Nowadays, production control problems has been widely studied and a lot of valuable approaches have been implemented. This paper addresses the problem of tracking the uncertain demand in case of uncertain production speeds. The uncertainties are described by deterministic inequalities and the performance is analyzed in from of the worst-case scenario. First, simple mathematical models are introduced and the control problem is formulated. In continuous-time, the cumulative output of a manufacturing machine is the integral of the production speed over time. At the same time, the production speed is bounded from below and above, and hence the manufacturing process can be modeled as an integrator with saturated input. Since the cumulative demand (which is the reference signal to track) is a growing function of time, it is natural to consider control policies that involve integration of the mismatch between the current output and current demand. In the simplest consideration it results in models similar to a double integrator closed by saturated linear feedback with an extra input that models disturbances of a different nature. This model is analyzed and particular attention is devoted to the integrator windup phenomenon: lack of global stability of the system solutions that correspond to the same input signal. As a systematic design procedure to prevent the windup phenomenon we present observer based techniques with full and reduced-order observers. The next part of the paper deals with a similar control problem in discrete-time under the surplus-based policy: each machine in the production network tracks the demand trying to keep the downstream buffer at some specified safe level. The performance of manufacturing networks with different topologies is analyzed via the second Lyapunov method, while the disturbances are modeled by deterministic inequalities. The nature of the approach leads to performance analysis in the form of a worst case scenario and allows to find a tradeoff between the inventory for each machine in the system and the demand tracking accuracy. The final part of the paper illustrates how to make the theoretical findings operational with the experimental setup called Liquitrol. The experimental setup consists of a number of water tanks and pumps that can be interconnected via flexible piping. Due to its flexibility and mobility, the setup allows not only to verify theoretical results via experiments, but it also can be used in an educational process to illustrate different phenomena in tandem and re-entrant manufacturing networks.