Tensegrity is a structural form that is defined as a set of rigid elements suspended in a net of continuous tension. This structure shows potential for compliance, impact tolerance and mechanical robustness. However, its non-linear coupled dynamics and often complex geometry require advanced control strategies. An actuator reference planning strategy to bring tensegrity robots closer to controlled full body movements was proposed by Guido Tournois \cite{GuidoThesis} in 2017. This strategy, called the Full Body Reference Planner (FBRP), finds a sequence of equilibrium configurations for a tensegrity structure, predominantly to follow a trajectory in space. However, the method is incapable of incorporating inequality constraints while obtaining said equilibrium configurations. This is a problem when dealing with certain restrictions, e.g., actuator limitations and stability of the structure.

In this thesis we implemented a robust way to account for inequality constraints while utilizing the FBRP. That was done by means of optimization, i.e., an implementation of a Sequential Quadratic Programming method to ensure inequality constraints were respected for each configuration. The approach was validated in scenarios related to practical applications where inequality constraints were enforced. The results showed advancements towards practical feasibility. Furthermore, the robustness, efficiency and accuracy of the method were validated. The extended implementation depicted robustness to parameter variations and good results in terms of accuracy. However, given the iterative nature of the method, it was more computationally expensive than its precursor.