This paper presents a preliminary study on the advantages of two bio-inspired homeostatic mechanisms in neural controllers of legged robots. We consider a robot made up of one leg of 3 dof pushing a body that is sliding on a rail with a friction force. The synthesis of the controller is done by an evolutionary algorithm which choose to attach to each synapse a particular plastic law. Four models of network incorporating or not each homeostatic law are proposed. After evolution, effectiveness of each kind of adaptive controllers is compared in term of statistics on a task of controlling the speed of the robot. The robustness to a perturbation generated by the viscous friction is analyzed in term of control. Results show that homeostatic mechanisms increase evolvability, stability and adaptivity of those controllers.