Design of Experiments for Nonlinear System Identification

L'abstract è presente nell'allegato / the abstract is in the attachmen

A data-driven model inversion approach to cancer immunotherapy control

A novel data-driven control design approach for Multiple Input Multiple Output nonlinear systems is proposed in the paper, relying on the identification of a polynomial prediction model of the system to control and its on-line inversion. A simulated study is then presented, concerning the design of a control strategy for cancer immunotherapy. This study shows that the proposed approach may be quite effective in treating cancer patients, and may give results similar to (or perhaps better than) those provided by “standard” methods. The fundamental difference is that “standard” methods are typically based on the unrealistic assumption that an accurate physiological model of the cancer-immune mechanism is avail- able; in the approach proposed here, the controller is designed without such a strong assumption

Set membership fault detection for nonlinear dynamic systems

In this chapter, an innovative approach to fault detection for nonlinear dynamic systems is proposed, based on the recently introduced quasi-local set membership- identification method, overcoming some relevant issues proper of the “classical” techniques. The approach is based on the direct identification from experimental data of a suitable filter and related uncertainty bounds. These bounds are used to detect when a change (e.g., a fault) has occurred in the dynamics of the system of inter- est. The main advantage of the approach compared to the existing methods is that it avoids the utilization of complex modeling and filter design procedures, since the filter/observer is directly designed from data. Other advantages are that the approach does not require to choose any threshold (as typically done in many “classical” tech- niques), and it is not affected by under-modeling problems. An experimental study regarding fault detection for a drone actuator is finally presented to demonstrate the effectiveness of the proposed approach

A Data-Driven Model Predictive Control Approach to Lean NOx Trap Regeneration

Lean NOx trap (LNT) is one of the most effective after-treatment technologies used to reduce NOx emissions of diesel engines. One relevant problem in this context is LNT regeneration timing control. This problem is indeed difficult due to the fact that LNTs are highly nonlinear systems, involving complex physical/chemical processes, that are hard to model. In this paper, a novel approach for regeneration timing of LNTs is proposed, allowing us to overcome these issues. This approach, named data-driven model predictive control (D2-MPC), does not require a physical model of the engine/trap system but is based on low-complexity polynomial prediction models, directly identified from data. The regeneration timing is computed through an optimization algorithm, which uses the iden- tified models to predict the LNT behavior. Two D2-MPC strategies are proposed, and tested in a co-simulation study, where the plant is represented by a detailed LNT model, built using the well-known commercial tool AMEsim, and the controller is implemented in MATLAB/SIMULINK

Lean NOx Trap Regeneration Control: A data-driven MPC Approach

Lean NOx Trap (LNT) is one of the most effective after-treatment technologies used to reduce NOx emissions of diesel engines. One relevant problem in this context is LNT regeneration timing control. This problem is indeed difficult due to the fact that LNTs are highly nonlinear systems, involving complex physical/chemical processes, that are hard to model. In this paper, a novel approach for regeneration timing of LNTs is proposed, allowing us to overcome these issues. This approach, named data-driven model predictive control (D2-MPC), does not require a physical model of the engine/trap system but is based on low-complexity polynomial prediction models, directly identified from data. The regeneration timing is computed through an optimization algorithm, which uses the identified models to predict the LNT behavior. Two D2-MPC strategies are proposed, and tested in a co-simulation study, where the plant is represented by a detailed LNT model, built using the well-known commercial tool AMEsim, and the controller is implemented in Matlab/Simulink

How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control

Interest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. In this context, one of the key technological enablers is represented by the motion-planning and control system, with the aim of guaranteeing the occupants comfort and safety. In this paper, a trajectory-planning and control algorithm is developed based on a Model Predictive Control (MPC) approach that is able to work in different road scenarios (such as urban areas and motorways). This MPC is designed considering imitation-learning from a specific dataset (from real-world overtaking maneuver data), with the aim of getting human-like behavior. The algorithm is used to generate optimal trajectories and control the vehicle dynamics. Simulations and Hardware-In-the-Loop tests are carried out to demonstrate the effectiveness and computation efficiency of the proposed approach

Data-driven Model Predictive Control for Lean NOx Trap Regeneration

Lean NOx Trap (LNT) is one of the most eective after-treatment technologies used to reduce NOx emissions of diesel engines. One relevant problem in this context is LNT regeneration timing control. This problem is indeed difficult due to the fact that LNTs are highly nonlinear systems, involving complex physical/chemical processes that are hard to model. In this paper, a novel data-driven model predictive control (D2-MPC) approach for regeneration timing of LNTs is proposed, allowing us to overcome these issues. This approach does not require a physical model of the engine/trap system but is based on low-complexity polynomial prediction model, directly identied from data. The regeneration timing is computed through an optimization algorithm, which uses the identied model to predict the LNT behavior. The proposed D2- MPC approach is tested in a co-simulation study, where the plant is represented by a detailed LNT model, developed using the well-known commercial tool AMEsim, and the controller is implemented in Matlab/Simulink

Polynomial classification model for real-time fall prediction system

Human gait is a dynamic biometrical feature that describes the kinematics of human walking. Gait modeling is studied in order to find a pattern of walking that can be used for diagnosis of walking disorder or abnormal walk detection. Difficulty in walking progressively increases with aging and causes unintentional falls, which is a common incident among elderly people. Fall prediction systems can help to prevent unintentional falls that could cause serious injuries, therefore they can reduce the health service costs. This paper presents an algorithm with polynomial classification model of human gait for real-time fall prediction. This approach enables the user to detect the transition from a normal to an abnormal walking pattern. A dataset based on the state-of-the-art techniques in simulating abnormal walks was created by using an accelerometer embedded in a smartphone, which is recognized to be precise enough for fall avoidance systems. The proposed approach improves state-of-the-art fall prediction approaches, by achieving 99.2% of accuracy in abnormal walk detection

Polynomial classification model for real-time fall prediction system

Human gait is a dynamic biometrical feature that describes the kinematics of human walking. Gait modeling is studied in order to find a pattern of walking that can be used for diagnosis of walking disorder or abnormal walk detection. Difficulty in walking progressively increases with aging and causes unintentional falls, which is a common incident among elderly people. Fall prediction systems can help to prevent unintentional falls that could cause serious injuries, therefore they can reduce the health service costs. This paper presents an algorithm with polynomial classification model of human gait for real-time fall prediction. This approach enables the user to detect the transition from a normal to an abnormal walking pattern. A dataset based on the state-of-the-art techniques in simulating abnormal walks was created by using an accelerometer embedded in a smartphone, which is recognized to be precise enough for fall avoidance systems. The proposed approach improves state-of-the-art fall prediction approaches, by achieving 99.2% of accuracy in abnormal walk detection

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3