A robust model predictive control approach for post-disaster relief distribution

We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers.

This paper extends efficient, robust model predictive control (MPC) approaches for linear parameter varying (LPV) systems to tracking scenarios. A dual mode approach is used and future information about target changes is included in the optimisation tracking problem. The controller guarantees recursive feasibility by adding an artificial target as an extra degree of freedom. Convergence to admissible targets is ensured by constructing a robustly invariant set to track any admissible target. The efficacy of the proposed algorithm is demonstrated by MATLAB simulations.

Robust model predictive control for stratospheric airships using LPV design

A new robust model predictive control (MPC) approach is proposed for constrained linear systems in this paper. It adopts a contractive state set as terminal set in MPC problem and employs a novel function of the state with respect to this terminal set as the stage cost of the objective function. When the controlled systems are subject to linear constraints, explicit MPC law can be obtained as a piecewise affine linear state feedback control law via multi-parameter linear programming method. The presented MPC, along with linear variable-structure control law associated with the terminal set, is essentially a dual mode control scheme to stabilize the constrained uncertain linear system. Compared to the existing MPC methods, the proposed approach have both better real-time applicability and more general robust stability guarantee.

In this paper, a robust constrained model predictive control approach for discrete-time uncertain Takagi-Sugeno (T-S) fuzzy systems is developed. Based on the non-parallel distributed compensation law (non-PDC), a fuzzy predictive controller is designed to stabilize the resulting closed-loop system via an extended non-quadratic Lyapunov function. Slack matrices and collection matrix are employed to obtain less conservative results and sufficient conditions for the solvability of this problem is provided in the form of linear matrix inequalities, the real-time fuzzy predictive control law is easily calculated and implemented, at each sampling time, which minimizes the objective function over infinite moving horizon subjects to input and output constraints. To demonstrate the effectiveness and applicability, the simulation results on a continuous stirred tank reactors (CSTR) is illustrated by the fuzzy model predictive control approach we proposed.

Smooth switching in a scheduled robust model predictive controller

<div class="section abstract"><div class="htmlview paragraph">In this paper, we propose a novel control architecture for dealing with the requirements arising in a cooling system of an ICE. The idea is to take advantage of the joint action of an electric pump and of an <i>ad-hoc</i> regulation module, which is used to determine adequate flow rates despite engine speeds. Specifically, a robust Model Predictive Control approach is exploited to take care formally of input/output constraints and disturbance effects of the resulting lumped parameter model of the engine cooling system, which incorporates the nucleate boiling heat transfer regime. Numerical simulations and test rig experimental data are presented. The results achieved show that the proposed control scheme is capable of providing effective and safe cooling while mitigating disturbance effects and minimizing coolant flow rates when compared with the action pertaining to standard crankshaft driven pumps.</div></div>

A reinforcement learning (RL)-based robust model predictive control (MPC) approach is proposed to achieve learning and control for unknown autoregressive exogenous (ARX) systems with bounded noises. We leverage the uniqueness of the ARX model to separately learn unknown coefficients and the corresponding augmented noise interval. A temporal difference (TD) RL algorithm is utilized to learn unknown coefficients by incorporating input-output data information, and both analytical update and empirical update methods are employed to learn the augmented noise interval at specific learning steps. With the learned coefficients of RL, a robust MPC optimization problem is formulated to provide safe control actions for RL, facilitating effective control and learning for the unknown ARX system. The convergence of learning unknown coefficients and input-to-state stability of the unknown system are analyzed. Simulation results are presented to illustrate the effectiveness of the proposed approach.

This paper presents a model-based approach for the collective control of residential air-conditioning loads to deliver robust and optimal demand management. The proposed approach performs an optimal trade-off between accurate tracking of system operator specified load-set points and minimisation of consumer discomfort, while ensuring robustness to parametric uncertainties and fluctuations in outdoor temperature. Benefiting from robustness to uncertainties, the proposed approach is reliant on minimal household specific information. The mathematical model of the population of residential air-conditioning loads is obtained through the aggregation of individual household specific thermal models. This is followed by the development of a robust model predictive control approach for aggregate demand management to deliver optimum regulation services to account for uncertainties in model mismatch and the prediction errors associated with outdoor temperature. The approach is consistent with the existing demand response standards and is validated using a reference signal from PJM. The results demonstrate that the developed control scheme is capable of precisely following the system-operator specified load set-points even under the worst-case uncertainties of thermal parameters. While achieving the target set-point, it is further observed that customer comfort is always preserved along with minimum compressor control action on air conditioners.

The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing available resources, increases the difficultly of controller design. This letter proposes a robust self-triggered model predictive control approach to optimize a control objective while managing resource consumption. In particular, a novel zero-order-hold aperiodic discrete-time feedback control law is developed to ensure robust constraint satisfaction for continuous-time linear systems.

Real-time random reference tracking nonlinear model predictive control: a case study on wind turbines.

In this paper a novel approach is proposed to coordinate wind generators and battery energy storage systems (BESS) to provide both energy balancing and frequency regulation services in electricity markets. A robust optimization based model predictive control (RMPC) scheme is developed to determine the optimal bidding strategy for wind and storage, under uncertainty due to electricity price forecast error. Allowing the wind generation and battery energy storage combination to participate in both the energy balancing and frequency regulation markets increases their joint profits. The performance of the proposed Robust MPC-based optimal bidding strategy is demonstrated through a case study.

Robust autonomous vehicle control by leveraging multi-stage MPC and quantized CNN in HIL Framework

A new approach for robust model predictive control of biological production processes

Model Predictive Control (MPC) algorithms have long been applied to nonlinear processes. In a quasi-Linear Parameter Varying (qLPV) setting, nonlinearities are included into bounded scheduling parameters, which are given as a function of endogenous variables; these scheduling parameters are <i>a-priori</i> unknown along a future prediction horizon, which complicates MPC design. To address this problem, literature points out two options: robust MPC approaches, considering the scheduling to be uncertain; or sub-optimal ones, that set values for these parameters along the horizon. With respect to the latter group, this paper proposes an extrapolation algorithm that estimates the future values of the qLPV scheduling parameters for a fixed prediction horizon of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> steps; the method is based on a recursive procedure using simple Taylor expansions. Sufficient conditions for convergent extrapolation are presented with regard to the form and class of the scheduling function and the robustness of the MPC. Different benchmark examples from the literature are presented to illustrate the algorithm, which is also compared to state-of-the-art techniques.