In order to solve the problems of faults and disturbances in linear switched systems, a new fault observer design method is proposed by combining robust observer design with the zonotpoes technology. First, the system fault is regarded as an augmented state, and an augmented system equivalent to the original system is constructed. With the switching signal satisfying the constraint of mean dwell time, the multi Lyapunov function method is used to make the switching system globally exponentially stable and meet the weighted $H_{\infty}$ performance index. Then, the zonotpoes technique is introduced into the interval estimation method, and the state estimation error at each time is recursively obtained by using the center and boundary generation matrix, so that the fault interval can be more accurately shrunk. Finally, a numerical example is given to demonstrate the proposed theoretical result.
This paper studies cooperative trajectory planning for multiple connected and automated vehicles (CAVs) traveling from and off multi-lane roads at a signal-free intersection with the consideration of velocity-prioritized emergency vehicles, such as ambulances and fire engines. In most of the current studies, the trajectory of each CAV is either fixed or in line with the topology of the fleet, whilst the velocity of each CAV is preset with an inflexible pattern, which severely limits the efficiency of the collaboration. Moreover, the priority of emergency vehicles is not well considered in the aforementioned strategies. This research is aimed to address the issues. The intersection control task is formulated as an optimal control problem (OCP), in which the velocities of the CAVs can be set with any reasonable aspiration, while there is no lane discipline at the intersection. When designing the control strategy, unmanned vehicles of various sizes with differentiated velocity priorities are taken into account. An adaptive stepwise optimization (ASO) method is proposed to improve the OCP solution efficiency.
For Hammerstein system with fractional linear state space model, it is very difficult to build its model by identification method. The difficulty lies in that the nonlinear state space equation model describing the system contains unknown model parameters, fractional order of the system and outside noise. In order to overcome these difficulties, we transform the fractional system from an input nonlinear pseudo-state space model to an input-output regression model, and estimate the model parameters with multi-innovation recursive gradient descent algorithm, and confirm the fractional order with the multi-innovation Levenberg-Marquardt algorithm. Finally, the effectiveness of the algorithm is verified by a numerical example.
