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This paper introduces our ongoing work consisting of evolving bio-inspired plastic neural controllers for autonomous robots submitted to various internal and external perturbations: transmission breaking, slippage, leg loss, etc. We propose a classical neuronal model using adaptive synapses and extended with two bio-inspired homeostatic mechanisms. We perform a comparative study of the impact of the two homeostatic mechanisms on the evolvability of a neural network controlling a single-legged robot that slides on a rail and that is confronted to an external perturbation. The robot has to achieve a required speed goal given by an operator. Evolved neural controllers are tested on long-term simulations to statistically analyse their stability and adaptivity to the perturbation. Finally, we perform behavioral tests to verify our results on the robot controlled with a sinusoidal input while a perturbation occurs. Results show that homeostatic mechanisms increase evolvability, stability and adaptivity of those controllers.