This paper is concerned with the problem of actuator fault estimation (FE) for vehicle active suspension systems. First, the fast FE approach, which combines the output error term with its derivative term in the FE algorithm, is extended to the active suspension system with actuator fault and external disturbance input. Then, considering three typical kinds of actuator faults, i.e., constant gain change fault, drift fault, and stuck fault, genetic algorithm (GA) is employed to optimize the adjustable parameters in the FE algorithm, which are usually determined by trials. Finally, simulation results of FE and fault-tolerant control (FTC) are presented to illustrate the effectiveness and applicability of the proposed FE method.
The actuator fault estimation (FE) problem is addressed in this study for the quarter-car active suspension system (ASS) with consideration of the sprung mass variation. Firstly, the ASS is modeled as a parameter-dependent system with actuator fault and external disturbance input. Then, a parameter-dependent FE observer is designed by using the radial basis function neural network (RBFNN) to approximate the actuator fault. In addition, the design conditions are turned into a linear matrix inequality (LMI) problem which can be easily solved with the aid of LMI toolbox. Finally, simulation and comparison results are given to show the accuracy and rapidity of the proposed FE method, as well as good adaptability against the sprung mass variation. Moreover, a simple FE-based active fault-tolerant control (AFTC) strategy is provided to further demonstrate the effectiveness and applicability of the proposed FE method.
This paper studied the optimal formation control of quadrotor UAV based on the dynamic model, and the collision avoidance between quadrotors is considered. By constructing the problem into a standard convex quadratic programming problem, we hope to improve the solving efficiency of the formation control problem. Firstly, the nonlinear dynamic model of quadrotor is linearized and the prediction model is established. Then, the safety zone constraints are transformed from a circular zone to a half-plane zone, making the optimization problem be a standard convex quadratic programming problem. Finally, the quadratic programming problem is solved using distributed receding horizon optimization. Numerical simulations in three-dimensional space show that this method can obtain the optimal formation trajectory with collision avoidance, and can improve the solving efficiency.
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