Reine Talj

Experimental Validation of a PEM Fuel-Cell Reduced-Order Model and a Moto-Compressor Higher Order Sliding-Mode Control

Fuel cells are electrochemical devices that convert the chemical energy of a gaseous fuel directly into electricity. They are widely regarded as potential future stationary and mobile power sources. The response of a fuel-cell system depends on the air and hydrogen feed, flow and pressure regulation, and heat and water management. In this paper, the study is concentrated on the air subsystem that feeds the fuel-cell cathode with oxygen. Proceeding from a fourth-order model representing the air subsystem of a proton exchange membrane (PEM) fuel cell, a reduced third-order model is presented. Simulations show that the relative error caused by this reduction does not exceed 5%. Experimental validation has been done on a 33-kW PEM fuel cell, for both fourth- and reduced third-order models with less than 5% relative error. Additionally, a higher order sliding-mode supertwisting algorithm, with a well-known heuristic modification using variable gains, has been designed and validated experimentally to control a permanent-magnet synchronous motor that drives a volumetric compressor (double screw) designed to feed the 33-kW fuel cell with air.

A controller tuning methodology for the air supply system of a PEM fuel-cell system with guaranteed stability properties

Fuel cells are electrochemical devices that convert the chemical energy of a gaseous fuel directly into electricity. They are widely regarded as potential future stationary and mobile power sources. The response of a fuel-cell system depends on the air and hydrogen feed, flow and pressure regulation, and heat and water management. In this article, the study is concentrated on the air subsystem that feeds the fuel-cell cathode with oxygen and, in particular, on the problem of providing tuning rules for these controllers ensuring stability of the overall system. Proceeding from a reduced order non-linear model, that preserves the main features of the (by-now classical) ninth order model, we suggest a natural decomposition into interconnected subsystems where one of them is strictly passive, hence finite ℒ2-gain stable, and the other one depends on the controller parameters. The proposed tuning methodology consists then on enforcing the required input–output property of the feedback loop, either passivity or a suitable ℒ2-gain. For this end, the feedback operator is linearised, then robust Kharitonov-based positive (or bounded) realness conditions are imposed to determine the allowable ranges for the controller gains. We illustrate the methodology with a classical cascaded loop-controller structure with an inner loop feedback linearising controller and an outer loop PI regulator. Simulation results are presented to illustrate the conservativeness of the analysis as well as the performance improvement obtained with a suitable tuning.

Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation

This paper presents design and experimental validation of a vehicle lateral controller for autonomous vehicle based on a higher-order sliding mode control. We used the super-twisting algorithm to minimize the lateral displacement of the autonomous vehicle with respect to a given reference trajectory. The control input is the steering angle and the output is the lateral displacement error. The particularity of such a strategy is to take advantage of the robustness of the sliding mode controller against nonlinearities and parametric uncertainties in the model, while reducing chattering, the main drawback of first order sliding mode. To validate the control strategy, the closed-loop system simulated on Matlab-Simulink has been compared to the experimental data acquired on our vehicle DYNA, a Peugeot 308, according to several driving scenarios. The validation shows robustness and good performance of the proposed control approach.

Passivity and robust PI control of the air supply system of a PEM fuel cell model

Fuel cells are electrochemical devices that convert the chemical energy of a gaseous fuel directly into electricity. They are widely regarded as potential future stationary and mobile power sources. The response of a fuel cell system depends on the air and hydrogen feed, flow and pressure regulation, and heat and water management. In this paper, the study is concentrated on the control of the air subsystem that feeds the fuel cell cathode with oxygen-whose dynamics is described with a widely accepted nonlinear model. Due to the complexity of this model, the model-based controllers that have been proposed for this application are designed using its linear approximation at a given equilibrium point, which might lead to conservative stability margin estimates for the usually wide operating ranges of the system. On the other hand, practitioners propose the use of simple proportional or proportional-integral controllers around the compressor flow, which ensures good performance in most applications. In this paper we provide the theoretical justification to this scheme, proving that this output variable has the remarkable property that the linearization (around any admissible equilibrium) of the input-output map is strictly passive. Hence, the controllers used in applications yield (locally) asymptotically stable loops-for any desired equilibrium point and all values of the controller gains. Ensuring stability for all tuning gains overcomes the inherent conservativeness of linearized dynamics analysis, and assures the designer on the current use of robust, high performance loops. Instrumental to prove the passivity property is the exploitation of some monotonicity characteristics of the system that stem from physical laws.

