Help on optimal control problem

In summary, the conversation discusses a proof of a verification theorem for an optimal control problem. The problem involves a cost function and a control that can only take on the values of 0 or 1. The individual is having difficulty with the standard verification argument due to the limit of integration being a stopping time. There is also a mention of an issue with the equation rendering correctly in a browser.
  • #1
psalgado
2
0
Hi guys, I could use some help on the proof of a verification theorem for the following optimal control problem


[itex]
J_{M}(x;u)&\equiv&\mathbb{E}^{x}\left[\int_{0}^{\tau_{C}}\left(\int_{0}^{t}e^{-rs}\pi_{M}(x_{s})ds\right)\lambda u_{t}e^{-\lambda\int_{0}^{t}u_{z}dz}dt+\int_{0}^{\tau_{C}}\lambda u_{t}e^{-rt-\lambda\int_{0}^{t}u_{z}dz}\phi x_{t}dt\right]
[/itex]



where the control can only assume the values 0 or 1.

Having some trouble with the standard verficication argument that relies on Dynkin Formula, since the limit of integration is a stopping time.
 
Last edited:
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  • #2
Your equation does not seem to be rendering correctly for me. Does it appear okay in your browser? (I'm using IE)
 

1. What is an optimal control problem?

An optimal control problem is a mathematical problem that involves finding the best control strategy for a given system in order to optimize a certain performance measure. It is commonly used in engineering, economics, and other fields to design efficient and effective control systems.

2. What are the main components of an optimal control problem?

The main components of an optimal control problem are the system dynamics, the control variables, the performance measure, and the constraints. The system dynamics describe how the system evolves over time, the control variables are the inputs that can be manipulated, the performance measure is the goal to be optimized, and the constraints are the limitations on the system and control variables.

3. How is an optimal control problem solved?

An optimal control problem is typically solved using techniques from optimization and control theory. These may include variational methods, dynamic programming, Pontryagin's maximum principle, and numerical methods such as gradient descent or shooting methods. The specific approach depends on the complexity and nature of the problem.

4. What are the applications of optimal control problems?

Optimal control problems have a wide range of applications in various fields, including aerospace engineering, robotics, economics, and finance. They are used to design efficient and optimal control strategies for systems such as spacecraft, industrial processes, economic models, and more.

5. What are some challenges in solving optimal control problems?

One of the main challenges in solving optimal control problems is the trade-off between complexity and accuracy. As the problem becomes more complex, it may be more accurate but also more difficult to solve. Additionally, the choice of performance measure and constraints can greatly affect the difficulty of the problem. Furthermore, real-world systems may have uncertainties and disturbances that can make finding an optimal control strategy more challenging.

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