Adaptive stepsize seems to be working

src/controller.rs

@@ -0,0 +1,90 @@
+use super::ode::SystemTrait;
+use num_traits::Float;
+
+pub trait Controller<T,F, const D: usize>
+where
+    f64: From<T>,
+    F: SystemTrait<T,D>,
+    T: Copy + From<f64>,
+{
+    fn determine_step(&mut self, h: T, err: T) -> (bool, T);
+}
+
+#[derive(Debug, Clone, Copy)]
+pub struct PIController<T> where f64: From<T> {
+    pub alpha: T,
+    pub beta: T,
+    pub factor_c1: T,
+    pub factor_c2: T,
+    pub factor_old: T,
+    pub h_max: T,
+    pub safety_factor: T,
+    pub old_h: T,
+}
+
+impl<T,F,const D:usize> Controller<T,F,D> for PIController<T>
+where
+    f64: From<T>,
+    F: SystemTrait<T,D>,
+    T: Copy + From<f64> + Float,
+{
+    /// Determines if the previously run step size and error were valid or not. Either way, it also
+    /// returns what the next step size should be
+    fn determine_step(&mut self, h: T, err: T) -> (bool, T) {
+        let factor_11 = err.powf(self.alpha);
+        let factor = self.factor_c2.max(self.factor_c1.min(factor_11 * self.factor_old.powf(-self.beta) / self.safety_factor));
+        let mut h_new = h / factor;
+        if err <= 1.0.into() {
+            // Accept the stepsize
+            self.factor_old = err.max(1.0e-4.into());
+            if h_new.abs() > self.h_max {
+                // If the step is too big
+                h_new = self.h_max.copysign(h_new);
+            }
+            (true, h_new)
+        } else {
+            // Reject the stepsize and propose a smaller one
+            h_new = h / (self.factor_c1.min(factor_11 / self.safety_factor));
+            (false, h_new)
+        }
+    }
+}
+
+impl<T> PIController<T>
+where
+    f64: From<T>,
+    T: Copy + From<f64> + Float,
+{
+    pub fn new(alpha:T, beta:T, max_factor: T, min_factor: T, h_max: T, safety_factor: T, initial_h: T) -> Self {
+        Self {
+            alpha: alpha,
+            beta: beta,
+            factor_c1: <T as From<T>>::from(1.0.into()) / min_factor,
+            factor_c2: <T as From<T>>::from(1.0.into()) / max_factor,
+            factor_old: 1.0e-4.into(),
+            h_max: h_max.abs(),
+            safety_factor: safety_factor,
+            old_h: initial_h,
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_controller_creation() {
+        let controller = PIController::new(0.17, 0.04, 10.0, 0.2, 10.0, 0.9, 1e-4);
+
+        assert!(controller.alpha == 0.17);
+        assert!(controller.beta == 0.04);
+        assert!(controller.factor_c1 == 1.0/0.2);
+        assert!(controller.factor_c2 == 1.0/10.0);
+        assert!(controller.factor_old == 1.0e-4);
+        assert!(controller.h_max == 10.0);
+        assert!(controller.safety_factor == 0.9);
+        assert!(controller.old_h == 1e-4);
+        // assert!(controller.determine_step(0.1, 0.1) == (true, 0.01));
+    }
+}

src/integrator.rs

@@ -0,0 +1,392 @@
+use num_traits::identities;
+use num_traits::Float;
+use std::cmp::PartialEq;
+use std::fmt::Debug;
+use std::ops::{Add, AddAssign, Sub, SubAssign, Mul, MulAssign};
+use nalgebra::{SVector, SMatrix};
+use nalgebra as na;
+
+use super::ode::{ODE, SystemTrait};
+
+#[derive(Debug, Clone, Copy)]
+pub struct Tableau<T, const D: usize> {
+    a: SMatrix<T,D,D>,
+    b: SVector<T,D>,
+    b_star: Option<SVector<T,D>>,
+    c: SVector<T,D>,
+}
+
+
+/// Integrator Trait
+///
+/// T = The value type, usually f64, but must at least implement to/from f64
