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Open and closed-loop control systems01:17

Open and closed-loop control systems

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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Reinforcement Learning-Based Fuzzy Control for Nonlinear Systems With Unknown Dynamics via Parallel Composite Policy

Yiqun Liu, Lifei Dai, Changzhu Zhang

    IEEE Transactions on Cybernetics
    |February 25, 2026
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    Summary
    This summary is machine-generated.

    This study introduces a novel Parallel Composite Policy Iteration (PCPI) algorithm for reinforcement learning (RL)-based fuzzy control in nonlinear systems. The PCPI algorithm overcomes limitations of traditional methods, enabling efficient control even with unknown system dynamics.

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    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Fuzzy Logic Systems

    Background:

    • Reinforcement learning (RL) and fuzzy control are crucial for nonlinear systems.
    • Traditional policy iteration (PI) and value iteration (VI) methods face challenges like initial stabilizing policies and persistent excitation (PE) conditions.
    • Solving fuzzy algebraic Riccati equations (FARE) for complex nonlinear systems is difficult with conventional approaches.

    Purpose of the Study:

    • To develop a novel Parallel Composite Policy Iteration (PCPI) algorithm for RL-based fuzzy control.
    • To address limitations of existing PI/VI algorithms, including the need for initial stabilizing control policies and PE conditions.
    • To solve the complex fuzzy algebraic Riccati equation (FARE) in nonlinear systems with unknown dynamics.

    Main Methods:

    • A novel PCPI algorithm is proposed, incorporating adaptive parameters to remove the need for an initial stabilizing control policy.
    • An online, model-free PCPI variant is introduced for systems with difficult-to-obtain dynamic information.
    • The PE condition is relaxed to an initial excitation (IE) condition by utilizing online data, and algorithms run concurrently per fuzzy rule.

    Main Results:

    • The proposed PCPI algorithm effectively alleviates drawbacks of traditional RL-based fuzzy control methods.
    • The adaptive parameters eliminate the requirement for an initial stabilizing control policy.
    • The online, model-free PCPI relaxes the PE condition to IE, enhancing applicability to systems with unknown dynamics.

    Conclusions:

    • The developed PCPI algorithm offers an effective solution for RL-based fuzzy control of nonlinear systems with unknown dynamics.
    • The algorithm's ability to relax excitation conditions and operate model-free enhances its practical applicability.
    • Experimental validation on a robot arm and active suspension system confirms the algorithm's effectiveness.