Fractional Calculus in Optimal Control and Game Theory: Theory, Numerics, and Applications -- A Survey
Many physical, biological, and engineered systems exhibit memory effects that challenge Markovian models. Fractional calculus provides nonlocal operators to capture hereditary dynamics. This survey connects modeling, analysis, and controller/game design for systems with memory. We unify notation for Caputo, Riemann-Liouville, and Grunwald-Letnikov derivatives and relate them to practical approximations, including diffusive (sum-of-exponentials) state augmentation and frequency-domain realizations (e.g., Oustaloup). We review fractional extensions of the calculus of variations and the Pontryagin maximum principle, and dynamic-programming formulations with memory, including path-dependent HJB for optimal control and HJI for zero-sum games. We cover design tools such as LQR, MPC, and fractional-order PID, as well as fractional differential games with Nash, Stackelberg, and minimax equilibria. Computational approaches are compared across time-domain schemes, frequency-domain approximations, and diffusive augmentations, highlighting accuracy-complexity trade-offs and remedies for the curse of history (windowing and sum-of-exponentials). We conclude with applications and open problems on equilibria with memory, Isaacs-type conditions, constraint handling, and scalable solvers.