  In the swift evolution of Cyber-Physical Systems (CPSs) within intelligent
environments, especially in the industrial domain shaped by Industry 4.0, the
surge in development brings forth unprecedented security challenges. This paper
explores the intricate security issues of Industrial CPSs (ICPSs), with a
specific focus on the unique threats presented by intelligent attackers capable
of directly compromising the controller, thereby posing a direct risk to
physical security. Within the framework of hierarchical control and incentive
feedback Stackelberg game, we design a resilient leading controller (leader)
that is adaptive to a compromised following controller (follower) such that the
compromised follower acts cooperatively with the leader, aligning its
strategies with the leader's objective to achieve a team-optimal solution.
First, we provide sufficient conditions for the existence of an incentive
Stackelberg solution when system dynamics are known. Then, we propose a
Q-learning-based Approximate Dynamic Programming (ADP) approach, and
corresponding algorithms for the online resolution of the incentive Stackelberg
solution without requiring prior knowledge of system dynamics. Last but not
least, we prove the convergence of our approach to the optimum.
