Application of Laguerre based adaptive predictive control to Shape Memory Alloy (SMA) actuators

This paper discusses the use of an existing adaptive predictive controller to control some Shape Memory Alloy (SMA) linear actuators. The model consists in a truncated linear combination of Laguerre filters identified online. The controller stability is studied in details. It is proven that the tracking error is asymptotically stable under some conditions on the modelling error. Moreover, the tracking error converge toward zero for step references, even if the identified model is inaccurate. Experimentalcresults obtained on two different kind of actuator validate the proposed control. They also show that it is robust with regard to input constraints.ANR MAFESM

Experimentally validated continuous-time repetitive control of non-minimum phase plants with a prescribed degree of stability

This paper considers the application of continuous-time repetitive control to non-minimum phase plants in a continuous-time model predictive control setting. In particular, it is shown how some critical performance problems associated with repetitive control of such plants can be avoided by use of predictive control with a prescribed degree of stability. The results developed are first illustrated by simulation studies and then through experimental tests on a non-minimum phase electro-mechanical system

Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

Stochastic MPC Design for a Two-Component Granulation Process

We address the issue of control of a stochastic two-component granulation process in pharmaceutical applications through using Stochastic Model Predictive Control (SMPC) and model reduction to obtain the desired particle distribution. We first use the method of moments to reduce the governing integro-differential equation down to a nonlinear ordinary differential equation (ODE). This reduced-order model is employed in the SMPC formulation. The probabilistic constraints in this formulation keep the variance of particles' drug concentration in an admissible range. To solve the resulting stochastic optimization problem, we first employ polynomial chaos expansion to obtain the Probability Distribution Function (PDF) of the future state variables using the uncertain variables' distributions. As a result, the original stochastic optimization problem for a particulate system is converted to a deterministic dynamic optimization. This approximation lessens the computation burden of the controller and makes its real time application possible.Comment: American control Conference, May, 201

Frequency-warped autoregressive modeling and filtering

This thesis consists of an introduction and nine articles. The articles are related to the application of frequency-warping techniques to audio signal processing, and in particular, predictive coding of wideband audio signals. The introduction reviews the literature and summarizes the results of the articles. Frequency-warping, or simply warping techniques are based on a modification of a conventional signal processing system so that the inherent frequency representation in the system is changed. It is demonstrated that this may be done for basically all traditional signal processing algorithms. In audio applications it is beneficial to modify the system so that the new frequency representation is close to that of human hearing. One of the articles is a tutorial paper on the use of warping techniques in audio applications. Majority of the articles studies warped linear prediction, WLP, and its use in wideband audio coding. It is proposed that warped linear prediction would be particularly attractive method for low-delay wideband audio coding. Warping techniques are also applied to various modifications of classical linear predictive coding techniques. This was made possible partly by the introduction of a class of new implementation techniques for recursive filters in one of the articles. The proposed implementation algorithm for recursive filters having delay-free loops is a generic technique. This inspired to write an article which introduces a generalized warped linear predictive coding scheme. One example of the generalized approach is a linear predictive algorithm using almost logarithmic frequency representation.reviewe

Optimized adaptive MPC for lateral control of autonomous vehicles

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksAutonomous vehicles are the upcoming solution to most transportation problems such as safety, comfort and efficiency. The steering control is one of the main important tasks in achieving autonomous driving. Model predictive control (MPC) is among the fittest controllers for this task due to its optimal performance and ability to handle constraints. This paper proposes an adaptive MPC controller (AMPC) for the path tracking task, and an improved PSO algorithm for optimising the AMPC parameters. Parameter adaption is realised online using a lookup table approach. The propose AMPC performance is assessed and compared with the classic MPC and the Pure Pursuit controller through simulationsPeer ReviewedPostprint (author's final draft

FLUTTER SUPPRESSION BY ACTIVE CONTROLLER OF A TWO-DIMENSIONAL WING WITH A FLAP

Flutter is a divergent oscillation of an aeroelastic structure, and one of a family of aeroelastic instability phenomena, that results from the interaction of elastic and inertial forces of the structure with the surrounding aerodynamic forces. Airfoil Flutter is important due to its catastrophic effect on the durability and operational safety of the structure. Traditionally, flutter is prevented within an aircraft\u27s flight envelope using passive approaches such as optimizing stiffness distribution, mass balancing, or modifying geometry during the design phase. Although these methods are effective but they led to heavier airfoil designs. On the other hand, active control methods allow for less weight and higher manoeuvring capabilities. The main objective of this study is to investigate the potential effectiveness of using Model Predictive Control MPC as an active control strategy to suppress flutter. Lagrange’s energy method and Theodore’s unsteady aerodynamic theory were employed to derive the equations of motion of a typical 2D wing section with a flap. Using MATLAB®, the airspeed at which the flutter occurs for a specific wing’s parameters were found to be 23.96 m/s, at a frequency of 6.12 Hz. A Linear Quadratic Gaussian compensator LQG was designed and simulated. MATLAB® was also used to design and simulate a discrete MPC using Laguerre orthonormal functions. The simulated results for states regulation and reference tracking tasks in the flutter airspeed region from both controllers were compared and discussed in terms of quantitative performance measures and performance indices. The results showed that both LQG and MPC are powerful in suppressing the flutter in addition to their effectiveness in tracking a reference input rapidly and accurately with zero steady-state error. The superiority for the constrained MPC is manifested by results comparison. MPC were able to save more than 40% of the needed settling time for states regulation task. Furthermore, it performed the job with much less control energy indicated by the ISE and ISU indices. On top of that, the key advantage of MPC, which is the ability to perform real-time optimization with hard constraints on input variables, was confirmed

Investigation of Model Predictive Control (MPC) for Steam Generator Level Control in Nuclear Power Plants

The capabilities and potential of Model Predictive Control (MPC) based strategies for steam generator level (SGL) controls in nuclear power plants (NPPs) have been investigated. The performance has been evaluated for all operating conditions that also include start-ups, low power operations and load rejections. These evaluations have been done for MPC controllers based on existing advanced methodologies, as well as for any potential performance improvement that can be achieved by fine tuning some of the parameters (based on the characteristics of the SGL) of the existing MPC approaches. Two version of MPC have been designed and implemented. The Standard MPC (SMPC) has investigated the performance of existing advanced MPC methodologies. The Improved MPC (IMPC) has investigated potential performance improvement over SMPC by selecting appropriate values in the weight matrix of the objective function. Performance of MPC based approaches has been evaluated and compared with an optimized PI controller in term of i) set point tracking, ii) load-following, iii) transient responses, and iv) effectiveness subject to steam and feed water flow disturbances and feed water flow signal noise. The performance evaluation has been done through computer simulation, and also through simulation on a mock-up steam generator level system. The simulation results indicate strong potential for MPC based strategies, in particular for IMPC strategy, for effective control of the steam generator levels in nuclear power plants

Identification of PEM fuel cells based on support vector regression and orthonormal bases

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Polymer Electrolyte Membrane Fuel Cells (PEMFC) are efficient devices that convert the chemical energy of the reactants in electricity. In this type of fuel cells, the performance of the air supply system is fundamental to improve their efficiency. An accurate mathematical model representing the air filling dynamics for a wide range of operating points is then necessary for control design and analysis. In this paper, a new Wiener model identification method based on Support Vector (SV) Regression and orthonormal bases is introduced and used to estimate a nonlinear dynamical model for the air supply system of a laboratory PEMFC from experimental data. The method is experimentally validated using a PEMFC system based on a ZB 8-cell stack with Nafion 115 membrane electrode assembliesPeer ReviewedPostprint (author's final draft