Optimal control: non-zero target control

In summary, the cost function for the arm with 2 joints (elbow, shoulder) is J=\frac{1}{2}x(t_f)^TS_fx(t_f)+\frac{1}{2}\int^{t_f}_{t_0}(x(t)^TQx(t)+u(t)^TRu(t))dt. The position where the wrist ends up depends in which state the arm was before the target control is applied.
  • #1
diminho
3
0
Dear all,

I am building an arm with 2 joints(elbow, shoulder) and want to optimally control it to a particular position(wirst). The examples I saw so far(e.g. acrobot) have a target control signal [itex]u(t_f)[/itex] which becomes zero when reaching the target [itex]x_{target}[/itex] in the final time step [itex]t_f[/itex].
In my case, the target control signal is not zero when reaching the target which can be any point in the horizontal plane within the range of the wrist(the arm is hanging down). My idea was to specify the target control as well as the target position which should be reached. So, the cost function
[tex]J=\frac{1}{2}x(t_f)^TS_fx(t_f)+\frac{1}{2}\int^{t_f}_{t_0}(x(t)^TQx(t)+u(t)^TRu(t))dt[/tex]
becomes
[tex]J=\frac{1}{2}(x(t_f)-x_{target}^T)S_f(x(t_f)-x_{target})+\frac{1}{2}\int^{t_f}_{t_0}((x(t)-x_{target})^TQ(x(t)-x_{target})+(u(t)-u_{target})^TR(u(t)-u_{target}))dt[/tex]
(see scholarpedia->optimal control for notation)
As I am building a real system with friction the target control [itex]u_{target}[/itex] does not always lead to the same position. The position where the wrist ends up depends in which state the arm was before the target control is applied.
So, although the wrist is at the correct position it gets penalized for not having the "correct" controls.

Does anyone of you have an idea how to formulate this problem?

PS: x contains the angles of the joints and the angle velocities.
 
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  • #2
Does no one have any idea? I really need helkp here:/
 
  • #3
Have you identified the reason the target control signal is not zero when the target is reached. Is it too little or too much?

How are you implementing this control. How is X(t) measured? How is U(tf) generated? Are you using a microcomputer to solve the function? Could you be getting digitization errors?
 
  • #4
The reason why there is nearly for all positions non-zero target control is that the arm is currently hanging down. I also put it in the horizontal plane, but the tendons pull the arm for some positions, too.

x(t)=[elbow, shoulder, elbowVelocity, shouldervelocity] is measured using rel. encoder with 333 steps/degree-> very precise. The control is generated using artificial muscles. I am using a Simulink and a PCI card to generate the signals. There might be some delay between setting the control and the generation of the torque using the muscles.
 
  • #5
I'm sorry but I do not understand your expressions. Would you be able to go through one or the other and explain each term and how you arrived at it?

Thanks.
 

Related to Optimal control: non-zero target control

1. What is optimal control?

Optimal control is a branch of mathematics and engineering that deals with finding the best way to control a dynamic system. This involves determining the optimal values of control inputs over a certain time period to achieve a desired outcome while considering constraints and minimizing costs.

2. What does "non-zero target control" mean?

Non-zero target control refers to a scenario where the target or desired state of a dynamic system is not equal to zero. This means that the system is aiming to reach a specific state or goal rather than just maintaining a steady state or equilibrium.

3. How is optimal control with non-zero target different from other types of control?

Unlike other types of control, optimal control with non-zero target takes into account the desired state of the system and aims to minimize the error or difference between the current state and the target state. It also considers constraints and minimizes costs, making it a more efficient and effective approach to control.

4. What are some real-world applications of optimal control with non-zero target?

Optimal control with non-zero target has various applications in different fields such as aerospace, robotics, economics, and environmental science. It is used to design autopilot systems, control the movements of robots, optimize economic policies, and manage pollution levels, among others.

5. What are the main challenges in implementing optimal control with non-zero target?

One of the main challenges in implementing optimal control with non-zero target is accurately modeling the dynamic system and its behavior. This requires a deep understanding of the system and its variables. Other challenges include dealing with uncertainties, optimizing over a large time horizon, and finding computationally efficient solutions.

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