Lineal Quadratic Regulator
In this short tutorial, we explain how to use Riccati’s theory to solve an LQ control problem with targets. The related MATLAB code is downloadable freely. We start considering the case of finite time horizon to later address the case of infinite time horizon.
Finite time horizon
We consider the optimal control problem:
In the above control problem, , , and . The control , while the state . The control target is and the state target is . and are positive parameters.
By the Direct Methods in the Calculus of Variations and strict convexity, the above problem admits an unique optimal control.
For further details regarding the algorithm, we refer to RiccatiAlgorithm.pdf.
Since the parameter is large enough and the control acts only on the first component of the state equation
- the first component of the state is close to the target;
- the second component of the state is less close to the target;
- the control is far from its target.
- the targets are constants;
- (A,B) is controllable;
- (A,C) is observable, and ;
- the time horizon T is large enough.
Infinite time horizon
We consider the optimal control problem
In the above control problem, , and The control , while the state . The control target is and the state target is . and are positive parameters.
- the targets and satisfies the equation
- (A,B) is controllable and (A,C) is observable.
The above assumptions guarantee the existence of a control such that . By the Direct Methods in the Calculus of Variations and strict convexity, the above problem admits an unique optimal control.
Further details are available in the second section of RiccatiAlgorithm.pdf.
- Dario Pighin
 A. PORRETTA and E. ZUAZUA, Long time versus steady state optimal control, SIAM Journal on Control and Optimization, 51 (2013), pp. 4242–4273.
 E. TRÉLAT, Contrôle optimal: théorie & applications, Vuibert, 2008.
 E. TRÉLAT and E. ZUAZUA, The turnpike property in finite-dimensional nonlinear optimal control, Journal of Differential Equations, 258 (2015), pp. 81–114.
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 694126-DyCon).