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The proliferation of Smart Cyber-Physical Systems (SCPS) is increasingly blurring the boundaries between physical and virtual entities. This trend is revolutionizing multiple application domains along the whole human activity spectrum, while pushing the growth of new businesses and innovations such as smart manufacturing, cities and transportation systems, as well as personalized healthcare. Technological advances in the Internet of Things, Big Data, Cloud Computing and Artificial Intelligence have effected tremendous progress toward the autonomic control of SCPS operations. However, the inherently dynamic nature of physical environments challenges SCPS’ ability to perform adequate control actions over managed physical assets in myriad of contexts. From a design perspective, this issue is related to the system states of operation that cannot be predicted entirely at design time, and the consequential need to define adequate capabilities for run-time self-adaptation and self-evolution. Nevertheless, adaptation and evolution actions must be assessed before realizing them in the managed system in order to ensure resiliency while minimizing the risks. Therefore, the design of SCPS must address not only dependable autonomy but also operational resiliency. In light of this, the contribution of this paper is threefold. First, we propose a reference architecture for designing dependable and resilient SCPS that integrates concepts from the research areas of Digital Twin, Adaptive Control and Autonomic Computing. Second, we propose a model identification mechanism for guiding self-evolution, based on continuous experimentation, evolutionary optimization and dynamic simulation, as the architecture’s first major component for dependable autonomy. Third, we propose an adjustment mechanism for self-adaptation, based on gradient descent, as the architecture’s second major component, addressing operational resiliency. Our contributions aim to further advance the research of reliable self-adaptation and self-evolution mechanisms and their inclusion in the design of SCPS. Finally, we evaluate our contributions by implementing prototypes and showing their viability using real data from a case study in the domain of intelligent transportation systems.