Feedback control

Report
Introductory Control
Theory
I400/B659: Intelligent robotics
Kris Hauser
Control Theory
• The use of feedback to regulate a signal
Desired
signal xd
Controller
Control input u
Signal x
Plant
Error e = x-xd
(By convention, xd = 0)
x’ = f(x,u)
What might we be interested in?
• Controls engineering
• Produce a policy u(x,t), given a description of the
plant, that achieves good performance
• Verifying theoretical properties
• Convergence, stability, optimality of a given policy
u(x,t)
Agenda
• PID control
• Feedforward + feedback control
• Control is a huge topic, and we won’t dive into
much detail
Model-free vs model-based
• Two general philosophies:
• Model-free: do not require a dynamics model to be provided
• Model-based: do use a dynamics model during computation
• Model-free methods:
• Simpler
• Tend to require much more manual tuning to perform well
• Model-based methods:
•
•
•
•
•
Can achieve good performance (optimal w.r.t. some cost function)
Are more complicated to implement
Require reasonably good models (system-specific knowledge)
Calibration: build a model using measurements before behaving
Adaptive control: “learn” parameters of the model online from
sensors
PID control
• Proportional-Integral-Derivative controller
• A workhorse of 1D control systems
• Model-free
Proportional term
Gain
• u(t) = -Kp x(t)
• Negative sign assumes control acts in the same
direction as x
x
t
Integral term
Integral gain
• u(t) = -Kp x(t) - Ki I(t)
• I(t) =


0
  (accumulation of errors)
x
t
Residual steady-state errors driven
asymptotically to 0
Instability
• For a 2nd order system (momentum), P control
Divergence
x
t
Derivative term
Derivative gain
• u(t) = -Kp x(t) – Kd x’(t)
x
Putting it all together
• u(t) = -Kp x(t) - Ki I(t) - Kd x’(t)
• I(t) =


0
 
Putting it all together
• u(t) = -Kp x(t) - Ki I(t) - Kd x’(t)
• I(t) =


0
 
• Intuition:
• Kp controls the “spring stiffness”
• Ki controls the “learning rate”
• Kd controls the damping
Putting it all together
• u(t) = -Kp x(t) - Ki I(t) - Kd x’(t)
• I(t) =


0
 
• Intuition:
• Kp controls the “spring stiffness”
• Kd controls the amount of damping
• Ki controls the “learning rate”
• Control limits
• If u is bounded in range [umin,umax], need to:
1. Clamp u
2. Bound the magnitude of the I term to prevent overshoot
Parameter tuning
Example: Damped Harmonic
Oscillator
• Second order time invariant linear system, PID controller
• x’’(t) = A x(t) + B x’(t) + C + D u(x,x’,t)
• For what starting conditions, gains is this stable and
convergent?
Stability and Convergence
• System is stable if errors stay bounded
• System is convergent if errors -> 0
Example: Damped Harmonic
Oscillator
•
•
•
•
x’’ = A x + B x’ + C + D u(x,x’)
PID controller u = -Kp x –Kd x’ – Ki I
x’’ = (A-DKp) x + (B-DKd) x’ + C - D Ki I
Assume Ki=0…
Homogenous solution
•
•
•
•
•
Instable if A-DKp > 0
Natural frequency w0 = sqrt(DKp-A)
Damping ratio z=(DKd-B)/2w0
If z > 1, overdamped
If z < 1, underdamped (oscillates)
Example: Trajectory following
• Say a trajectory xdes(t) has been designed
• E.g., a rocket’s ascent, a steering path for a car, a plane’s landing
• Apply PID control
• u(t) = Kp (xdes(t)- x(t)) - Ki I(t) + Kd (x’des(t)-x’(t))
• I(t) =


0 
 −   
• The designer of xdes needs to be knowledgeable about the
controller’s behavior!
x(t)
xdes(t)
x(t)
Controller Tuning Workflow
• Hypothesize a control policy
• Analysis:
• Assume a model
• Assume disturbances to be handled
• Test performance either through mathematical analysis, or
through simulation
• Go back and redesign control policy
• Mathematical techniques give you more insight to improve
redesign, but require more work
Example
Feedforward control
• If we know a model for a system and know how it should
move, why don’t we just compute the correct control?
• Ex: damped harmonic oscillator
•
•
•
•
x’’ = A x + B x’ + C + D u
Calculate a trajectory x(t) leading to x(T)=0 at some point T
Compute its 1st and 2nd derivatives x’(t), x’’(t)
Solve for u(t) = 1/D*(x’’(t) - A x(t) + B x’(t) + C)
• Would be perfect!
Feedforward control
• If we know a model for a system and know how it should
move, why don’t we just compute the correct control?
• Ex: damped harmonic oscillator
•
•
•
•
x’’ = A x + B x’ + C + D u
Calculate a trajectory x(t) leading to x(T)=0 at some point T
Compute its 1st and 2nd derivatives x’(t), x’’(t)
Solve for u(t) = 1/D*(x’’(t) - A x(t) + B x’(t) + C)
• Problems
• Control limits: trajectory must be planned with knowledge of
control constraints
• Disturbances and modeling errors: open loop control leads to
errors not converging to 0
Handling errors: feedforward +
feedback
• Idea: combine feedforward control uff with feedback control
ufb
• e.g., ufb computed with PID control
• Feedback term doesn’t have to “work” as hard (= 0 ideally)
x(0)
Feedforward
calculation
uff
+
u
ufb
xdes
Plant
Feedback
controller
x(t)
Application: Feedforward control
• Feedback control: let torques be a
function of the current error between
actual and desired configuration
• Problem: heavy arms require strong
torques, requiring a stiff system
• Stiff systems become unstable relatively
quickly
Application: Feedforward control
• Solution: include feedforward torques
to reduce reliance on feedback
• Estimate the torques that would
compensate for gravity and achieve
desired accelerations (inverse
dynamics), send those torques to the
motors
Handling errors: feedforward +
feedback
• Idea: combine feedforward control uff with feedback control
ufb
• e.g., ufb computed with PID control
• Feedback term doesn’t have to “work” as hard (= 0 ideally)
x(0)
Feedforward
calculation
uff
+
u
ufb
Model based
xdes
Model free
Plant
Feedback
controller
x(t)
Model predictive control
(MPC)
• Idea: repeatedly compute feedforward control using model of
the dynamics
• In other words, rapid replanning
Feedforward
calculation
u=uff
Plant
xdes
x(t)
MPC to avoid moving obstacles
Next class: Sphero Lab
• You will design a feedback controller for the “move” command

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