Tube-based robust economic model predictive control

A hybrid Gaussian process approach to robust economic model predictive control

Homothetic tube-based robust offset-free economic Model Predictive Control

A reinforcement learning-based economic model predictive control framework for autonomous operation of chemical reactors

I0639. Economic model predictive control is considered a suitable strategy to achieve the aforementioned economy goal. However, since the variable wind speed is difficult to measure efficiently, the wind speed error may lead to a series of problem for the traditional control. In this paper, a robust economic model predictive control is developed for variable speed variable pitch wind turbines. A linear feedback controller and corresponding robust control invariant set are designed off-line to ensure the wind turbine system's robustness. Simulation results are given to show the effectiveness of the proposed scheme.

In this paper, the problem of optimal time-varying attitude tracking control for rigid spacecraft with system constraints and unknown additive disturbances is considered. Through the design of a new non-linear tube-based robust model predictive control (TRMPC) algorithm, a dual-loop cascaded tracking control framework is established. The proposed TRMPC algorithm explicitly considers the effect of disturbances and applies tightened system constraints to predict the motion of the nominal system. The obtained optimal control action is then combined with a non-linear feedback law such that the actual system trajectories can always be steered within a tube region centred around the nominal solution. To facilitate the recursive feasibility of the optimization process and guarantee the input-to-state stability of the tracking control process, the terminal controller and the corresponding terminal invariant set are also constructed. The effectiveness of using the proposed dual-loop TRMPC control scheme to track reference attitude trajectories is validated by experimental studies. A number of comparative studies were carried out, and the obtained results reveal that the proposed design is able to achieve more promising constraint handling and attitude tracking performance than that of the other newly developed methods investigated in this research.

Reconfiguration of flow-based networks with back-up components using robust economic MPC

This paper addresses a computationally efficient robust tube-based model predictive control (RTBMPC) strategy of linear systems in the presence of bounded disturbance. In the RTBMPC strategy, a nominal system is introduced by ignoring the disturbances of uncertain system, and then the uncertain system will be controlled in a robust manner through its nominal system as well as an additional feedback term which rejects a bounded additive disturbance. In this paper, the tracking problem is converted into the regulation problem by introducing an extra system called regulation nominal system that its constraints are translated from tracking into regulation. It leads to a reduction in complexity of the objective function and simplification of driving the stability theory. On the other hand, RTBMPC strategy solves optimization problem for nominal system which ignores the disturbances. Since in the absence of disturbances, the state measured at the following sample will be the same as the one predicted by model, a variable prediction horizon is suggested to reduce the computational burden. In addition, new constraints are introduced to prove the recursive feasibility, local and asymptotic stability. The constrained sampled double integrator is presented to illustrate the effectiveness of the proposed RTBMPC.

This paper focuses on the design of a robust tube-based Model Predictive Control law for the control of constrained mobile robots. A time-varying trajectory tracking error model has been used, where uncertainties are assumed to be bounded and additive. The proposed solution to the control problem is a tube-based MPC ensuring robustness and stability. A comparative simulation example is presented showing the promising behavior of the robust MPC controller.

In this paper, a decentralized robust tube-based model predictive control algorithm is used for two-dimensional Voronoi-based deployment of a multi-agent system in a bounded convex area, where the planar motion of each agent is subject to uncertain measurements. A bias bounded by a rectangle is thus considered for each agent's position measurement. The convex area of deployment is then partitioned into guaranteed Voronoi cells separated by a bounded unauthorized corridor. By using a decentralized robust predictive control, each agent is guaranteed to evolve inside the safety region defined by the agent's guaranteed Voronoi cell and to converge to a point in a set centered on the Chebyshev center of this cell, driving the multi-agent system into a static configuration. Simulation results show the effectiveness of the proposed decentralized control strategy on a fleet of quadrotors when one of the agents is subject to a measurement bias due to a sensor fault.

Robust Time-Varying Model Predictive Control with Application to Mobile Robot Unmanned Path Tracking

Carbon neutral greenhouse: Economic model predictive control framework for education

We propose a framework for safe reinforcement learning that can handle stochastic nonlinear dynamical systems. We focus on the setting where the nominal dynamics are known, and are subject to additive stochastic disturbances with known distribution. Our goal is to ensure the safety of a control policy trained using reinforcement learning, e.g., in a simulated environment. We build on the idea of model predictive shielding (MPS), where a backup controller is used to override the learned policy as needed to ensure safety. The key challenge is how to compute a backup policy in the context of stochastic dynamics. We propose to use a tube-based robust nonlinear model predictive controller (NMPC) as the backup controller. We estimate the tubes using sampled trajectories, leveraging ideas from statistical learning theory to obtain high-probability guarantees. We empirically demonstrate that our approach can ensure safety in stochastic systems, including cart-pole and a non-holonomic particle with random obstacles.

A tracking MPC formulation that is locally equivalent to economic MPC

Integrating scheduling and control for economic MPC of buildings with energy storage

Economic model predictive control for demand flexibility of a residential building