Name
Title | ||
|---|---|---|
Approximate optimal hybrid control synthesis by classification-based derivative-free optimization |
Abstract | ||
|---|---|---|
ABSTRACTHybrid systems are widely used in safety-critical areas. Hybrid optimal control synthesis, which aims to generate an optimal sequence of control inputs for a given task, is one of the most important problems in the field. The classical Gradient-based methods are efficient but they require the system under control should be differentiable. Sampling-based methods have no such limitations, but the ability of existing ones to solve complex control missions is restricted. In this paper, we propose a practical and efficient method to solve a general class of hybrid optimal control problems. Basically, we transform the control synthesis problem into a derivative-free optimization (DFO) problem. Then, we adapt a start-of-art classification-based DFO method to solve the optimization problems based on sampled variables efficiently. Furthermore, for complex state space, which is difficult to solve, we present a piecewise control synthesis method to make a tradeoff between optimality and efficiency by generating feasible and piecewise optimal control inputs instead. The empirical results on two complex real-world hybrid systems: a vehicle and a quadcopter drone system, demonstrate that our method outperforms existing methods significantly. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3447928.3456658 | Cyber-physical Systems |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaopeng Xing | 1 | 0 | 1.69 |
| Jiawan Wang | 2 | 0 | 1.69 |
| Lei Bu | 3 | 189 | 22.50 |
| Xin Chen | 4 | 2 | 2.40 |
| Li Xuandong | 5 | 672 | 79.78 |