In the previous chapters, various matrix-weighted consensus algorithms were proposed for multiagent systems operating in ideal environments. This chapter aims to enhance the applicability of these algorithms by incorporating uncertain factors, such as disturbances and unknown parameters, into the control design. Several control approaches are presented in this chapter to mitigate uncertainties and restore the performance of the multiagent system to an acceptable or even uncertainty-free state. In this way, the system becomes robust against uncertainties, which are often encountered in harsh environments. There are numerous robust control methods in literature, and many studies have focused on integrating them into scalar-consensus algorithms. However, it is impractical to consider all these methods for matrix-weighted consensus in a single chapter. Instead, this chapter focuses on two categories of uncertainties: a linear combination of known time-varying functions with unknown constant coefficients and unknown time-varying disturbances with a priori known upper bounds. For the first disturbance category, adaptive backstepping and disturbance-observer-based control schemes are presented. For the other category, sliding-mode control and its variations are selected as the design approaches. The behavior of the matrix-weighted consensus network under the proposed algorithms is thoroughly analyzed using the Lyapunov method.