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This paper studies the adaptive and optimal output-feedback problem for continuous-time uncertain systems with nonlinear dynamic uncertainties. Data-driven output-feedback control policies are developed by approximate/adaptive dynamic programming (ADP) based on both policy iteration and value iteration methods. The obtained adaptive and optimal outputfeedback controllers differ from the existing literature on the ADP in that they are derived from sampled-data systems theory and are guaranteed to be robust to dynamic uncertainties. A small-gain condition is given under which the overall system is globally asymptotically stable at the origin. An application to power systems is given to test the effectiveness of the proposed approaches.

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Natural biomaterials, such as collagen, gelatin, and chitosan, are considered as promising candidates for use in tissue regeneration treatment, given their similarity to natural tissues regarding components and structure. Nevertheless, only receiving a crosslinking process can these biomaterials exhibit sufficient strength to bear high tensile loads for use in skeletal system regeneration. Recently, genipin, a natural chemical compound extracted from gardenia fruits, has shown great potential as a reliable crosslinking reagent, which can reconcile the crosslinking effect and biosafety profile simultaneously. In this review, we briefly summarize the genipin extraction process, biosafety, and crosslinking mechanism. Subsequently, the applications of genipin regarding aiding skeletal system regeneration are discussed in detail, including the advances and technological strategies for reconstructing cartilage, bone, intervertebral disc, tendon, and skeletal muscle tissues. Finally, based on the specific pharmacological functions of genipin, its potential applications, such as its use in bioprinting and serving as an antioxidant and anti-tumor agent, and the challenges of genipin in the clinical applications in skeletal system regeneration are also presented.

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

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