Robust multivariable predictive control: how can it be applied to industrial test stands ?

To cope with recent technological evolutions of air conditioning systems for aircraft, the French Aeronautical Test Center built a new test stand for certification at ground level. The constraints specified by the industrial users of the process seemed antagonistic for many reasons. First, the controller had to be implemented on an industrial automaton, not adaptable to modern algorithms. Then the specified dynamic performances were very demanding, especially taking into account the wide operating ranges of the process. Finally, the proposed controller had to be easy for nonspecialist users to handle. Thus, the control design and implementation steps had to be conducted considering both theoretical and technical aspects. This finally led to the development of a new multivariable predictive controller, called alpha-MPC, whose main characteristic is the introduction of an extra tuning parameter alpha that has enhanced the overall control robustness. In particular, the H1-norm of the sensitivity functions can be significantly reduced by tuning this single new parameter. It turns out to be a simple but efficient way to improve the robustness of the initial algorithm. The other classical tuning parameters are still physically meaningful, as is usual with predictive techniques. The initial results are very promising and this controller has already been adopted by the industrial users as the basis of the control part for future developments of the test stand

Multivariable predictive controller for a test stand of air conditionning

In this paper a Multivariable Predictive Controller has been proposed in a stochastic framework for a M-input N-output system. It has been investigated using a simulation study based on an experimental model of an industrial test stand of air conditioning. Comparisons with the existing PID regulation show a great improvement : both step response and coupling effect limitation have been improved. With a 32 ms calculation time on a PC with 486DX processor (or 8 ms with a Pentium 100 processor), this regulator is able to answer the problems raised by this industrial test stand. Compatible with the industrial regulation hardware, this control algorithm will be soon set up and tested to lead the future air conditioning tests

Practical application of active noise control in a duct using predictive control

This paper presents a practical application of Active Noise Control (ANC) using the Generalised Predictive Control (GPC) algorithm. The main objective of this application was to reduce significantly the noise level in a duct using this technique. It is shown that the experimental results obtained using GPC are very close from those obtained with the classical LMS algorithm on the same experimental set-up. On the other side, no adaptation time is needed and a single controller is used over a broad band. The predictive controller is synthetized using a realistic simulation of the process based upon some rough assumptions on the transfer functions

Improvement of flight simulator feeling using adaptive fuzzy backlash compensation

In this paper we addressed the problem of improving the control of DC motors used for the specific application of a 3 degrees of freedom moving base flight simulator. Indeed the presence of backlash in DC motors gearboxes induces shocks and naturally limits the flight feeling. In this paper, dynamic inversion with Fuzzy Logic is used to design an adaptive backlash compensator. The classification property of fuzzy logic techniques makes them a natural candidate for the rejection of errors induced by the backlash. A tuning algorithm is given for the fuzzy logic parameters, so that the output backlash compensation scheme becomes adaptive. The fuzzy backlash compensator is first validated using a realistic model of the mechanical system and is actually tested on the real flight simulator

Robust multivariable predictive control: Aplication to an industrial test stand

This paper reports a theoretical extension of Multivariable Predictive Control (MPC). The robustness of an augmented algorithm (alpha-MPC) for a general M-input N-output system is explored. It is shown that an extra parameter alpha in the criterion function can reduce the Hinf-norm of the multivariable sensitivity function, thus improving the disturbance-rejection properties of the closed loop system. This control law is finally applied to a test stand for air conditioning equipments of aircrafts with a great improvement of performances regarding the former regulation

Adaptive output feedback control of aircraft flexible modes

The application of adaptive output feedback augmentative control to the flexible aircraft problem is presented. Experimental validation of control scheme was carried out using a three disk torsional pendulum. In the reference model adaptive control scheme, the rigid aircraft reference model and neural network adaptation is used to control structural flexible modes and compensate for the effects unmodeled dynamics and parametric variations of a classical high order large passenger aircraft. The attenuation of specific low and high frequency flexible mode depending on linear controller design specifications and adaptation parameters were observed. The effectiveness of the approach was seen in flexibility control of the high dimensional, nonminimum phase, nonlinear aircraft model with parametric uncertainties of wind and unmodeled dynamics of actuators and sensors

Adaptive output feedback control based on neural networks: application to flexible aircraft control

One of the major challenges in aeronautical flexible structures control is the uncertain for the non stationary feature of the systems. Transport aircrafts are of unceasingly growing size but are made from increasingly light materials so that their motion dynamics present some flexible low frequency modes coupled to rigid modes. For reasons that range from fuel transfer to random flying conditions, the parameters of these planes may be subject to significative variations during a flight. A single control law that would be robust to so large levels of uncertainties is likely to be limited in performance. For that reason, we follow in this work an adaptive control approach. Given an existing closed-loop system where a basic controller controls the rigid body modes, the problem of interest consists in designing an adaptive controller that could deal with the flexible modes of the system in such a way that the performance of the first controller is not deteriorated even in the presence of parameter variations. To this purpose, we follow a similar strategy as in Hovakimyan (2002) where a reference model adaptive control method has been proposed. The basic model of the rigid modes is regarded as a reference model and a neural network based learning algorithm is used to compensate online for the effects of unmodelled dynamics and parameter variations. We then successfully apply this control policy to the control of an Airbus aircraft. This is a very high dimensional dynamical model (about 200 states) whose direct control is obviously hard. However, by applying the aforementioned adaptive control technique to it, some promising simulation results can be achieved

A symbolic sensor for an Antilock brake system of a commercial aircraft

The design of a symbolic sensor that identifies thecondition of the runway surface (dry, wet, icy, etc.) during the braking of a commercial aircraft is discussed. The purpose of such a sensor is to generate a qualitative, real-time information about the runway surface to be integrated into a future aircraft Antilock Braking System (ABS). It can be expected that this information can significantly improve the performance of ABS. For the design of the symbolic sensor different classification techniques based upon fuzzy set theory and neural networks are proposed. To develop and to verify theses classification algorithms data recorded from recent braking tests have been used. The results show that the symbolic sensor is able to correctly identify the surface condition. Overall, the application example considered in this paper demonstrates that symbolic information processing using fuzzy logic and neural networks has the potential to provide new functions in control system design. This paper is part of a common research project between E.N.S.I.C.A. and Aerospatiale in France to study the role of the fuzzy set theory for potential applications in future aircraft control systems

Active control of a clamped beam equipped with piezoelectric actuator and sensor using generalized predictive control

of a flexible structure is here presented. The studied structure is a clamped-free beam equipped with collocated piezoelectric actuator/sensor. Piezoelectric transducers advantages lie in theirs compactness and reliability, making them commonly used in aeronautic applications, context in which our study fits. Theirs collocated placement allow the use of well-known control strategies with guaranteed stability. First an analytical model of this equipped beam is given, using the Hamilton's principle and the Rayleigh-Ritz method. After a review of the experimental setup (and notablv of the piezoelectric transducers), two control laws are described. The chosen one - Generalized Predictive Control (GPC) - will be compared to a typical control law in the domain of flexible structures, the Positive Position Feedback, one of the control lam mentioned above. Majors benefits of GPC lie in its robustness in front of model uncertainties and others disturbances. The results given come from experiments on the structure, performed thanks to a DSP. GPC appears to suit for the considered study's context (i.e. damping of the first vibration mode). Some improvements may, be reached. Among them, a more complex structure with more than a single mode to damp, and more uncertainties may be considered