mirror of
https://github.com/esiur/esiur-dotnet.git
synced 2025-05-06 11:32:59 +00:00
471 lines
20 KiB
C#
471 lines
20 KiB
C#
/*
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Copyright (c) 2022 Ahmed Kh. Zamil
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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*/
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using Esiur.Analysis.DSP;
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using Esiur.Analysis.Fuzzy;
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using Esiur.Analysis.Optimization;
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using Esiur.Analysis.Signals;
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using Esiur.Analysis.Units;
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using Microsoft.VisualBasic.Logging;
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using ScottPlot;
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using ScottPlot.Drawing.Colormaps;
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using System.Security.Cryptography;
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using Esiur.Analysis.Statistics;
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using System.Diagnostics;
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namespace Esiur.Analysis.Test
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{
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public partial class FSoft : Form
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{
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private double[] num = new double[] { 10 };
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private double[] denum = new double[] { 1, 1, 0.1 };
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private int interval = 8000;
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private double stability = 100;
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public FSoft()
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{
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InitializeComponent();
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//var outage = Capacity.ComputeOutage(20000000, new Capacity.CSI[]
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//{
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// new Capacity.CSI(PowerUnit.FromDb(20), 0.1),
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// new Capacity.CSI(PowerUnit.FromDb(15), 0.15),
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// new Capacity.CSI(PowerUnit.FromDb(10), 0.25),
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// new Capacity.CSI(PowerUnit.FromDb(5), 0.25),
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// new Capacity.CSI(PowerUnit.FromDb(0), 0.15),
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// new Capacity.CSI(PowerUnit.FromDb(-5), 0.1),
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//});
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var outage = Capacity.ComputeOutage(1, new Capacity.CSI[]
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{
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new Capacity.CSI(PowerUnit.FromDb(30), 0.2),
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new Capacity.CSI(PowerUnit.FromDb(20), 0.3),
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new Capacity.CSI(PowerUnit.FromDb(10), 0.3),
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new Capacity.CSI(PowerUnit.FromDb(0), 0.2),
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});
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var low = new ContinuousSet(MembershipFunctions.Descending(20, 40));
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var mid = new ContinuousSet(MembershipFunctions.Triangular(20, 40, 60));
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var high = new ContinuousSet(MembershipFunctions.Ascending(40, 60));
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var bad = new ContinuousSet(MembershipFunctions.Descending(0, 30));
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var ok = new ContinuousSet(MembershipFunctions.Triangular(20, 50, 80));
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var excelent = new ContinuousSet(MembershipFunctions.Ascending(70, 100));
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var small = new ContinuousSet(MembershipFunctions.Descending(100, 200));
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var avg = new ContinuousSet(MembershipFunctions.Triangular(100, 200, 300));
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var big = new ContinuousSet(MembershipFunctions.Ascending(200, 300));
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//var speedIsLowThenSmall = new FuzzyRule("Low=>Small", low, small);
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double rating = 80;
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for (double temp = 60; temp < 100; temp++)
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{
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var v = MamdaniDefuzzifier.Evaluate(new INumericalSet<double>[]
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{
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temp.Is(low).And(rating.Is(bad)).Then(small),
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temp.Is(mid).And(rating.Is(ok)).Then(avg),
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temp.Is(high).And(rating.Is(excelent)).Then(big),
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}, MamdaniDefuzzifierMethod.CenterOfGravity, 100, 300, 1);
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}
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}
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private void FMain_Load(object sender, EventArgs e)
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{
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button3_Click(sender, e);
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}
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struct KK
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{
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public float Ki;
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public float Kp;
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public float Kd;
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public override string ToString()
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{
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return $"Ki {Ki} Kp {Kp} Kd {Kd}";
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}
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}
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struct FuzzyChromosome
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{
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////public sbyte KiInputErrPosition;
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//public sbyte KiInputErrScale;
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////public sbyte KiInputErrAccPosition;
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//public sbyte KiInputErrAccScale;
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////public sbyte KiOutputPosition;
