Learning-Based Nonlinear $H^\infty$ Control via Game-Theoretic Differential Dynamic Programming
In this work, we present a learning-based nonlinear $H^\infty$ control algorithm that guarantee system performance under learned dynamics and disturbance estimate. The Gaussian Process (GP) regression is utilized to update the nominal dynamics of the system and provide disturbance estimate based on data gathered through interaction with the system. A soft-constrained differential game associated with the disturbance attenuation problem in nonlinear $H^\infty$ control is then formulated to obtain the nonlinear $H^\infty$ controller. The differential game is solved through the min-max Game-Theoretic Differential Dynamic Programming (GT-DDP) algorithm in continuous time. Simulation results on a quadcopter system demonstrate the efficiency of the learning-based control algorithm in handling external disturbances.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Controller Reduction for Nonlinear Systems by Generalized Differential Balancing
In this paper, we aim at developing computationally tractable methods for nonlinear model/controller reduction. Recently, model reduction by generalized differential (GD) balancing has been proposed for nonlinear systems…
Nonlinear receding-horizon differential game for drone racing along a three-dimensional path
Drone racing involves high-speed navigation of three-dimensional paths, posing a substantial challenge in control engineering. This study presents a game-theoretic control framework, the nonlinear receding-horizon differ…
Model Predictive ControlRobust Policy Optimization in Continuous-time Mixed $\mathcal{H}_2/\mathcal{H}_\infty$ Stochastic Control
Following the recent resurgence in establishing linear control theoretic benchmarks for reinforcement leaning (RL)-based policy optimization (PO) for complex dynamical systems with continuous state and action spaces, an …
reinforcement-learningReinforcement Learning (RL)Off-policy reinforcement learning for $ H_\infty $ control design
The $H_\infty$ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear $ H_\infty $ control problem can be transformed into solving the so-called Hami…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Equilibrium Selection in Replicator Equations Using Adaptive-Gain Control
In this paper, we deal with the equilibrium selection problem, which amounts to steering a population of individuals engaged in strategic game-theoretic interactions to a desired collective behavior. In the literature, t…