diff --git a/.gitignore b/.gitignore index 508562dc..110ad714 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,8 @@ .DS_Store .vscode/ -__pycache__/ \ No newline at end of file +<<<<<<< HEAD +__pycache__/ +venv/ +======= +__pycache__/ +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb index aa0c5cee..0c6c5322 100644 --- a/notebooks/demo.ipynb +++ b/notebooks/demo.ipynb @@ -2,7 +2,11 @@ "cells": [ { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 2, +======= "execution_count": 1, +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a "metadata": {}, "outputs": [], "source": [ @@ -11,9 +15,31 @@ }, { "cell_type": "code", +<<<<<<< HEAD +<<<<<<< HEAD + "execution_count": 3, +======= + "execution_count": 7, +>>>>>>> feature/add-mission-method + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ +<<<<<<< HEAD + "['c:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312\\\\python312.zip', 'c:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312\\\\DLLs', 'c:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312\\\\Lib', 'c:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312', '', 'C:\\\\Users\\\\admin\\\\AppData\\\\Roaming\\\\Python\\\\Python312\\\\site-packages', 'C:\\\\Users\\\\admin\\\\AppData\\\\Roaming\\\\Python\\\\Python312\\\\site-packages\\\\win32', 'C:\\\\Users\\\\admin\\\\AppData\\\\Roaming\\\\Python\\\\Python312\\\\site-packages\\\\win32\\\\lib', 'C:\\\\Users\\\\admin\\\\AppData\\\\Roaming\\\\Python\\\\Python312\\\\site-packages\\\\Pythonwin', 'c:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312\\\\Lib\\\\site-packages']\n" +======= + "['C:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312\\\\python312.zip', 'C:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312\\\\DLLs', 'C:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312\\\\Lib', 'C:\\\\Users\\\\admin\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python312', 'c:\\\\Users\\\\admin\\\\Desktop\\\\Engineering\\\\B1-coding practical\\\\b1-coding-practical-mt24-main\\\\venv', '', 'c:\\\\Users\\\\admin\\\\Desktop\\\\Engineering\\\\B1-coding practical\\\\b1-coding-practical-mt24-main\\\\venv\\\\Lib\\\\site-packages', 'c:\\\\Users\\\\admin\\\\Desktop\\\\Engineering\\\\B1-coding practical\\\\b1-coding-practical-mt24-main\\\\venv\\\\Lib\\\\site-packages\\\\win32', 'c:\\\\Users\\\\admin\\\\Desktop\\\\Engineering\\\\B1-coding practical\\\\b1-coding-practical-mt24-main\\\\venv\\\\Lib\\\\site-packages\\\\win32\\\\lib', 'c:\\\\Users\\\\admin\\\\Desktop\\\\Engineering\\\\B1-coding practical\\\\b1-coding-practical-mt24-main\\\\venv\\\\Lib\\\\site-packages\\\\Pythonwin']\n" +>>>>>>> feature/add-mission-method + ] + } + ], +======= "execution_count": null, "metadata": {}, "outputs": [], +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a "source": [ "# You can double-check your Python path like this...\n", "import sys \n", @@ -21,6 +47,67 @@ ] }, { +<<<<<<< HEAD + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 4, +======= + "execution_count": 3, +>>>>>>> feature/add-mission-method + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c:\\Users\\admin\\Desktop\\Engineering\\B1-coding practical\\b1-coding-practical-mt24-main\\notebooks\n" + ] + } + ], + "source": [ + "import os\n", + "print(os.getcwd()) # Print current directory" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 5, +======= + "execution_count": 4, +>>>>>>> feature/add-mission-method + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# Switch to the root directory\n", + "os.chdir(r'c:\\Users\\admin\\Desktop\\Engineering\\B1-coding practical\\b1-coding-practical-mt24-main')" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 6, +======= + "execution_count": 5, +>>>>>>> feature/add-mission-method + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c:\\Users\\admin\\Desktop\\Engineering\\B1-coding practical\\b1-coding-practical-mt24-main\n" + ] + } + ], + "source": [ + "print(os.getcwd()) # Print current working directory, should be the root directory" + ] + }, + { +======= +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a "cell_type": "markdown", "metadata": {}, "source": [ @@ -30,6 +117,61 @@ }, { "cell_type": "code", +<<<<<<< HEAD +<<<<<<< HEAD + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'numpy'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Import relevant modules\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m \u001b[38;5;66;03m# 用于数组计算和随机扰动生成\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01muuv_mission\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdynamic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Submarine, Trajectory, Mission, ClosedLoop\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01muuv_mission\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontrol\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PDController \u001b[38;5;66;03m# 