Recently, an efficient multistep direct model predictive control (MPC) scheme for power converters has been proposed. It relies on the Sphere Decoding Algorithm (SDA) to solve the associated long-horizon optimal control problem. Since the SDA evaluates only a small number of candidate solutions to find the optimal one, a significant reduction in the average computational burden can be achieved compared to the basic exhaustive search approach. However, this is only true during steady-state operation. In fact, the SDA still requires a large execution time during transients. This paper shows that if not properly addressed, the dynamic performance of the system may be degraded, which clearly limits its practical application. To mitigate this issue, which particularly arises during transients, an efficient preconditioning approach for the SDA is proposed. This approach ensures that only a small number of candidate solutions are evaluated during both steady-state and transients. This allows the multistep direct MPC to become a viable control alternative for power converters operating at low semiconductor switching frequencies, e.g., below 450 Hz. The proposal is validated using a grid-connected three-level converter as a case study. Both processor-in-the-loop simulations and experimental results on a scaled-down 2.24 kVA laboratory setup are presented.

Automatic control using ultrareliable and low latency communication is one of the potential applications of the new fifth-generation wireless systems. A remaining challenge is then to guarantee a low end-to-end delay with low jitter over combined internet and wireless interfaces that are packet switched and capacity optimized. The main novelty of this paper is to introduce stringent delay control to meet this challenge, over simultaneous multiple data paths. The proposed multiple-input-multiple-output (MIMO) cascade control system is nonlinear since the dwell times of the transmission node queues used as actuators cannot be negative. Stability analysis based on integral quadratic constraint theory is, therefore, applied to characterize the global stability of the controller. The practical performance is evaluated with experiments using product like test bed C++ code. It is stressed that the proposed controller does not require internode time synchronization.

This note addresses the problem of feedback control with a constrained number of active inputs. This problem is known as sparse control. Specifically, we describe a novel quadratic model predictive control strategy that guarantees sparsity by bounding directly the ℓ ₀ -norm of the control input vector at each control horizon instant. Besides this sparsity constraint, bounded constraints are also imposed on both control input and system state. Under this scenario, we provide sufficient conditions for guaranteeing practical stability of the closed-loop. We transform the combinatorial optimization problem into an equivalent optimization problem that does not consider relaxation in the cardinality constraints. The equivalent optimization problem can be solved utilizing standard nonlinear programming toolboxes that provides the input control sequence corresponding to the global optimum.