This thesis aims at exploring the application of control systems theory to the problem of portfolio optimization for retail investors with limited capital. The financial market is modeled as a dynamic system with feedback, uncertainty, and time-varying parameters. Techniques from modern control theory—such as state estimation, adaptive control, and feedback stabilization—are employed to design portfolios that maintain stability and robustness under market volatility. The study emphasizes strategies like dynamic leverage, volatility targeting, and risk parity as control problems rather than static optimizations. By interpreting portfolio rebalancing as a feedback law, the research aims to minimize drawdowns while maintaining target performance. Simulations and backtesting will be conducted on equity–bond balanced portfolios to validate the proposed framework. The goal is verify whether control-theoretic methods can enhance resilience and adaptivity in retail portfolio management.