导入PD控制器\u001b[39;00m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'numpy'" + ] +======= + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" +>>>>>>> feature/add-mission-method + } + ], + "source": [ + "# Import relevant modules\n", +<<<<<<< HEAD + "import numpy as np \n", + "from uuv_mission.dynamic import Submarine, Trajectory, Mission, ClosedLoop\n", + "from uuv_mission.control import PDController \n", +======= + "import numpy as np # 用于数组计算和随机扰动生成\n", + "from uuv_mission.dynamic import Submarine, Trajectory, Mission, ClosedLoop\n", + "from uuv_mission.control import PDController # 导入PD控制器\n", +>>>>>>> feature/add-mission-method + "\n", + "# Initialize the submarine model\n", + "sub = Submarine()\n", + "\n", + "# Set the PD controller gains\n", + "kp = 0.15 # Proportional gain\n", + "kd = 0.6 # Derivative gain\n", + "controller = PDController(kp=kp, kd=kd)\n", + "\n", + "# Instantiate your controller (depending on your implementation)\n", + "closed_loop = ClosedLoop(sub, controller)\n", + "mission = Mission.from_csv(\"data/mission.csv\") # You must implement this method in the Mission class\n", +======= "execution_count": null, "metadata": {}, "outputs": [], @@ -40,6 +182,7 @@ "# Instantiate your controller (depending on your implementation)\n", "closed_loop = ClosedLoop(sub, controller)\n", "mission = Mission.from_csv(\"path/to/file\") # You must implement this method in the Mission class\n", +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a "\n", "trajectory = closed_loop.simulate_with_random_disturbances(mission)\n", "trajectory.plot_completed_mission(mission)" @@ -48,7 +191,15 @@ ], "metadata": { "kernelspec": { +<<<<<<< HEAD +<<<<<<< HEAD + "display_name": "Python 3", +======= + "display_name": "venv", +>>>>>>> feature/add-mission-method +======= "display_name": "first-venv", +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a "language": "python", "name": "python3" }, @@ -62,7 +213,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", +<<<<<<< HEAD + "version": "3.12.1" +======= "version": "3.9.12" +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a } }, "nbformat": 4, diff --git a/uuv_mission/control.py b/uuv_mission/control.py new file mode 100644 index 00000000..09762c5e --- /dev/null +++ b/uuv_mission/control.py @@ -0,0 +1,28 @@ +class PDController: + def __init__(self, kp, kd): + """ + Initialize the PD controller. + :param kp: Proportional gain. + :param kd: Derivative gain. + """ + self.kp = kp # Proportional gain + self.kd = kd # Derivative gain + self.prev_error = 0 # Store previous error for derivative calculation + + + def get_control_action(self, error): + """ + Calculate the control action using the PD formula. + :param error: The current error (difference between reference and actual depth). + :return: Control action to adjust the submarine's depth. + """ + # Derivative of error + derivative = error - self.prev_error + + # PD control law: u[t] = kp * e[t] + kd * (e[t] - e[t-1]) + control_action = self.kp * error + self.kd * derivative + + # Update previous error for next step + self.prev_error = error + + return control_action \ No newline at end of file diff --git a/uuv_mission/dynamic.py b/uuv_mission/dynamic.py index c7c7ad53..b28b53c8 100644 --- a/uuv_mission/dynamic.py +++ b/uuv_mission/dynamic.py @@ -3,6 +3,10 @@ import numpy as np import matplotlib.pyplot as plt from .terrain import generate_reference_and_limits +<<<<<<< HEAD +import pandas as pd +======= +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a class Submarine: def __init__(self): @@ -76,7 +80,20 @@ def random_mission(cls, duration: int, scale: float): @classmethod def from_csv(cls, file_name: str): # You are required to implement this method +<<<<<<< HEAD + # Read the CSV file into a pandas DataFrame + df = pd.read_csv(file_name) + + # Extract the relevant columns: 'reference', 'cave_height', and 'cave_depth' + reference = df['reference'].values + cave_height = df['cave_height'].values + cave_depth = df['cave_depth'].values + + # Create and return a Mission instance + return cls(reference, cave_height, cave_depth) +======= pass +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a class ClosedLoop: @@ -98,6 +115,16 @@ def simulate(self, mission: Mission, disturbances: np.ndarray) -> Trajectory: positions[t] = self.plant.get_position() observation_t = self.plant.get_depth() # Call your controller here +<<<<<<< HEAD + # Step 1: Compute the error between the reference depth and the observed depth + error = mission.reference[t] - observation_t + + # Step 2: Use the controller to compute the control action + actions[t] = self.controller.get_control_action(error) + + # Step 3: Apply the control action and the disturbance to the submarine +======= +>>>>>>> d423aa10851a341bb416191f14724147c4bfba0a self.plant.transition(actions[t], disturbances[t]) return Trajectory(positions)