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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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    This study introduces inverse reinforcement learning (IRL) algorithms to enable control systems to track target systems effectively, even with data loss during wireless transmission. The methods allow systems to learn unknown target behaviors for improved tracking performance.

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    Area of Science:

    • Control Systems Engineering
    • Machine Learning
    • Wireless Communication

    Background:

    • Networked control systems (NCS) face challenges with data loss during wireless transmission.
    • Tracking control requires understanding an unknown target system's behavior, often defined by an unknown cost function.

    Purpose of the Study:

    • To develop inverse reinforcement learning (IRL) algorithms for tracking control of linear NCS with random state dropouts.
    • To enable a controlled system to infer the unknown cost function and optimal policy of a target system.

    Main Methods:

    • A model-based IRL algorithm integrating a Smith predictor for state estimation was developed.
    • A state-dropout-aware inverse Q-learning algorithm was proposed, requiring only accessible system data.

    Main Results:

    • The proposed algorithms effectively infer the target's cost function and optimal control policy.
    • Theoretical validity was rigorously established.
    • Numerical simulations confirmed the practical effectiveness of the algorithms.

    Conclusions:

    • The developed IRL algorithms provide a robust solution for tracking control in NCS with random state dropouts.
    • These methods enhance tracking performance by enabling systems to learn and adapt to unknown target dynamics despite communication uncertainties.