Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
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发表时间：
2021-09
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通讯作者：
Charles Dawson;Zengyi Qin;Sicun Gao;Chuchu Fan
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作者：
Charles Dawson;Zengyi Qin;Sicun Gao;Chuchu Fan
安全性和稳定性是机器人控制系统的共同要求，然而对于非线性和不确定模型，设计安全、稳定的控制器仍然是困难的。我们发展了一种基于模型的学习方法来综合具有安全性和稳定性保证的鲁棒反馈控制器。我们从鲁棒凸优化和Lyapunov理论中得到启发，定义了鲁棒控制Lyapunov障碍函数，该障碍函数在模型不确定的情况下具有泛化能力。在汽车轨迹跟踪、避障非线性控制、带安全约束的卫星交会和带学习地面效应模型的飞行控制等问题上，我们展示了我们的方法。仿真结果表明，我们的方法所产生的控制器达到或超过了稳健预测控制的能力，同时将计算成本降低了一个数量级。
Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models. We develop a model-based learning approach to synthesize robust feedback controllers with safety and stability guarantees. We take inspiration from robust convex optimization and Lyapunov theory to define robust control Lyapunov barrier functions that generalize despite model uncertainty. We demonstrate our approach in simulation on problems including car trajectory tracking, nonlinear control with obstacle avoidance, satellite rendezvous with safety constraints, and flight control with a learned ground effect model. Simulation results show that our approach yields controllers that match or exceed the capabilities of robust MPC while reducing computational costs by an order of magnitude.