Sparse Linear-Quadratic Feedback Design Using Affine Approximation
For control engineers seeking sparse feedback controllers, this work offers a computationally tractable approach with competitive results, though it is incremental over existing ℓ1-based methods.
The paper tackles the NP-hard problem of sparse linear-quadratic regulator design by proposing an iterative algorithm based on ℓ1-relaxation and affine approximation. Numerical experiments show the algorithm achieves performance and sparsity comparable or superior to existing methods.
We consider a class of $\ell_0$-regularized linear-quadratic (LQ) optimal control problems. This class of problems is obtained by augmenting a penalizing sparsity measure to the cost objective of the standard linear-quadratic regulator (LQR) problem in order to promote sparsity pattern of the state feedback controller. This class of problems is generally NP hard and computationally intractable. First, we apply a $\ell_1$-relaxation and consider the $\ell_1$-regularized LQ version of this class of problems, which is still nonconvex. Then, we convexify the resulting $\ell_1$-regularized LQ problem by applying affine approximation techniques. An iterative algorithm is proposed to solve the $\ell_1$-regularized LQ problem using a series of convexified $\ell_1$-regularized LQ problems. By means of several numerical experiments, we show that our proposed algorithm is comparable to the existing algorithms in the literature, and in some cases it even returns solutions with superior performance and sparsity pattern.