+/// F = A type that implements System<T,D1>
+/// D1 = The dimension of the system
+/// D2 = The dimension of the butcher tableau (the order of the integrator)
+pub trait Integrator<T,F, const D1: usize, const D2: usize>
+where
+    f64: From<T>,
+    F: SystemTrait<T,D1>,
+    T: Copy + From<f64> + identities::One + identities::Zero + PartialEq + Debug + Add + AddAssign + Sub + SubAssign + Mul + MulAssign + 'static,
+{
+    /// Returns the tableau
+    fn tableau(&self) -> Tableau<T,D2>;
+
+    fn _step1(&self, ode: &ODE<T, F, D1>, h: T) -> (SVector<T,D1>, Option<SVector<T,D1>>) {
+        let k0 = ode.f.derivative(ode.t, ode.y);
+        let sum = k0 * self.tableau().b[0];
+        (ode.y + (sum * h), None)
+    }
+
+    fn _step2(&self, ode: &ODE<T, F, D1>, h: T) -> (SVector<T,D1>, Option<SVector<T,D1>>) {
+        let k0 = ode.f.derivative(ode.t, ode.y);
+        let k1 = ode.f.derivative(
+            ode.t + self.tableau().c[1] * h,
+            ode.y + k0 * self.tableau().a[(1,0)] * h,
+        );
+        let sum =
+            k0 * self.tableau().b[0] +
+            k1 * self.tableau().b[1];
+        let err = match self.tableau().b_star {
+            Some(b_star) => Some(sum + (k0 * b_star[0] +
+                                        k1 * b_star[1]) * T::from(-1.0_f64)),
+            None => None
+        };
+        (ode.y + (sum * h), err)
+    }
+
+    fn _step3(&self, ode: &ODE<T, F, D1>, h: T) -> (SVector<T,D1>, Option<SVector<T,D1>>) {
+        let k0 = ode.f.derivative(ode.t, ode.y);
+        let k1 = ode.f.derivative(
+            ode.t + self.tableau().c[1] * h,
+            ode.y + k0 * self.tableau().a[(1,0)] * h,
+        );
+        let k2 = ode.f.derivative(
+            ode.t + self.tableau().c[2] * h,
+            ode.y + (
+                k0 * self.tableau().a[(2,0)] +
+                k1 * self.tableau().a[(2,1)]
+                ) * h,
+        );
+        let sum =
+            k0 * self.tableau().b[0] +
+            k1 * self.tableau().b[1] +
+            k2 * self.tableau().b[2];
+        let err = match self.tableau().b_star {
+            Some(b_star) => Some(sum + (k0 * b_star[0] +
+                                        k1 * b_star[1] +
+                                        k2 * b_star[2]) * T::from(-1.0_f64)),
+            None => None
+        };
+        (ode.y + (sum * h), err)
+    }
+
+    fn _step4(&self, ode: &ODE<T, F, D1>, h: T) -> (SVector<T,D1>, Option<SVector<T,D1>>) {
+        let k0 = ode.f.derivative(ode.t, ode.y);
+        let k1 = ode.f.derivative(
+            ode.t + self.tableau().c[1] * h,
+            ode.y + k0 * self.tableau().a[(1,0)] * h,
+        );
+        let k2 = ode.f.derivative(
+            ode.t + self.tableau().c[2] * h,
+            ode.y + (
+                k0 * self.tableau().a[(2,0)] +
+                k1 * self.tableau().a[(2,1)]
+                ) * h,
+        );
+        let k3 = ode.f.derivative(
+            ode.t + self.tableau().c[3] * h,
+            ode.y + (
+                k0 * self.tableau().a[(3,0)] +
+                k1 * self.tableau().a[(3,1)] +
+                k2 * self.tableau().a[(3,2)]
+                ) * h,
+        );
+        let sum =
+            k0 * self.tableau().b[0] +
+            k1 * self.tableau().b[1] +
+            k2 * self.tableau().b[2] +
+            k3 * self.tableau().b[3];
+        let err = match self.tableau().b_star {
+            Some(b_star) => Some(sum + (k0 * b_star[0] +
