<![CDATA[Modeling the six-state Double Inverted Pendulum on Cart (DIPC) is highly challenging due to its strong nonlinearities and underactuated dynamics. To address this, the system model in this study is derived using a systematic forward-kinematics-based formulation from robotics theory, previously validated for accuracy in both LQR experiments and ANFIS simulations reported in earlier work. Building on this validated foundation, the present study proposes an Information Fusion Fuzzy Logic Controller (IF-FLC) to overcome the curse of dimensionality commonly encountered when designing fuzzy controllers for high-order systems. The method compresses the six measured state variables into two synthesized linguistic inputs—Synthesized Error (E) and Error Change (EC)—allowing the construction of an efficient 49-rule fuzzy controller without compromising essential system dynamics. Simulations incorporating encoder quantization and realistic measurement constraints show that the proposed IF-FLC provides stable balancing performance and improved robustness compared with the LQR benchmark. The results indicate that information-fusion-based fuzzy design is a promising approach for reducing controller complexity while maintaining high performance, offering a practical pathway for implementing intelligent control strategies on nonlinear and underactuated systems such as the DIPC.]]>
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