In this paper, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal input) constraints. To facilitate efficient implementation, shape of is based on offline computed incremental Lyapunov function with corresponding (nonlinear) incrementally stabil...

This paper aims to solve the problem of robust model predictive control, contradiction system robustness, and conservative terminal constraint set range. A control (RMPC) method based on an ε-approximation single-tube is proposed. We construct a RMPC structure for linear discrete time invariant systems with additive disturbances. To this structure, we add state estimation feedback improve conve...

In this letter we propose a data-driven distributionally robust Model Predictive Control framework for constrained stochastic systems with unbounded additive disturbances. Recursive feasibility is ensured by optimizing over linearly interpolated initial state constraint in combination simplified affine disturbance feedback policy. We consider moment-based ambiguity set radius the second moment ...

Efficient control of disturbed industrial systems requires methods to handle complex and nondifferentiable performance criteria given by customers directly in the design process. In laws, our works evaluates nonlinear criterion for subject additive disturbances. Model Predictive Control using barrier functions is proposed. First all, stability method proven linear case Lyapunov function invaria...

Abstract This work develops a new robust multi‐objective model predictive control (MoMPC) strategy for constrained non‐linear systems with bounded disturbances. The multiple objectives are always contradictory, and the presence of disturbances may result in violation state constraints instability controlled system. Firstly, conflict between is reconciled by minimizing distance cost function vec...

A tube-based robust economic model predictive control scheme is proposed for sparse, linear storage network systems under bounded disturbances. We present a modeling framework that reflects conservation principles, and which centralized controller optimizes performance metric the entire over finite time horizon. Our main result computationally efficient projection-based approach exploits sparse...