+                                        k1 * b_star[1] +
+                                        k2 * b_star[2] +
+                                        k3 * b_star[3]) * T::from(-1.0_f64)),
+            None => None
+        };
+        (ode.y + (sum * h), err)
+    }
+
+    fn _step5(&self, ode: &ODE<T, F, D1>, h: T) -> (SVector<T,D1>, Option<SVector<T,D1>>) {
+        let k0 = ode.f.derivative(ode.t, ode.y);
+        let k1 = ode.f.derivative(
+            ode.t + self.tableau().c[1] * h,
+            ode.y + k0 * self.tableau().a[(1,0)] * h,
+        );
+        let k2 = ode.f.derivative(
+            ode.t + self.tableau().c[2] * h,
+            ode.y + (
+                k0 * self.tableau().a[(2,0)] +
+                k1 * self.tableau().a[(2,1)]
+                ) * h,
+        );
+        let k3 = ode.f.derivative(
+            ode.t + self.tableau().c[3] * h,
+            ode.y + (
+                k0 * self.tableau().a[(3,0)] +
+                k1 * self.tableau().a[(3,1)] +
+                k2 * self.tableau().a[(3,2)]
+                ) * h,
+        );
+        let k4 = ode.f.derivative(
+            ode.t + self.tableau().c[4] * h,
+            ode.y + (
+                k0 * self.tableau().a[(4,0)] +
+                k1 * self.tableau().a[(4,1)] +
+                k2 * self.tableau().a[(4,2)] +
+                k3 * self.tableau().a[(4,3)]
+                ) * h,
+        );
+        let sum =
+            k0 * self.tableau().b[0] +
+            k1 * self.tableau().b[1] +
+            k2 * self.tableau().b[2] +
+            k3 * self.tableau().b[3] +
+            k4 * self.tableau().b[4];
+        let err = match self.tableau().b_star {
+            Some(b_star) => Some(sum + (k0 * b_star[0] +
+                                        k1 * b_star[1] +
+                                        k2 * b_star[2] +
+                                        k3 * b_star[3] +
+                                        k4 * b_star[4]) * T::from(-1.0_f64)),
+            None => None
+        };
+        (ode.y + (sum * h), err)
+    }
+
+    fn _step6(&self, ode: &ODE<T, F, D1>, h: T) -> (SVector<T,D1>, Option<SVector<T,D1>>) {
+        let k0 = ode.f.derivative(ode.t, ode.y);
+        let k1 = ode.f.derivative(
+            ode.t + self.tableau().c[1] * h,
+            ode.y + k0 * self.tableau().a[(1,0)] * h,
+        );
+        let k2 = ode.f.derivative(
+            ode.t + self.tableau().c[2] * h,
+            ode.y + (
+                k0 * self.tableau().a[(2,0)] +
+                k1 * self.tableau().a[(2,1)]
+                ) * h,
+        );
+        let k3 = ode.f.derivative(
+            ode.t + self.tableau().c[3] * h,
+            ode.y + (
+                k0 * self.tableau().a[(3,0)] +
+                k1 * self.tableau().a[(3,1)] +
+                k2 * self.tableau().a[(3,2)]
+                ) * h,
+        );
+        let k4 = ode.f.derivative(
+            ode.t + self.tableau().c[4] * h,
+            ode.y + (
+                k0 * self.tableau().a[(4,0)] +
+                k1 * self.tableau().a[(4,1)] +
+                k2 * self.tableau().a[(4,2)] +
+                k3 * self.tableau().a[(4,3)]
+                ) * h,
+        );
+        let k5 = ode.f.derivative(
+            ode.t + self.tableau().c[5] * h,
+            ode.y + (
+                k0 * self.tableau().a[(5,0)] +
+                k1 * self.tableau().a[(5,1)] +
+                k2 * self.tableau().a[(5,2)] +