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//public sbyte KiOutputScale;
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public sbyte KiStart;
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public byte KiLength;
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public sbyte KdStart;
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public byte KdLength;
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public sbyte KpStart;
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public byte KpLength;
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public override string ToString()
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{
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return $"Ki {KiStart}:{KiLength} Kp {KpStart}:{KpLength} Kd {KdStart}:{KdLength}";
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}
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}
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private double CalculateFuzzyPIDStepError(FuzzyChromosome config, double errStart, double errEnd, double errAccStart, double errAccEnd, bool draw, string label)
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{
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var lowErr = new ContinuousSet(MembershipFunctions.Descending(errStart, errStart + (errEnd - errStart) * 0.5));
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var midErr = new ContinuousSet(MembershipFunctions.Triangular(errStart, errStart + (errEnd - errStart) * 0.5, errEnd));
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var highErr = new ContinuousSet(MembershipFunctions.Ascending(errStart + (errEnd - errStart) * 0.5, errEnd));
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var lowAccErr = new ContinuousSet(MembershipFunctions.Descending(errAccStart, errAccStart + (errAccEnd - errAccStart) * 0.5));
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var midAccErr = new ContinuousSet(MembershipFunctions.Triangular(errAccStart, errAccStart + (errAccEnd - errAccStart) * 0.5, errAccEnd));
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var highAccErr = new ContinuousSet(MembershipFunctions.Ascending(errAccStart + (errAccEnd - errAccStart) * 0.5, errAccEnd));
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var kiSmall = new ContinuousSet(MembershipFunctions.Descending(config.KiStart * 0.1, (config.KiStart + (config.KiLength * 0.5)) * 0.1));
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var kiAvg = new ContinuousSet(MembershipFunctions.Triangular(config.KiStart * 0.1, (config.KiStart + (config.KiLength * 0.5)) * 0.1, (config.KiStart + config.KiLength) * 0.1));
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var kiBig = new ContinuousSet(MembershipFunctions.Ascending((config.KiStart + (config.KiLength * 0.5)) * 0.1, (config.KiStart + config.KiLength) * 0.1));
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var kdSmall = new ContinuousSet(MembershipFunctions.Descending(config.KdStart * 0.1, (config.KdStart + (config.KdLength * 0.5)) * 0.1));
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var kdAvg = new ContinuousSet(MembershipFunctions.Triangular(config.KdStart * 0.1, (config.KdStart + (config.KdLength * 0.5)) * 0.1, (config.KdStart + config.KdLength) * 0.1));
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var kdBig = new ContinuousSet(MembershipFunctions.Ascending((config.KdStart + (config.KdLength * 0.5)) * 0.1, (config.KdStart + config.KdLength) * 0.1));
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var kpSmall = new ContinuousSet(MembershipFunctions.Descending(config.KpStart * 0.1, (config.KpStart + (config.KpLength * 0.5)) * 0.1));
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var kpAvg = new ContinuousSet(MembershipFunctions.Triangular(config.KpStart * 0.1, (config.KpStart + (config.KpLength * 0.5)) * 0.1, (config.KpStart + config.KpLength) * 0.1));
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var kpBig = new ContinuousSet(MembershipFunctions.Ascending((config.KpStart + (config.KpLength * 0.5)) * 0.1, (config.KpStart + config.KpLength) * 0.1));
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double Ki = -1.9181372, Kp = 18.625, Kd = 0.38281253;
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//double Ki = 1, Kp = 1, Kd = 1;
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var step = Enumerable.Repeat(1, interval).Select(x => (double)x).ToArray();
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step[0] = 0;
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var motor = new TransferFunction(num, denum, 0.01);
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var fuzzyPID = new TransferFunction(new double[] { Kd, Kp, Ki }, new double[] { 1, 1 }, 0.01);
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var sysOutFuzzyPID = new double[step.Length];
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var pidOutFuzzy = new double[step.Length];
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var errorOutFuzzy = new double[step.Length];
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var errorOutAccFuzzy = new double[step.Length];
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for (var i = 0; i < step.Length; i++)
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{
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sysOutFuzzyPID[i] = motor.Evaluate(step[i] + (i == 0 ? 0 : pidOutFuzzy[i - 1]));
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errorOutFuzzy[i] = (stability - sysOutFuzzyPID[i]);
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errorOutAccFuzzy[i] = (errorOutFuzzy[i] - (i == 0 ? 0 : errorOutFuzzy[i - 1]));
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var ki = MamdaniDefuzzifier.Evaluate(new INumericalSet<double>[]
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{
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kiSmall),
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kiSmall),
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kiAvg),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kiSmall),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kiAvg),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kiBig),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kiAvg),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kiBig),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kiBig),
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}, MamdaniDefuzzifierMethod.CenterOfGravity, config.KiStart * 0.1, (config.KiStart + config.KiLength) * 0.1, 0.5);
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if (double.IsNaN(ki))
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return double.MaxValue;
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var kp = MamdaniDefuzzifier.Evaluate(new INumericalSet<double>[]
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{
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kpSmall),
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kpSmall),
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kpAvg),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kpSmall),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kpAvg),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kpBig),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kpAvg),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kpBig),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kpBig),
