduling with changing vehicle speed or road adhesion coefficient estimation. An improvement involving scheduling of longitudinal slip reference with longitudinal acceleration measurement is included. Electromechanical braking actuators are used in simulations, and the algorithm used in this study is shown to have high performance on roads with constant and varying adhesion coefficients, displaying nice robustness properties against large vehicle speed and road adhesion coefficient variations. Guidelines are provided for tuning controller gains to cope with unknown actuator delay and measurement noise.     

用LMI方法设计不确定关联时滞大系统H∞容错控制器

针对关联项具有N×N个任意未知常时滞的不确定线性连续大系统,在执行器失效的情况下,提出了一种分散的鲁棒H∞容错控制器的设计方法,通过假设可能失效执行器的输出信号为能量有界的干扰输入信号,不确定关联时滞大系统的容错控制问题转化为分散的鲁棒H∞控制问题.控制器可以通过解线性矩阵不等式给出,因而具有数值易解性.最后用一个数值例子验证了该设计方法的有效性.     

Linear matrix inequality based control of vehicle active suspension system

Linear matrix inequality (LMI) methods, novel techniques in solving optimisation problems, were introduced as a unified approach for vehicle's active suspension system controller design. LMI methods were used to provide improved and computationally efficient controller design techniques. The active suspension problem was formulated as a standard convex optimisation problem involving LMI constraints that can be solved efficiently using recently developed interior point optimisation methods. An LMI based controller for a vehicle system was developed. The controller design process involved setting up an optimisation problem with matrix inequality constraints. These LMI constraints were derived for a vehicle suspension system. The resulting LMI controller was then tested on a quarter-car model using computer simulations. The LMI controller results were compared with an optimal PID controller design solution. The LMI controller was further tested by incorporating a nonlinear term in the vehicle's suspension model; the LMI's controller degraded response was enhanced by using gain-scheduling techniques. The LMI controller with gain-scheduling gave good results in spite of the unmodelled dynamics in the suspension system, which was triggered by large deflections due to off-road driving.     

Velocity control with disturbance observer for pedal-assisted electric bikes

This paper proposes a velocity control approach for light electric bicycles with human power assistance. A disturbance observer mechanism is used to estimate the sum of the human torque and resistance torques. The resulting vehicle velocity control provides better battery energy efficiency by knowledge of the instantaneous human torque assistance and better speed control by knowledge of the instantaneous resistive torque. The disturbance observer is tuned in terms of the DC gain of a low-passed Q-filter for both open-loop and closed-loop schemes. Assuming that the slow varying nature of the disturbance has been properly estimated and compensated, the torque control law is designed via an optimal control approach to achieve multi-objective performances regarding the external disturbance input, control signal magnitude, and velocity tracking error. The three main parameters of the electric bike, including the moment of inertia, the radius of tyre and the vehicle weight are allowed to be variational. Specifically, the deviation of the inertia moment and deviation of the tyre radius are addressed during the controller design in terms of linear matrix inequalities. On the other hand, the effect of vehicle weight deviation on the system behaviour is evaluated when the vehicle is implemented with the constructed control law. Based on the parameters and specifications of the EL-168 electric bike produced by KENTFA Advanced Technology, Taiwan, the design results are verified through time–response simulations.