+                k3 * self.tableau().a[(5,3)] +
+                k3 * self.tableau().a[(5,4)]
+                ) * h,
+        );
+        let sum =
+            k0 * self.tableau().b[0] +
+            k1 * self.tableau().b[1] +
+            k2 * self.tableau().b[2] +
+            k3 * self.tableau().b[3] +
+            k4 * self.tableau().b[4] +
+            k5 * self.tableau().b[5];
+        let err = match self.tableau().b_star {
+            Some(b_star) => Some(sum + (k0 * b_star[0] +
+                                        k1 * b_star[1] +
+                                        k2 * b_star[2] +
+                                        k3 * b_star[3] +
+                                        k4 * b_star[4] +
+                                        k5 * b_star[5]) * T::from(-1.0_f64)),
+            None => None
+        };
+        (ode.y + (sum * h), err)
+    }
+
+    fn step(&self, ode: &ODE<T, F, D1>, h: T) -> (SVector<T,D1>, Option<T>);
+
+}
+
+pub struct FixedFourthOrder<T, const D2: usize>
+where
+    f64: From<T>,
+    T: Copy + From<f64> + identities::One + identities::Zero + PartialEq + Debug + Add + AddAssign + Sub + SubAssign + Mul + MulAssign + 'static,
+{
+    pub tableau: Tableau<T, D2>,
+}
+
+impl<T,F, const D1: usize, const D2: usize> Integrator<T,F,D1,D2> for FixedFourthOrder<T,D2>
+where
+    f64: From<T>,
+    F: SystemTrait<T,D1>,
+    T: Copy + From<f64> + identities::One + identities::Zero + PartialEq + Debug + Add + AddAssign + Sub + SubAssign + Mul + MulAssign + 'static,
+{
+    fn tableau(&self) -> Tableau<T,D2> { self.tableau }
+
+    fn step(&self, ode: &ODE<T,F,D1>, h: T) -> (SVector<T,D1>, Option<T>) { (self._step4(ode, h).0, None) }
+}
+
+pub struct AdaptiveSixthOrder<T, const D2: usize>
+where
+    f64: From<T>,
+    T: Copy + From<f64> + identities::One + identities::Zero + PartialEq + Debug + Add + AddAssign + Sub + SubAssign + Mul + MulAssign + 'static,
+{
+    pub tableau: Tableau<T, D2>,
+    pub atol: T,
+    pub rtol: T,
+}
+
+impl<T,F, const D1: usize, const D2: usize> Integrator<T,F,D1,D2> for AdaptiveSixthOrder<T,D2>
+where
+    f64: From<T>,
+    F: SystemTrait<T,D1>,
+    T: Copy +
+       From<f64> +
+       identities::One +
+       identities::Zero +
+       PartialEq +
+       Debug +
+       Add +
+       AddAssign +
+       Sub +
+       SubAssign +
+       Mul +
+       MulAssign +
+       Float +
+       'static,
+{
+    fn tableau(&self) -> Tableau<T,D2> { self.tableau }
+
+    fn step(&self, ode: &ODE<T,F,D1>, h: T) -> (SVector<T,D1>, Option<T>) {
+        let (next_y, err_option) = self._step6(ode, h);
+        let mut err: T = 0.0.into();
+        let err_array = err_option.unwrap();
+        ode.y.iter().zip(next_y.iter()).enumerate().for_each(|(i, (yi, next_yi))| {
+            let tol = self.atol + yi.abs().max(next_yi.abs()) * self.rtol;
+            err += (err_array[i] / tol).powi(2);
+        });
+        (next_y, Some(err))
+    }
+}
+
+pub const RUNGE_KUTTA_4_TABLEAU: Tableau<f64, 4> = Tableau {
+    a: na::Matrix4::new(
+           0.0, 0.0, 0.0, 0.0,
+           0.5, 0.0, 0.0, 0.0,
+           0.0, 0.5, 0.0, 0.0,
+           0.0, 0.0, 1.0, 0.0,
+           ),
+    b: na::Vector4::new(1.0/6.0, 1.0/3.0, 1.0/3.0, 1.0/6.0),
+    b_star: None,
+    c: na::Vector4::new(0.0, 0.5, 0.5, 1.0),
+};
+
+pub const RUNGE_KUTTA_FEHLBERG_54_TABLEAU: Tableau<f64, 6> = Tableau {
+    a: na::Matrix6::new(
+           0.0,           0.0,            0.0,            0.0,           0.0,        0.0,