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}, MamdaniDefuzzifierMethod.CenterOfGravity, config.KpStart * 0.1, (config.KpStart + config.KpLength) * 0.1, 0.5);
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if (double.IsNaN(kp))
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return double.MaxValue;
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var kd = MamdaniDefuzzifier.Evaluate(new INumericalSet<double>[]
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{
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kdSmall),
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kdSmall),
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errorOutFuzzy[i].Is(lowErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kdAvg),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kdSmall),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kdAvg),
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errorOutFuzzy[i].Is(midErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kdBig),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(lowAccErr)).Then(kdAvg),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(midAccErr)).Then(kdBig),
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errorOutFuzzy[i].Is(highErr).And(errorOutAccFuzzy[i].Is(highAccErr)).Then(kdBig),
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}, MamdaniDefuzzifierMethod.CenterOfGravity, config.KdStart * 0.1, (config.KdStart + config.KdLength) * 0.1, 0.5);
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if (double.IsNaN(kd))
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return double.MaxValue;
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fuzzyPID.InputCoefficients[0] = ki;
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fuzzyPID.InputCoefficients[1] = kp;
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fuzzyPID.InputCoefficients[1] = kd;
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pidOutFuzzy[i] = fuzzyPID.Evaluate(errorOutFuzzy[i]);
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}
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if (draw)
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{
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formsPlot1.Plot.Clear();
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var x = Enumerable.Range(0, interval).Select(x => x * 0.01).ToArray();
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formsPlot1.Plot.AddScatter(x, sysOutFuzzyPID, Color.Green);
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formsPlot1.Plot.AddText(label, 0, 1.5, 24, Color.DarkOrange);
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formsPlot1.Refresh();
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formsPlot2.Plot.Clear();
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var range = FuzzyExtensions.Range(config.KiStart * 0.1, (config.KiStart + config.KiLength) * 0.1, 0.1);
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formsPlot2.Plot.AddScatter(range, kiSmall.Sample(range));
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formsPlot2.Plot.AddScatter(range, kiAvg.Sample(range));
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formsPlot2.Plot.AddScatter(range, kiBig.Sample(range));
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//formsPlot2.Plot.AddText("Ki", 0, 0, 20);
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formsPlot2.Refresh();
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formsPlot3.Plot.Clear();
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range = FuzzyExtensions.Range(config.KpStart * 0.1, (config.KpStart + config.KpLength) * 0.1, 0.1);
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formsPlot3.Plot.AddScatter(range, kpSmall.Sample(range));
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formsPlot3.Plot.AddScatter(range, kpAvg.Sample(range));
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formsPlot3.Plot.AddScatter(range, kpBig.Sample(range));
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//formsPlot2.Plot.AddText("Kp", 0, 0, 20);
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formsPlot3.Refresh();
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formsPlot4.Plot.Clear();
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range = FuzzyExtensions.Range(config.KdStart * 0.1, (config.KdStart + config.KdLength) * 0.1, 0.1);
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formsPlot4.Plot.AddScatter(range, kdSmall.Sample(range));
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formsPlot4.Plot.AddScatter(range, kdAvg.Sample(range));
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formsPlot4.Plot.AddScatter(range, kdBig.Sample(range));
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//formsPlot2.Plot.AddText("Kd", 0, 0, 20);
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formsPlot4.Refresh();
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}
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//Debug.WriteLine("ERR " + errorOutFuzzy.Max() + " " + errorOutFuzzy.Min());
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var r = errorOutFuzzy.Sum(x => Math.Abs((decimal)x));// .RMS();
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//if (decimal.IsNaN(r) || decimal.IsInfinity(r))
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// Console.WriteLine();
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return (double) r;
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}
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private double CalculatePIDStepError(double Kd, double Kp, double Ki, bool draw, string label)
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{
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var step = Enumerable.Repeat(1, interval).Select(x => (double)x).ToArray();
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step[0] = 0;
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var motor = new TransferFunction(num, denum, 0.01);
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var sysOutPID = new double[step.Length];
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var pidOut = new double[step.Length];
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var errorOutPID = new double[step.Length];
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var pid = new TransferFunction(new double[] { Kd, Kp, Ki }, new double[] { 1, 1 }, 0.01);
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for (var i = 0; i < step.Length; i++)
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{
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sysOutPID[i] = motor.Evaluate(step[i] + (i == 0 ? 0 : pidOut[i - 1]));
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if (double.IsInfinity(sysOutPID[i]))
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Console.WriteLine();
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errorOutPID[i] = (stability - sysOutPID[i]);
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if (double.IsNaN(errorOutPID[i]))
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Console.WriteLine();
