[copybara] Import robotpy examples (#8608)

GitOrigin-RevId: 9ba4bc3040fa7e772f5a594039e78fc6c43d114e

robotpyExamples/StateSpaceArm/robot.py

@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) FIRST and other WPILib contributors.
+# Open Source Software; you can modify and/or share it under the terms of
+# the WPILib BSD license file in the root directory of this project.
+#
+
+import math
+import wpilib
+import wpimath.units
+import wpimath
+
+kMotorPort = 0
+kEncoderAChannel = 0
+kEncoderBChannel = 1
+kJoystickPort = 0
+kRaisedPosition = wpimath.units.degreesToRadians(90.0)
+kLoweredPosition = wpimath.units.degreesToRadians(0.0)
+
+# Moment of inertia of the arm, in kg * m^2. Can be estimated with CAD. If finding this constant
+# is difficult, LinearSystem.identifyPositionSystem may be better.
+kArmMOI = 1.2
+
+# Reduction between motors and encoder, as output over input. If the arm spins slower than
+# the motors, this number should be greater than one.
+kArmGearing = 10.0
+
+
+class MyRobot(wpilib.TimedRobot):
+    """This is a sample program to demonstrate how to use a state-space controller to control an arm."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self.profile = wpimath.TrapezoidProfile(
+            wpimath.TrapezoidProfile.Constraints(
+                wpimath.units.degreesToRadians(45),
+                wpimath.units.degreesToRadians(90),  # Max arm speed and acceleration.
+            )
+        )
+
+        self.lastProfiledReference = wpimath.TrapezoidProfile.State()
+
+        # The plant holds a state-space model of our arm. This system has the following properties:
+        #
+        # States: [position, velocity], in radians and radians per second.
+        # Inputs (what we can "put in"): [voltage], in volts.
+        # Outputs (what we can measure): [position], in radians.
+        self.armPlant = wpimath.Models.singleJointedArmFromPhysicalConstants(
+            wpimath.DCMotor.NEO(2),
+            kArmMOI,
+            kArmGearing,
+        ).slice(0)
+
+        # The observer fuses our encoder data and voltage inputs to reject noise.
+        self.observer = wpimath.KalmanFilter_2_1_1(
+            self.armPlant,
+            # How accurate we think our model is, in radians and radians/sec.
+            (
+                0.015,
+                0.17,
+            ),
+            # How accurate we think our encoder position data is. In this case we very highly trust our encoder position reading.
+            (0.01,),
+            0.020,
+        )
+
+        # A LQR uses feedback to create voltage commands.
+        self.controller = wpimath.LinearQuadraticRegulator_2_1(
+            self.armPlant,
+            # qelms. Velocity error tolerance, in radians and radians per second.
+            # Decrease this to more heavily penalize state excursion, or make the
+            # controller behave more aggressively.
+            (
+                wpimath.units.degreesToRadians(1.0),
+                wpimath.units.degreesToRadians(10.0),
+            ),
+            # relms. Control effort (voltage) tolerance. Decrease this to more
+            # heavily penalize control effort, or make the controller less
+            # aggressive. 12 is a good starting point because that is the
+            # (approximate) maximum voltage of a battery.
+            (12.0,),
+            # Nominal time between loops. 20ms for TimedRobot, but can be lower if
+            # using notifiers.
+            0.020,
+        )
+
+        # The state-space loop combines a controller, observer, feedforward and plant for easy control.
+        self.loop = wpimath.LinearSystemLoop_2_1_1(
+            self.armPlant, self.controller, self.observer, 12.0, 0.020
+        )
+
+        # An encoder set up to measure flywheel velocity in radians per second.
+        self.encoder = wpilib.Encoder(kEncoderAChannel, kEncoderBChannel)
+
+        self.motor = wpilib.PWMSparkMax(kMotorPort)
+
+        # A joystick to read the trigger from.
+        self.joystick = wpilib.Joystick(kJoystickPort)
+
+        # We go 2 pi radians in 1 rotation, or 4096 counts.
+        self.encoder.setDistancePerPulse(math.tau / 4096)
+
+    def teleopInit(self) -> None:
+        # Reset our loop to make sure it's in a known state.
+        self.loop.reset([self.encoder.getDistance(), self.encoder.getRate()])
+
+        # Reset our last reference to the current state.
+        self.lastProfiledReference = wpimath.TrapezoidProfile.State(
+            self.encoder.getDistance(), self.encoder.getRate()
+        )
+
+    def teleopPeriodic(self) -> None:
+        # Sets the target position of our arm. This is similar to setting the setpoint of a
+        # PID controller.
+
+        if self.joystick.getTrigger():
+            # the trigger is pressed, so we go to the high goal.
+            goal = wpimath.TrapezoidProfile.State(kRaisedPosition, 0.0)
+
+        else:
+            # Otherwise, we go to the low goal
+            goal = wpimath.TrapezoidProfile.State(kLoweredPosition, 0.0)
+
+        # Step our TrapezoidalProfile forward 20ms and set it as our next reference
+        self.lastProfiledReference = self.profile.calculate(
+            0.020, self.lastProfiledReference, goal
+        )
+        self.loop.setNextR(
+            [self.lastProfiledReference.position, self.lastProfiledReference.velocity]
+        )
+
+        # Correct our Kalman filter's state vector estimate with encoder data.
+        self.loop.correct([self.encoder.getDistance()])
+
+        # Update our LQR to generate new voltage commands and use the voltages to predict the next
+        # state with out Kalman filter.
+        self.loop.predict(0.020)
+
+        # Send the new calculated voltage to the motors.
+        # voltage = duty cycle * battery voltage, so
+        # duty cycle = voltage / battery voltage
+        nextVoltage = self.loop.U(0)
+        self.motor.setVoltage(nextVoltage)