+           0.25,          0.0,            0.0,            0.0,           0.0,        0.0,
+           3.0/32.0,      9.0/32.0,       0.0,            0.0,           0.0,        0.0,
+           1932.0/2197.0, -7200.0/2197.0, 7296.0/2197.0,  0.0,           0.0,        0.0,
+           439.0/216.0,   -8.0,           3680.0/513.0,   -845.0/4104.0, 0.0,        0.0,
+           -8.0/27.0,     2.0,            -3544.0/2565.0, 1859.0/4104.0, -11.0/40.0, 0.0,
+           ),
+    b: na::Vector6::new(
+        16.0/135.0,
+        0.0,
+        6656.0/12825.0,
+        28561.0/56430.0,
+        -9.0/50.0,
+        2.0/55.0,
+        ),
+    b_star: Some(
+        na::Vector6::new(
+            25.0/216.0,
+            0.0,
+            1408.0/2565.0,
+            2197.0/4104.0,
+            -1.0/5.0,
+            0.0,
+        )
+    ),
+    c: na::Vector6::new(0.0, 0.25, 3.0/8.0, 12.0/13.0, 1.0, 0.5),
+};
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use nalgebra::Vector3;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn test_rk4() {
+        struct System {}
+
+        impl SystemTrait<f64,3> for System {
+            fn derivative(&self, _t: f64, y: Vector3<f64>) -> Vector3<f64> { y }
+        }
+
+        let system = System {};
+        let y0 = Vector3::new(1.0, 1.0, 1.0);
+        let mut ode = ODE::new(system, 0.0, 10.0, y0);
+
+        let rk4 = FixedFourthOrder { tableau: RUNGE_KUTTA_4_TABLEAU };
+
+        // Test that y'(t) = y(t) solves to y(t) = e^t for rk4
+        let step = 0.001;
+        while ode.t < ode.t_end {
+            ode.y = rk4.step(&ode, step).0;
+            ode.t += step;
+            assert_relative_eq!(ode.y[0], ode.t.exp(), max_relative=0.000001);
+        }
+    }
+
+    #[test]
+    fn test_rkf54() {
+        struct System {}
+
+        impl SystemTrait<f64,3> for System {
+            fn derivative(&self, _t: f64, y: Vector3<f64>) -> Vector3<f64> { y }
+        }
+
+        let system = System {};
+        let y0 = Vector3::new(1.0, 1.0, 1.0);
+        let mut ode = ODE::new(system, 0.0, 10.0, y0);
+
+        let rkf54 = AdaptiveSixthOrder {
+            tableau: RUNGE_KUTTA_FEHLBERG_54_TABLEAU,
+            atol: 1e-3_f64,
+            rtol: 1e-3_f64,
+        };
+
+        // Test that y'(t) = y(t) solves to y(t) = e^t for rkf54
+        // and also that the error seems reasonable
+        let step = 0.001;
+        while ode.t < ode.t_end {
+            let (new_y, err) = rkf54.step(&ode, step);
+            ode.y = new_y;
+            ode.t += step;
+            assert_relative_eq!(ode.y[0], ode.t.exp(), max_relative=0.000001);
+            assert!(err.unwrap() < 0.0001);
+        }
+    }
+}

src/lib.rs

@@ -1,14 +1,48 @@
-pub fn add(left: usize, right: usize) -> usize {
-    left + right
-}
+#![allow(dead_code)]
+
+pub mod ode;
+pub mod integrator;
+pub mod controller;
+// pub mod callback;
+pub mod problem;
+
 
 #[cfg(test)]
 mod tests {
-    use super::*;
+    use nalgebra::Vector6;
+    use approx::assert_relative_eq;
+    use crate::integrator::{AdaptiveSixthOrder, RUNGE_KUTTA_FEHLBERG_54_TABLEAU};
+    use crate::controller::{PIController};
+    use crate::ode::{SystemTrait, ODE};
+    use crate::problem::Problem;
 
     #[test]
-    fn it_works() {
-        let result = add(2, 2);
-        assert_eq!(result, 4);
+    fn test_ode_creation() {
+        struct System {
+            mu: f64,
+        }
+
+        impl SystemTrait<f64,6> for System {
