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| Original file line number | Diff line number | Diff line change |
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| # mpcpy requirements.txt | ||
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| numpy | ||
| numpydoc | ||
| numpydoc | ||
| pyomo |
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| #!/usr/bin/env python | ||
| ################################################################################ | ||
| # Copyright 2015 Brecht Baeten | ||
| # This file is part of mpcpy. | ||
| # | ||
| # mpcpy is free software: you can redistribute it and/or modify | ||
| # it under the terms of the GNU General Public License as published by | ||
| # the Free Software Foundation, either version 3 of the License, or | ||
| # (at your option) any later version. | ||
| # | ||
| # mpcpy is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| # GNU General Public License for more details. | ||
| # | ||
| # You should have received a copy of the GNU General Public License | ||
| # along with mpcpy. If not, see <http://www.gnu.org/licenses/>. | ||
| ################################################################################ | ||
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| import numpy as np | ||
| import mpcpy | ||
| import pyomo.environ as pyomo | ||
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| # Define an emulator class | ||
| class Emulator(mpcpy.Emulator): | ||
| """ | ||
| A custom system emulator | ||
| """ | ||
| def simulate(self,starttime,stoptime,input): | ||
| dt = 1 | ||
| time = np.arange(starttime,stoptime+dt,dt,dtype=np.float) | ||
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| # initialize | ||
| x = np.ones_like(time)*self.res['x'][-1] | ||
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| # interpolate inputs | ||
| u = np.interp(time,input['time'],input['u']) | ||
| d = np.interp(time,input['time'],input['d']) | ||
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| # perform simulation | ||
| for i,t in enumerate(time[:-1]): | ||
| # dx/dt = A*x + d + u | ||
| x[i+1] = x[i] + ( self.parameters['A']*x[i] + d[i] + u[i] )*dt | ||
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| # create and return a results dict | ||
| res = { | ||
| 'time': time, | ||
| 'x': x, | ||
| 'd': d, | ||
| 'u': u, | ||
| } | ||
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| return res | ||
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| # Define a control class | ||
| class SetpointControl(mpcpy.Control): | ||
| """ | ||
| A control to keep the state as close to a set point as possible | ||
| """ | ||
| def formulation(self): | ||
| # create a pyomo model | ||
| model = pyomo.AbstractModel() | ||
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| model.i = pyomo.Set() | ||
| model.ip = pyomo.Set() | ||
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| model.time = pyomo.Param(model.ip) | ||
| model.d = pyomo.Param(model.ip, initialize=0.) | ||
| model.x = pyomo.Var(model.ip, domain=pyomo.Reals, initialize=0.) | ||
| model.u = pyomo.Var(model.ip, domain=pyomo.NonNegativeReals, bounds=(0.,1.), initialize=0.) | ||
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| model.x0 = pyomo.Param(initialize=0.) | ||
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| model.initialcondition = pyomo.Constraint( | ||
| rule=lambda model: model.x[0]==model.x0 | ||
| ) | ||
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| model.constraint = pyomo.Constraint( | ||
| model.i, | ||
| rule=lambda model,i: (model.x[i+1]-model.x[i])/(model.time[i+1]-model.time[i]) == \ | ||
| self.parameters['A']*model.x[i] + model.d[i] + model.u[i] | ||
| ) | ||
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| model.objective = pyomo.Objective( | ||
| rule=lambda model: sum( (model.x[i]-self.parameters['set'])**2 for i in model.i) | ||
| ) | ||
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| # store the model inside the object | ||
| self.model = model | ||
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| def solution(self,sta,pre): | ||
| # create data and instantiate the pyomo model | ||
| ip = np.arange(len(pre['time'])) | ||
| data = { | ||
| None:{ | ||
| 'i':{None: ip[:-1]}, | ||
| 'ip':{None: ip}, | ||
| 'time':{(i,): v for i,v in enumerate(pre['time'])}, | ||
| 'x0':{None: sta['x']}, | ||
| 'd':{(i,): pre['d'][i] for i in ip}, | ||
| } | ||
| } | ||
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| instance = self.model.create_instance(data) | ||
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| # solve and return the contol inputs | ||
| optimizer = pyomo.SolverFactory('cplex') | ||
| results = optimizer.solve(instance) | ||
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| sol = { | ||
| 'time': np.array([pyomo.value(instance.time[i]) for i in instance.ip]), | ||
| 'x': np.array([pyomo.value(instance.x[i]) for i in instance.ip]), | ||
| 'u': np.array([pyomo.value(instance.u[i]) for i in instance.ip]), | ||
| 'd': np.array([pyomo.value(instance.d[i]) for i in instance.ip]), | ||
| } | ||
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| return sol | ||
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| # Define a state estimation class | ||
| class StateestimationPerfect(mpcpy.Stateestimation): | ||
| """ | ||
| Perfect state estimation | ||
| """ | ||
| def stateestimation(self,time): | ||
| return {'x': np.interp(time,self.emulator.res['time'],self.emulator.res['x'])} | ||
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| # instantiate the emulator | ||
| emulator = Emulator(['u','d'],parameters={'A':-0.2},initial_conditions={'x':0}) | ||
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| # test the emulator with some random data | ||
| time = np.arange(0.,1001.,10.) | ||
| d = np.random.random(len(time)) - 0.5 | ||
| u = 1.0*np.ones(len(time)) | ||
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| emulator.initialize() | ||
| res = emulator(time,{'time':time,'d':d,'u':u}) | ||
| print(res) | ||
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| # create a boundaryconditions object | ||
| time = np.arange(0.,1001.,10.) | ||
| d = np.random.random(len(time)) - 0.5 | ||
| boundaryconditions = mpcpy.Boundaryconditions({'time':time,'d':d}) | ||
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| bcs = boundaryconditions(np.array([0,20,40,60,100])) | ||
| print(bcs) | ||
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| # create a stateestimation object | ||
| stateestimation = StateestimationPerfect(emulator) | ||
| sta = stateestimation(0) | ||
| print(sta) | ||
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| # create a prediction object | ||
| prediction = mpcpy.Prediction(boundaryconditions) | ||
| pre = prediction(np.array([0,20,40,60,100])) | ||
| print(pre) | ||
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| # create a control object and mpc object | ||
| control = SetpointControl(stateestimation,prediction,parameters={'A':-0.2,'set':3.0},horizon=100.,timestep=10.,receding=10.) | ||
| mpc = mpcpy.MPC(emulator,control,boundaryconditions,emulationtime=1000,resulttimestep=10) | ||
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| # run the mpc | ||
| res = mpc() | ||
| print(res) |
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