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pidOut[i] = pid.Evaluate(errorOutPID[i]);
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if (double.IsInfinity(pidOut[i]))
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Console.WriteLine();
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}
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if (draw)
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{
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formsPlot1.Plot.Clear();
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var x = Enumerable.Range(0, interval).Select(x => x * 0.01).ToArray();
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formsPlot1.Plot.AddText(label, 0, 1.5, 24, Color.DarkOliveGreen);
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formsPlot1.Plot.AddScatter(x, sysOutPID, Color.DeepSkyBlue);
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formsPlot1.Refresh();
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}
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var r = errorOutPID.Sum(x => Math.Abs(x));// .RMS();
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if (double.IsPositiveInfinity(r))
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return double.MaxValue;
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else if (double.IsNegativeInfinity(r))
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return double.MinValue;
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return r;// errorOutPID.RMS();
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}
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private void button1_Click(object sender, EventArgs e)
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{
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var genetic = new Genetic<FuzzyChromosome>(100, k =>
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{
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if (float.IsNaN(k.KiStart)
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|| float.IsNaN(k.KiLength)
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|| float.IsNaN(k.KpStart)
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|| float.IsNaN(k.KiLength)
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|| float.IsNaN(k.KdStart)
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|| float.IsNaN(k.KiLength))
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return (double.MaxValue);
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var r = CalculateFuzzyPIDStepError(k, -(stability / 2), stability / 2, -(stability / 2), stability / 2, false, null);
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//var r = CalculateFuzzyPIDStepError(k, -50, 50, -50, 50, false, null);
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if (double.IsNaN(r))
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Console.WriteLine();
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return r;
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});
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foreach (var (generation, fitness, k) in genetic.Evaluate(100))
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{
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if (float.IsNaN(k.KiStart)
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|| float.IsNaN(k.KiLength)
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|| float.IsNaN(k.KpStart)
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|| float.IsNaN(k.KiLength)
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|| float.IsNaN(k.KdStart)
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|| float.IsNaN(k.KiLength))
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continue;
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CalculateFuzzyPIDStepError(k, -(stability / 2), stability / 2, -(stability / 2), stability / 2, true, $"Fuzzy PID: Generation {generation} Fitness {Math.Round( fitness)} {k}");
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// CalculateFuzzyPIDStepError(k, -50, 50, -50, 50, true, $"Fuzzy PID: Generation {generation} Fitness {fitness}\r\n{k}");
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}
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// Console.WriteLine(best);
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}
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private void button2_Click(object sender, EventArgs e)
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{
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var gen = new Genetic<KK>(100, k =>
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{
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if (float.IsNaN(k.Ki) || float.IsNaN(k.Kp) || float.IsNaN(k.Kd))
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return (double.MaxValue);
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var r = CalculatePIDStepError(k.Kd, k.Kp, k.Ki, false, null);
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if (double.IsNaN(r))
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Console.WriteLine();
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return r;
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});
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foreach (var (generation, fitness, k) in gen.Evaluate(100))
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CalculatePIDStepError(k.Kd, k.Kp, k.Ki, true, $"PID: Generation {generation} Fitness {Math.Round( fitness)} {k}");
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}
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private void button3_Click(object sender, EventArgs e)
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{
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num = textBox1.Text.Split("/").First().Trim().Split(" ").Select(x=>Convert.ToDouble(x)).ToArray();
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denum = textBox1.Text.Split("/").Last().Trim().Split(" ").Select(x=>Convert.ToDouble(x)).ToArray();
|
|
|
|
var x = Enumerable.Range(0, interval).Select(x => x * 0.01).ToArray();
|
|
|
|
var step = Enumerable.Repeat(1, interval).Select(x => (double)x).ToArray();
|
|
step[0] = 0;
|
|
|
|
var motor = new TransferFunction(num, denum, 0.01);
|
|
|
|
var sysOut = new double[step.Length];
|
|
|
|
var errOut = new double[step.Length];
|
|
var errAccOut = new double[step.Length];
|
|
|
|
|
|
for (var i = 0; i < step.Length; i++)
|
|
{
|
|
sysOut[i] = motor.Evaluate(step[i]);
|
|
errOut[i] = stability - sysOut[i];
|
|
errAccOut[i] = errOut[i] - (i == 0 ? 0 : errOut[i - 1]);
|
|
|
|
|
|
|
|
}
|
|
|
|
Debug.WriteLine($"Error Values Min: {errOut.Min()} Max: {errOut.Max()} ");
|
|
Debug.WriteLine($"Error Acc Values Min: {errAccOut.Min()} Max: {errAccOut.Max()} ");
|
|
|
|
formsPlot1.Plot.AddScatter(x, sysOut, Color.Red);
|
|
|
|
formsPlot1.Refresh();
|
|
}
|
|
|
|
private void button4_Click(object sender, EventArgs e)
|
|
{
|
|
formsPlot1.Plot.Clear();
|
|
formsPlot1.Refresh();
|
|
}
|
|
|
|
private void formsPlot1_Load(object sender, EventArgs e)
|
|
{
|
|
|
|
}
|
|
|
|
private void textBox2_TextChanged(object sender, EventArgs e)
|
|
{
|
|
double s;
|
|
if (double.TryParse(textBox2.Text, out s))
|
|
stability = s;
|
|
}
|
|
}
|
|
} |