Critic Learning-Based Safe Optimal Control for Nonlinear Systems with Asymmetric Input Constraints and Unmatched Disturbances.

In this paper, the safe optimal control method for continuous-time (CT) nonlinear safety-critical systems with asymmetric input constraints and unmatched disturbances based on the adaptive dynamic programming (ADP) is investigated. Initially, a new non-quadratic form function is implemented to effectively handle the asymmetric input constraints. Subsequently, the safe optimal control problem is transformed into a two-player zero-sum game (ZSG) problem to suppress the influence of unmatched disturbances, and a new Hamilton-Jacobi-Isaacs (HJI) equation is introduced by integrating the control barrier function (CBF) with the cost function to penalize unsafe behavior. Moreover, a damping factor is embedded in the CBF to balance safety and optimality. To obtain a safe optimal controller, only one critic neural network (CNN) is utilized to tackle the complex HJI equation, leading to a decreased computational load in contrast to the utilization of the conventional actor-critic network. Then, the system state and the parameters of the CNN are uniformly ultimately bounded (UUB) through the application of the Lyapunov stability method. Lastly, two examples are presented to confirm the efficacy of the presented approach.

On the existence of Equivalent-Input-Disturbance and multiple integral augmentation via H-Infinity Synthesis for unmatched systems.

In this paper, the existence of a solution for the transformation of the disturbances from the unmatched cases to the matched one is investigated. The usage of matched/unmatched disturbance notions and the underlying assumptions are clarified. Then, a simplified definition is introduced to obtain a set of performance metrics to be used in observer design. Using bilinear pole shifting and multiple integral augmentation to the plant, not only the stabilizability/detectability conditions but also infinity-norm bounds for unstable MIMO systems are derived. Then, the solvability of the augmented Hamiltonian matrices to get stabilizing solutions via standard H∞-Synthesis is explained. Finally, the solutions, definitions, and assumptions are validated through numerical examples.