Verification and validation (V&V) are essential stages in the design cycle of industrial controllers to remove the gap between the designed and implemented controller. In this study, a model-based adaptive methodology is proposed to enable easily verifiable controller design based on the formulation of a sliding mode controller (SMC). The proposed adaptive SMC improves the controller robustness against major implementation imprecisions including sampling and quantization. The application of the proposed technique is demonstrated on the engine cold start emission control problem in a mid-size passenger car. The cold start controller is first designed in a single-input single-output (SISO) structure with three separate sliding surfaces, and then is redesigned based on a multiinput multi-output (MIMO) SMC design technique using nonlinear balanced realization. The MIMO controller prioritizes the states of the model based on their energy and put more efforts to track trajectories with higher level of energy. The controller behavior is improved using the MIMO structure with fewer tunable parameters. The performance of the MIMO adaptive controller is validated in real-time by testing the control algorithm in a processor-in-the-loop (PIL) platform. The validation results indicate the proposed adaptive SMC is 20-40% more robust to sampling and quantization imprecisions compared to the baseline SMC. The proposed methodology from this paper is expected to reduce typical V&V efforts in the development of automotive controllers.