Slovakia

This paper presents an approach to design nonlinear control policies for the class of systems with nonlinear steady-state characteristics while assuming constant dynamic behavior. We present an approach where the nonlinear steady-state characteristics are approximated with a nonlinear function. Such a model then serves as the design model for the nonlinear model predictive control strategy. The validity of such a control approach is experimentally implemented on a laboratory scale device.

Nonlinear Model Predictive Control Conference 2021

Nonlinear MPC Policy for Systems with Data Driven Identification

identifi cation

data approximation

nonlinear model predictive control

The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) aims to bring together researchers interested in and work in the MPC field from both academia and industry. This allows us to reflect and establish the current state-of-the-art and focus on the future development of the MPC field towards relevant directions.

Model predictive control (MPC) is indisputably one of the advanced control techniques that have significantly affected control engineering practice with thousands of controllers implemented in various fields, spanning from the process industry to automotive and robotics. This is due to its unique ability to handle constraints, take preview information about disturbances and setpoint changes into account, and optimize performance. Furthermore, MPC allows the consideration of various types of dynamics, from linear to nonlinear, discrete to continuous, as well as an online adaptation of models and parameters. The research community on MPC is very active, with hundreds of papers published every year. Over the past decades, many theoretical, as well as implementation results, have been achieved.

### **All content presented at NMPC2021, including videos, papers and slides will be available only to registered (regular/student) participants. The content can be accessed until August 31st, 2021.**



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The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) aims to bring together researchers interested in and work in the MPC field from both academia and industry. This allows us to reflect and establish the current state-of-the-art and focus on the future development of the MPC field towards relevant directions.

technical paper

This paper presents a real-time capable nonlinear model predictive control (NMPC) strategy to effectively control the driving performance of an electric vehicle (EV) while optimizing thermal utilization. The prediction model is based on an experimentally validated two-node lumped parameter thermal network (LPTN) and one-dimensional driving dynamics. An efficient solver for the trajectory tracking problem is exported using acados and deployed on a dSPACE SCALEXIO embedded system. The lap time of a high-load driving cycle compared to a state-of-the-art derating strategy improved by 2.56% with an energy consumption reduction of 2.43% while respecting the temperature constraints of the electric drive.

Embedded Real-Time Nonlinear Model Predictive Control for the Thermal Torque Derating of an Electric Vehicle

**Read paper on the following link:**

https://drive.google.com/file/d/1WNxyobfYRHi6Mnx3X9En_M5cgzIbDRAO/view?usp=sharing

**Abstract:**



Motion Cueing Control Design Based on a Nonlinear MPC Algorithm

Model predictive control requires the real-time solution of an optimal control problem, which can be challenging on computationally limited systems. Approximating the solution such as by neural networks or series expansions, or deriving an explicit solution, can overcome this challenge. Using neural networks for approximation, a question arises as to how to guarantee closed-loop safety and stability. We use robust control theoretic tools to provide stability guarantees using a neural network trained to approximate a model predictive controller. Notably, the model predictive controller, which might offer desirable closed-loop performance, is not required to provide provable stability properties. To provide stability guarantees for the neural network approximated controller, the closed-loop system is reformulated as a diagonal nonlinear differential form, exploiting that the neural network activation functions are sector bounded and that their slopes are globally bounded. Based on this representation, we establish sufficient closed-loop stability conditions in form of linear matrix inequalities for the nominal and the disturbed system using the neural network approximated model predictive controller.

Robust Control Theory Based Stability Certificates for Neural Network Approximated Nonlinear Model Predictive Control

Buildings are responsible for about a quarter of global energy-related CO2 emissions. The talk presents a co-design framework for simultaneous optimal design and operation of residential buildings using Model Predictive Control (MPC). The framework is capable of explicitly taking into account operational

Predictive Control Co-Design for Enhancing Flexibility in Residential Housing with Battery Degradation

This paper presents a distributed predictive control framework coordinating battery

Virtual Storage Plant Aggregating Electrical Energy Storages and HVAC Systems Providing Regulating Reserve and Voltage Regulation

We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems.

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a terminal cost and a terminal set constraint on an extended state containing past input-output values. We provide an explicit design procedure for the corresponding terminal ingredients that only uses measured input-output data. Further, we prove that the MPC scheme based on these terminal ingredients exponentially stabilizes the desired setpoint in closed loop. Finally, we illustrate the advantages over existing data-driven MPC approaches with a numerical example.

On the Design of Terminal Ingredients for Data-Driven MPC

We develop an efficient algorithmic implementation of output feedback control of discrete-time linear systems with state and input constraints that is based on model predictive control (MPC) and moving horizon estimation (MHE). We present an iteration scheme which combines previously proposed proximity-based MHE and relaxed barrier function based MPC algorithms in a certainty equivalence output feedback fashion. The resulting overall closed-loop system is shown to converge to the origin under mild assumptions and simulation examples are used to demonstrate the advantages of the proposed approach.

Anytime MHE-Based Output Feedback MPC

Designing predictive controllers for systems with computationally limited embedded hardware, e.g. for autonomous vehicles, requires solving an optimization problem in real-time taking the vehicle dynamics and constraints into account.

Continuous-Time Approximated Parametric Output-Feedback Nonlinear Model Predictive Control

**Read paper on the following link:**

https://drive.google.com/file/d/10Ja3BWljpdOVUSBzNBzD-RJgnxp0Z-fD/view?usp=sharing

**Abstract:**



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