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bresch
103 lines
3.9 KiB
C++
103 lines
3.9 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2020-2021 PX4 Development Team. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in
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* the documentation and/or other materials provided with the
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* distribution.
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* 3. Neither the name PX4 nor the names of its contributors may be
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* used to endorse or promote products derived from this software
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* without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
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* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
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* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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****************************************************************************/
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/**
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* @file system_identification.hpp
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*
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* @author Mathieu Bresciani <mathieu@auterion.com>
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*/
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#pragma once
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#include <lib/mathlib/math/filter/AlphaFilter.hpp>
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#include <matrix/matrix/math.hpp>
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#include <mathlib/mathlib.h>
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#include <mathlib/math/filter/LowPassFilter2p.hpp>
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#include <px4_platform_common/defines.h>
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#include "arx_rls.hpp"
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class SystemIdentification final
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{
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public:
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SystemIdentification() = default;
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~SystemIdentification() = default;
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void reset(const matrix::Vector<float, 5> &id_state_init = {});
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void update(float u, float y); // update filters and model
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void update(); // update model only (to be called after updateFilters)
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void updateFilters(float u, float y);
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bool areFiltersInitialized() const { return _are_filters_initialized; }
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void updateFitness();
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const matrix::Vector<float, 5> &getCoefficients() const { return _rls.getCoefficients(); }
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const matrix::Vector<float, 5> getVariances() const { return _rls.getVariances(); }
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const matrix::Vector<float, 5> &getDiffEstimate() const { return _rls.getDiffEstimate(); }
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float getFitness() const { return _fitness_lpf.getState(); }
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float getInnovation() const { return _rls.getInnovation(); }
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void setLpfCutoffFrequency(float sample_freq, float cutoff)
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{
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_u_lpf.set_cutoff_frequency(sample_freq, cutoff);
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_y_lpf.set_cutoff_frequency(sample_freq, cutoff);
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}
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void setHpfCutoffFrequency(float sample_freq, float cutoff) { _alpha_hpf = sample_freq / (sample_freq + 2.f * M_PI_F * cutoff); }
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void setForgettingFactor(float time_constant, float dt) { _rls.setForgettingFactor(time_constant, dt); }
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void setFitnessLpfTimeConstant(float time_constant, float dt)
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{
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_fitness_lpf.setParameters(dt, time_constant);
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_dt = dt;
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}
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float getFilteredInputData() const { return _u_hpf; }
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float getFilteredOutputData() const { return _y_hpf; }
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private:
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ArxRls<2, 2, 1> _rls;
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math::LowPassFilter2p<float> _u_lpf{400.f, 30.f};
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math::LowPassFilter2p<float> _y_lpf{400.f, 30.f};
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//TODO: replace by HighPassFilter class
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float _alpha_hpf{0.f};
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float _u_hpf{0.f};
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float _y_hpf{0.f};
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float _u_prev{0.f};
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float _y_prev{0.f};
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bool _are_filters_initialized{false};
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AlphaFilter<float> _fitness_lpf;
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float _dt{0.1f};
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};
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