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This paper proposes a framework for adaptively learning a feedback linearization-based tracking controller for an unknown system using discrete-time model-free policy-gradient parameter update rules. The primary advantage of the scheme over standard model-reference adaptive control techniques is that it does not require the learned inverse model to be invertible at all instances of time. This enables the use of general function approximators to approximate the linearizing controller for the system without having to worry about singularities. However, the discrete-time and stochastic nature of these algorithms precludes the direct application of standard machinery from the adaptive control literature to provide deterministic stability proofs for the system. Nevertheless, we leverage these techniques alongside tools from the stochastic approximation literature to demonstrate that with high probability the tracking and parameter errors concentrate near zero when a certain persistence of excitation condition is satisfied. A simulated example of a double pendulum demonstrates the utility of the proposed theory. 1

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The food industry is a global collective of diverse businesses that supply much of the food energy consumed by the world population. Eggs are one of the most nutritious foods available. The versatility of eggs means that they are an important ingredient in many food products including cakes, sauces, desserts and sandwiches. In egg processing industry the approximate amount of egg processed is 14, 00,500 eggs per day including wastages. But there is no accurate method to measure the broke eggs excluding the wastages. Our Project enhances the correct measurement and provides the data that how much eggs can be break to improve the amount of egg powder produced. Our project also includes the level maintenance the collecting tank which collects yolk, albumen and whole egg separately after breaking the eggs and the suction motor can be operated automatically when the level reaches the required level, this helps in complete automation in the industry by using Programmable Logic Controller (PLC).