Abstract In this paper, the control objective is driving the output of a discrete-time switched constrained system with uncertainty to track a desired output of reference by an optimal manner. A new augmented switched system with discounted cost function is constructed based on the switched and reference dynamics, which converts the complex tracking problem to a stabilizing robust control optimization problem. Combining the two stage optimization and iteration learning technique, the overall optimal hybrid policy is first achieved for the constrained switched tracking control. Instead of the general critic-actor structure, only critic neural network (NN) is applied in the algorithm to simply the architecture and manner of implementation. As the main computational burden or load in iteration learning process comes from the information transmissions of tuning NNs, the designed critic-only structure can reduce computational load with less transmissions. Then the convergence of the iteration learning process is demonstrated by theorems and the tracking objective is achieved as the output tracking errors get converged to zero. Finally, the proposed robust tracking control scheme for constrained-input switched systems is applied in the simulation, and the tracking results proved the effectiveness and applicability of the designed method.