+            fn derivative(&self, _t: f64, state: Vector6<f64>) -> Vector6<f64> {
+                let acc = -(self.mu * state.fixed_rows<3>(0)) / (state.fixed_rows<3>(0).norm.powi(3));
+                Vector6::new(state[3], state[4], state[5], acc[0], acc[1], acc[2])
+            }
+        }
+
+        let system = System { mu: 3.98600441500000e14 };
+        let y0 = Vector3::new(1.0, 0.0, 0.0);
+        let ode = ODE::new(system, 0.0, 10.0, y0);
+        let controller = PIController::new(0.17, 0.04, 10.0, 0.2, 10.0, 0.9, 1e-4);
+        let rkf54 = AdaptiveSixthOrder {
+            tableau: RUNGE_KUTTA_FEHLBERG_54_TABLEAU,
+            atol: 1e-8_f64,
+            rtol: 1e-8_f64,
+        };
+        let mut problem = Problem::new(ode, rkf54, controller);
+
+        let solution = problem.solve();
+        solution.times.iter().zip(solution.states.iter()).for_each(|(time, state)| {
+            assert_relative_eq!(state[0], time.exp(), max_relative=1e-7);
+        })
     }
 }

src/ode.rs

@@ -0,0 +1,67 @@
+use nalgebra::SVector;
+
+/// A System trait.
+///
+/// The user will have to define their own system. They are free to add params to their system
+/// definition and use those in the derivative function
+pub trait SystemTrait<T,const D: usize> {
+    fn derivative(&self, t: T, y: SVector<T,D>) -> SVector<T,D>;
+}
+
+/// The basic ODE object that will be passed around. The type (T) and the size (D) will be
+/// determined upon creation of the object
+#[derive(Debug, Clone, Copy)]
+pub struct ODE<T,F, const D: usize> where F: SystemTrait<T,D>{
+    pub f: F,
+    pub y: SVector<T,D>,
+    pub t: T,
+    pub t0: T,
+    pub t_end: T,
+    pub h: T,
+    pub finished: bool,
+}
+
+impl<T, F, const D: usize> ODE<T,F,D>
+where
+    F: SystemTrait<T,D>,
+    T: Copy + From<f64>,
+    f64: From<T>,
+{
+    pub fn new(f: F, t0: T, t_end: T, y0: SVector<T,D>) -> Self {
+        Self {
+            f: f,
+            y: y0,
+            t: t0,
+            t0: t0,
+            t_end: t_end,
+            h: 0.001.into(),
+            finished: false,
+        }
+    }
+}
+
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use nalgebra::Vector3;
+
+    #[test]
+    fn test_ode_creation() {
+        struct System {}
+
+        impl SystemTrait<f64,3> for System {
+            fn derivative(&self, _t: f64, y: Vector3<f64>) -> Vector3<f64> { -y }
+        }
+
+        let system = System {};
+        let y0 = Vector3::new(1.0, 0.0, 0.0);
+        let ode = ODE::new(system, 0.0, 10.0, y0);
+
+        assert!(ode.f.derivative(0.0, y0) == Vector3::new(-1.0, 0.0, 0.0));
+        assert!(ode.y == Vector3::new(1.0, 0.0, 0.0));
+        assert!(ode.t == 0.0);
+        assert!(!ode.finished);
+        assert!(ode.t_end == 10.0);
+    }
+}

src/problem.rs

@@ -0,0 +1,138 @@
+use num_traits::identities;
+use num_traits::Float;
+use std::fmt::Debug;
+use std::cmp::PartialOrd;
+use std::ops::{Add, AddAssign, Sub, SubAssign, Mul, MulAssign};
+use nalgebra::SVector;
+
+use super::ode::{ODE, SystemTrait};
+use super::controller::{Controller, PIController};
+use super::integrator::Integrator;
+
+pub struct Problem<T, F, const D1: usize, const D2: usize, S>
+where
+    f64: From<T>,
+    F: SystemTrait<T,D1>,
+    T: Copy +
+       From<f64> +
+       identities::One +
+       identities::Zero +
+       PartialEq +
+       Debug +
+       Add +
+       AddAssign +
+       Sub +
+       SubAssign +
+       Mul +
