This article focuses on the design and validation of a new robust control framework referred to as “twin-observers-based control (TOBC)”, which is capable of suppressing the influence of disturbances or uncertainties on the path-tracking performance of intelligent vehicles. The TOBC technique is proposed and investigated by introducing the concept of an imaginary physical plant and equivalently treating the controller as a special unknown input observer (UIO). Unlike the existing disturbance-observer-based control (DOBC) (also known as UIO-based control) with a two-stage design procedure, the proposed TOBC method only requires the design of one UIO. In the TOBC framework, two identical UIOs are used to estimate the total disturbance and generate control commands, respectively. Firstly, the issue of path-tracking control is transformed into the problem of yaw rate tracking. Next, a nonlinear UIO capable of estimating first-order disturbances is developed. Based on this approach, twin UIOs with different objectives are combined to achieve path-tracking control of intelligent vehicles in the presence of disturbances or uncertainties. Exponential stability of the UIO is ensured by appropriately selecting design parameters. Furthermore, the stability of the closed-loop system under the composite controller, consisting of twin UIOs, is established. The effectiveness and superiority of the proposed control technique for path-tracking tasks are demonstrated through comprehensive simulation studies, comparative analyses, and experimental verifications.