dcdc example refactoring

docs/src/examples/solvers/DC-DC converter.jl

@@ -44,47 +44,38 @@ const AB = OP.Abstraction
 include(joinpath(dirname(dirname(pathof(Dionysos))), "problems", "dc_dc.jl"))
 
 # and we can instantiate the DC system with the provided system
-concrete_problem = DCDC.problem(; approx_mode = DCDC.GROWTH)
-concrete_system = concrete_problem.system
-
-x0 = SVector(0.0, 0.0)
-hx = SVector(2.0 / 4.0e3, 2.0 / 4.0e3)
-state_grid = DO.GridFree(x0, hx)
-u0 = SVector(1)
-hu = SVector(1)
-input_grid = DO.GridFree(u0, hu)
-
-using JuMP
-optimizer = MOI.instantiate(AB.UniformGridAbstraction.Optimizer)
-MOI.set(optimizer, MOI.RawOptimizerAttribute("concrete_problem"), concrete_problem)
-MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)
-MOI.set(optimizer, MOI.RawOptimizerAttribute("input_grid"), input_grid)
-MOI.optimize!(optimizer)
-
-abstract_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_controller"))
-@test length(abstract_controller.data) == 893803 #src
-concrete_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("concrete_controller"))
+problem = DCDC.problem(; approx_mode = DCDC.GROWTH)
+
+controller = problem.solve(
+    method = :uniform_abstraction,
+    initial_state = [0.0, 0.0], 
+    initial_input = [1.], 
+)
+# Didn't work because the default step sizes are too coarse / too small
+problem.set_state_stepsize([2.0 / 4.0e3, 2.0 / 4.0e3])
+problem.set_input_stepsize([1.])
+controller = problem.solve()
+# Now it worked
+
 
 # ### Trajectory display
 # We choose the number of steps `nsteps` for the sampled system, i.e. the total elapsed time: `nstep`*`tstep`
 # as well as the true initial state `x0` which is contained in the initial state-space defined previously.
 nstep = 300
-x0 = SVector(1.2, 5.6)
+x0 = [1.2, 5.6]
 control_trajectory =
-    ST.get_closed_loop_trajectory(concrete_system.f, concrete_controller, x0, nstep)
+    ST.get_closed_loop_trajectory(problem.system.f, controller, x0, nstep)
 
 fig = plot(; aspect_ratio = :equal);
-plot!(concrete_system.X);
+plot!(problem.system.X);
 plot!(control_trajectory)
 
 # # Example: DC-DC converter solved by [Uniform grid abstraction] (https://github.com/dionysos-dev/Dionysos.jl/blob/master/docs/src/manual/manual.md#solvers) by exploiting the incremental stability of the system.
 # ### Definition of the system
 # we can import the module containing the DCDC problem like this 
-include(joinpath(dirname(dirname(pathof(Dionysos))), "problems", "dc_dc.jl"))
 
 # and we can instantiate the DC system with the provided system
-concrete_problem = DCDC.problem(; approx_mode = DCDC.DELTA_GAS)
-concrete_system = concrete_problem.system
+problem = DCDC.problem(; approx_mode = DCDC.DELTA_GAS)
 
 origin = SVector(0.0, 0.0)
 η = (2 / 4.0) * 10^(-3)
@@ -94,30 +85,25 @@ origin = SVector(0.0, 0.0)
 P = SMatrix{2, 2}(1.0224, 0.0084, 0.0084, 1.0031)
 state_grid = DO.GridEllipsoidalRectangular(origin, SVector(η, η), P / ϵ, concrete_system.X)
 
-u0 = SVector(1)
-hu = SVector(1)
-input_grid = DO.GridFree(u0, hu)
-
-optimizer = MOI.instantiate(AB.UniformGridAbstraction.Optimizer)
-MOI.set(optimizer, MOI.RawOptimizerAttribute("concrete_problem"), concrete_problem)
-MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)
-MOI.set(optimizer, MOI.RawOptimizerAttribute("input_grid"), input_grid)
-MOI.set(optimizer, MOI.RawOptimizerAttribute("δGAS"), true)
-MOI.optimize!(optimizer)
-
-abstract_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("abstract_controller"))
-concrete_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("concrete_controller"))
+# Here we give a more complex grid to the solver
+controller = problem.solve(
+    method = :uniform_abstraction, 
+    δ_gas = true, 
+    state_grid = DO.GridEllipsoidalRectangular(origin, SVector(η, η), P / ϵ, concrete_system.X), 
+    initial_input = [1.], 
+    input_stepsize = [1.]
+)
 
 # ### Trajectory display
 # We choose the number of steps `nsteps` for the sampled system, i.e. the total elapsed time: `nstep`*`tstep`
 # as well as the true initial state `x0` which is contained in the initial state-space defined previously.
 nstep = 300
-x0 = SVector(1.2, 5.6)
+x0 = [1.2, 5.6]
 control_trajectory =
-    ST.get_closed_loop_trajectory(concrete_system.f, concrete_controller, x0, nstep)
+    ST.get_closed_loop_trajectory(problem.system.f, controller, x0, nstep)
 
 fig = plot(; aspect_ratio = :equal);
-plot!(concrete_system.X);
+plot!(problem.system.X);
 plot!(control_trajectory)
 
 # ### References