+       MulAssign +
+       Float +
+       PartialOrd +
+       'static,
+    S: Integrator<T, F, D1, D2>,
+{
+    ode: ODE<T,F,D1>,
+    integrator: S,
+    controller: PIController<T>,
+}
+
+impl<T, F, const D1: usize, const D2: usize, S> Problem<T,F,D1,D2,S>
+where
+    f64: From<T>,
+    F: SystemTrait<T,D1>,
+    T: Copy +
+       From<f64> +
+       identities::One +
+       identities::Zero +
+       PartialEq +
+       Debug +
+       Add +
+       AddAssign +
+       Sub +
+       SubAssign +
+       Mul +
+       MulAssign +
+       Float +
+       PartialOrd +
+       'static,
+    S: Integrator<T, F, D1, D2>,
+{
+    pub fn new(ode: ODE<T,F,D1>, integrator: S, controller: PIController<T>) -> Self {
+        Problem {
+            ode: ode,
+            integrator: integrator,
+            controller: controller,
+        }
+    }
+    pub fn solve(&mut self) -> Solution<T, D1> {
+        let mut times: Vec::<T> = Vec::new();
+        let mut states: Vec::<SVector<T,D1>> = Vec::new();
+        let mut step: T = self.controller.old_h;
+        while self.ode.t < self.ode.t_end {
+            let (mut new_y, mut err_option) = self.integrator.step(&self.ode, step);
+            times.push(self.ode.t);
+            states.push(self.ode.y);
+            match err_option {
+                Some(mut err) => {
+                    // Adaptive Step Size
+                    let mut accepted: bool = false;
+                    while !accepted {
+                        (accepted, step) = <PIController<T> as Controller<T, F, D1>>::determine_step(&mut self.controller, step, err);
+                        (new_y, err_option) = self.integrator.step(&self.ode, step);
+                        err = err_option.unwrap();
+                    }
+                    self.controller.old_h = step;
+                    self.ode.y = new_y;
+                    self.ode.t += step;
+                },
+                None => {
+                    // Fixed Step Size
+                    self.ode.y = new_y;
+                    self.ode.t += self.controller.old_h;
+                },
+            };
+        }
+        Solution {
+            times: times,
+            states: states,
+        }
+    }
+}
+
+pub struct Solution<T,const D: usize> {
+    pub times: Vec<T>,
+    pub states: Vec<SVector<T,D>>,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use nalgebra::Vector3;
+    use approx::assert_relative_eq;
+    use crate::integrator::{AdaptiveSixthOrder, RUNGE_KUTTA_FEHLBERG_54_TABLEAU};
+    use crate::controller::{PIController};
+
+    #[test]
+    fn test_ode_creation() {
+        struct System {}
+
+        impl SystemTrait<f64,3> for System {
+            fn derivative(&self, _t: f64, y: Vector3<f64>) -> Vector3<f64> { y }
+        }
+
+        let system = System {};
+        let y0 = Vector3::new(1.0, 0.0, 0.0);
+        let ode = ODE::new(system, 0.0, 10.0, y0);
+        let controller = PIController::new(0.17, 0.04, 10.0, 0.2, 10.0, 0.9, 1e-4);
+        let rkf54 = AdaptiveSixthOrder {
+            tableau: RUNGE_KUTTA_FEHLBERG_54_TABLEAU,
+            atol: 1e-8_f64,
+            rtol: 1e-8_f64,
+        };
+        let mut problem = Problem::new(ode, rkf54, controller);
+
+        let solution = problem.solve();
+        solution.times.iter().zip(solution.states.iter()).for_each(|(time, state)| {
+            assert_relative_eq!(state[0], time.exp(), max_relative=1e-7);
+        })
+    }
+}