Iterative State- and Control-Dependent Model Predictive Control: A Jacobian-Free Formulation for Constrained Nonlinear Systems

Koopman-based model predictive control for nonlinear systems with bounded model uncertainty

Carleman approximation based adaptive optimal control design of nonlinear systems: A three-phase policy iteration approach.

As one efficient algorithm in adaptive dynamic programming to address adaptive optimal control problems, policy iteration always involves an initial admissible control guess during iteration. Such an initialization process is nontrivial, especially for unstable systems or when system dynamics are totally unknown. To circumvent this limitation, this paper develops a novel adaptive optimal controller design scheme for continuous-time nonlinear systems through neural network-based policy iteration. By employing Carleman linearization, we lift the nonlinear model into a bilinear form, which allows the optimal feedback control to be derived from a state-dependent Riccati (SDR) equation rather than the Hamilton-Jacobi-Bellman equation. To solve the SDR equation under unknown system parameters, we leverage a homotopy-based three-phase policy iteration algorithm that learns the optimal control protocol directly from state and input measurements. In comparison to existing results, the proposed algorithm has the ability to automatically update the initial parameters and specifies the same basis functions of neural networks to approximate all unknown functions, which gives rise to computational improvement. Two case studies are provided to validate our methodology.

Verifying performance, stability, and feasibility of inexact non-linear model predictive controllers

We introduce a verification framework to numerically analyze inexact model predictive controllers (MPCs) in the constrained non-linear discrete-time setting. Rather than modifying the controller so that guarantees hold by construction, we treat the controller as given. In particular, we focus on two types of inexact controllers: (a) one whose input is extracted from a primal-dual point satisfying the Karush-Kuhn-Tucker (KKT) conditions of the non-convex MPC problem, and (b) one whose input is obtained by linearizing the dynamics and solving a convex quadratic program. The main idea of our verification framework is to formulate an optimization problem that searches over the worst-case initial state within a given set and control inputs consistent with the inexact controller to maximize a carefully-chosen performance metric. Using this framework, we show how to certify (i) the worst-case suboptimality gap of a single MPC problem, (ii) the worst-case closed-loop suboptimality gap over a given number of dynamical system iterations, (iii) closed-loop stability, and (iv) feasibility of the closed-loop system. Through numerical examples, we showcase the ability of our framework to precisely quantify both types of suboptimality, and to test the stability and feasibility of the inexact controllers.

Bilinear Koopman-Based Robust Model Predictive Control for Unknown Nonlinear Systems via Contraction Metrics

A RMPC framework for unknown nonlinear systems with general nonlinear constraints based on data-driven bilinear Koopman realizations is proposed and robust satisfaction of the original nonlinear constraints is proved by the true closed-loop trajectory, recursive feasibility, and convergence to a neighborhood of the target state.

Distributed model predictive control via finite-step control Lyapunov functions

As opposed to classical converse Lyapunov theorems, finite-step converse results are constructive and offer a different starting point: for sufficiently large finite step ahead, say $M$, in an explicit sense, any scaled norm can serve as a converse finite-step Lyapunov function. As for interconnected discrete-time systems, similar lines of argument lead to ``non-conservative''small-gain conditions. Motivated by this viewpoint, this paper develops a distributed model predictive control framework for constrained interconnected nonlinear discrete-time systems. Each subsystem solves one local optimization problem at each system time step instant by setting the local stage function in form of a local control finite-step like Lyapunov function, using time-aligned neighbor predictions, optimized state-constraint tightening radii, and a finite-step small-gain terminal inequality. Since received neighbor predictions need not equal the trajectories generated by future receding-horizon optimizations, the nominal converse certificates do not alone ensure recursive feasibility or stability. We therefore develop shift-compatible constraint margins, a local one-step terminal feasibility test for networks that are affine in control, and an analytical bound for the prediction and reoptimization mismatch. The resulting analysis gives recursive feasibility, constraint satisfaction, and a practical $M$-step Lyapunov estimate, with asymptotic convergence when the prediction and reoptimization mismatch bound tends to zero. For constrained linear networks, the conditions reduce to finite-dimensional matrix, QP, and SOCP tests. The framework is specialized to current sharing and terminal bus voltage safety under DC/DC power converters'operational constraints in a two-DGU DC microgrid evaluated on a small laboratory-scale prototype.

Parallel Policy-Gradient Methods for Parameter Optimization of Nonlinear Feedback Controllers

Structured feedback controllers provide rigorous stability guarantees, but often require manual parameter tuning to achieve good closed-loop performance. Policy-gradient methods offer a systematic approach to parameter optimization; however, conventional gradient evaluation requires sequential forward state rollout and backward costate propagation. This letter develops a time-parallel policy-gradient framework for discrete-time nonlinear control-affine systems. We derive the policy-gradient expression where the state and costate rollouts required for policy-gradient evaluation are formulated as residual-minimization problems and solved using Gauss-Newton (GN) iterations with parallel associative scans. For closed-loop systems that are globally asymptotically stable and locally exponentially stable, we show that the residual-minimization problems satisfy a local Polyak-Lojasiewicz (PL) inequality and that the GN iterates converge locally at a quadratic rate. Moreover, the PL constant, the size of the convergence neighborhood, and the quadratic convergence bound are independent of the rollout horizon T. We also prove that, for any finite horizon T, the state solver recovers the exact trajectory from any initialization in at most T iterations. Finally, an inertia-wheel pendulum example with interconnection and damping assignment passivity-based control (IDA-PBC) demonstrates improved closed-loop performance and the computational benefits of the proposed parallel policy-gradient framework.