Nowadays it is clear that multi-robot systems offer several advantages that are very difficult to reach with single systems. However, to leave the simulators and the academic environment it is a mandatory condition that they must fill: these systems must be economically attractive to increment their implantation in realistic scenarios. Due to multirobots systems are composed of several robots that generally are similar, if an economic optimisation is done in one of them, such optimisation can be replicated in each member of the team. In this paper we show a work to implement low level controllers with small computational needs that can be used in each of the subsystems that must be controlled in each of the robots that belongs to a multi-robot system. If a robot is in a multi-robot system that robot needs bigger computational capacity, because it has to do some tasks derived from being in the team, for example, coordination and communication with the remaining members of the team. Besides, occasionally, it has to deduce cooperatively the global strategy of the team. One of the theoretical advantage of multi-robot systems is that the cost of the team must be lower than the cost of a single robot with the same capabilities. To become this idea true it is mandatory that the cost of each member was under a certain value, and we can get this if each of them is equipped with very cheap computational systems. One of the cheapest and more flexible devices for control systems implementation are Field Programmable Gate Arrays (FPGAs). If we could implement a control loop using a very simple FPGA structure, the economic cost of each of them could be about 10 dollars. On the other hand, and under a pessimistic vision, the subsystems to control could have problems to be controlled using classic and well known control schemas as PID controllers. In this situation we can use other advanced control systems which try to emulate the human brain, as Predictive Control. This kind of control works using a world model and calculating some predictions about the response that it will show under some stimulus, and it obtains the better way of control the subsystem knowing which is the desired behavior from this moment until a certain instant later. The predictive controller tuning is a process that is done using analytical and manual methods. Such tuning process is expensive in computational terms, but it is done one time and in this paper we don’t deal with this problem. However, in spite of the great advantage of predictive control, which contributes to control systems that the classic control is unable to do, it has a great drawback: it is very computationally expensive while it is working. In section 4 we will revise the cause of this