Second order sliding mode control of the moto-compressor of a PEM fuel cell air feeding system, with experimental validation

Fuel cells are electrochemical devices that convert the chemical energy of a gaseous fuel directly into electricity. They are widely regarded as potential future stationary and mobile power sources. The response of a fuel cell system depends on the air and hydrogen feed, flow and pressure regulation, and, heat and water management. In this paper, the study is concentrated on the air subsystem that feeds the fuel cell cathode with oxygen. An IP control, a RST regulator and a higher order sliding mode control, super-twisting algorithm, with variable gains, have been designed and validated experimentally to control the air flow of the moto-compressor system, composed of a DC motor driving a volumetric compressor of type piston, designed to feed a 500W fuel cell with air. Experimental results show better performance with the sliding mode control, especially when dealing with a delayed air flow sensor response.

Immersion and invariance control for reference trajectory tracking of autonomous vehicles

This paper presents design and validation of a vehicle lateral controller for autonomous trajectory following based on Immersion and Invariance (I&I) principle. The aim is to minimize the lateral displacement of the autonomous vehicle with respect to a given reference trajectory. The control input is the steering angle and the output is the lateral displacement error. A first version of this controller based on I&I principle has been proposed previously by the authors in [1], but it suffers from a lack of robustness in some critical situations, near the nonlinear zones. In this paper, we propose a considerable improvement in the used model and the control synthesis. To validate the control strategy, the closed-loop system simulated on Matlab-Simulink has been compared to the experimental data acquired on the vehicle DYNA of Heudiasyc laboratory, a Peugeot 308, according to several real driving scenarios. The validation shows robustness and good performances of the proposed control approach, and puts in evidence the improvement with regard to the previous I&I control strategy proposed in [1].

Design and Comparison of Robust Nonlinear Controllers for the Lateral Dynamics of Intelligent Vehicles

This paper focuses on the lateral control of intelligent vehicles; the aim is to minimize the lateral displacement of the autonomous vehicle with respect to a given reference trajectory. The control input is the steering angle, and the output is the lateral error displacement. We present here an analysis of commonality of three lateral nonlinear adaptive controllers. The first controller is a higher order sliding-mode controller (SMC). The second controller is based on the immersion and invariance (I & I) principle. The design of this controller led us to prove a very strong stability criterion of the closed-loop system for all controller gains chosen to be positive. Thereafter, some interesting characteristics of passivity of the systems were proved following this development. Hence, the third controller is a passivity-based controller (PBC), an adaptive PI controller based on the feedback of a passive output. To validate our control laws, tests have been performed on SCANeR Studio, a driving simulation engine, according to several real driving scenarios. A comparison of these different controllers is made to highlight the advantages and drawbacks of each control approach in lateral tracking of a reference trajectory.

A Markov Decision Process-based approach for trajectory planning with clothoid tentacles

The work presented in this paper focuses on reactive local trajectory planning which plays an essential role for future autonomous vehicles. The challenge is to avoid obstacles in respect to road rules while following a global reference trajectory. The planning approach used in this work is the method of clothoid tentacles generated in the egocentered reference frame related to the vehicle. Generated tentacles in a egocentered grid represent feasible trajectories by the vehicle, and in order to choose the right one, we formulate the problem as a Markov Decision Process.

Integrating safety distances with trajectory planning by modifying the occupancy grid for autonomous vehicle navigation

The goal of the work in this paper is to use occupancy grid in integrating safety distances with the planning strategy for autonomous vehicle navigation. The challenge is to avoid static and dynamic obstacles at high speed with respect to some specific road rules while following a global reference trajectory. Our local trajectory planning algorithm is based on the method of clothoid tentacles. It consists on generating clothoid tentacles in the egocentered reference frame related to the vehicle. Using information provided from sensors, we build an occupancy grid that we modify to take into consideration safety distances. We use this modified occupancy grid to classify each tentacle as navigable or not navigable. By formulating the problem as Markov Decision Process, only one tentacle among the navigable ones is chosen as the vehicle local reference trajectory.

Passivity analysis and design of passivity-based controllers for trajectory tracking at high speed of autonomous vehicles

Autonomous intelligent vehicles are under intensive development, especially this last decade. This paper focuses on the lateral control of intelligent vehicles, with the aim of minimizing the lateral displacement of the autonomous vehicle with respect to a given reference trajectory. The control input is the steering angle and the output is the lateral error displacement. After passivity analysis of the system to establish the properties of passivity between some inputs and outputs, we present design and validation of lateral controllers based on passivity, to ensure robust stability and good performances with respect to parametric variations and uncertainties encountered in driving applications. The control strategies have been validated in closed-loop on SCANeRTM studio [1], a driving simulation engine, according to several real driving scenarios. The validation shows robustness and good performances of the proposed control approaches, and puts in evidence the improvement brought by the proposed Nested Passivity-Based Controller (PBC).