P. Boucher

Nonlinear Receding-Horizon Control of Rigid Link Robot Manipulators:

The approximate nonlinear receding-horizon control law is used to treat the trajectory tracking control problem of rigid link robot manipulators. The derived nonlinear predictive law uses a quadratic performance index of the predicted tracking error and the predicted control effort. A key feature of this control law is that, for their implementation, there is no need to perform an online optimization, and asymptotic tracking of smooth reference trajectories is guaranteed. It is shown that this controller achieves the positions tracking objectives via link position measurements. The stability convergence of the output tracking error to the origin is proved. To enhance the robustness of the closed loop system with respect to payload uncertainties and viscous friction, an integral action is introduced in the loop. A nonlinear observer is used to estimate velocity. Simulation results for a two-link rigid robot are performed to validate the performance of the proposed controller.

Cascaded Nonlinear Predictive Control of Induction Motor

Two versions of continuous-time feedback nonlinear predictive control laws with a fixed end point prediction and a finite horizon prediction are presented. They are applied in a cascade structure to induction motor that includes both electrical and mechanical dynamics. Both speed and torque/flux tracking objectives are achieved in matched and mismatched parameters case. The rotor flux is estimated by the Kalman filter. The derived nonlinear predictive laws minimize a quadratic performance index of the predicted tracking error. A key feature of the control law is that its implementation does not need to perform an online optimization and asymptotic tracking of smooth reference signal is guaranteed. Simulations show that the proposed controllers are suitable for high dynamic performance applications. A good robustness with respect to parameters variation and load torque disturbance are achieved.

Two cascaded nonlinear predictive controls of induction motor

Two versions of continuous-time feedback nonlinear predictive control laws with a fixed end point prediction and a finite horizon prediction are presented. They are applied in a cascade structure to induction motor. Both speed and torque/flux tracking objectives are achieved in matched and mismatched parameters case. The rotor flux is estimated by the Kalman filter. The derived nonlinear predictive laws minimize a quadratic performance index of the predicted tracking error. Its implementation does not need to perform an on line optimization and asymptotic tracking of smooth reference signal is guaranteed. Simulations show that the proposed controllers are suitable for high dynamic performance applications.

Cascaded nonlinear predictive control of induction motor

A cascaded nonlinear predictive control design is applied to a nonlinear induction machine with both electrical and mechanical dynamics. With full state measurement assumption, both rotor speed and rotor flux amplitude tracking objectives are satisfied. The proposed control law is able to deal with mismatched parameters and load torque disturbance. Simulation results are performed to illustrate the efficiency of the proposed control law.

A new control strategy for induction motor based on non-linear predictive control and feedback linearization

Proposed in this paper is a new approach of design using a monovariable generalized predictive control law, with multiple reference model (GPC/MRM) to control, firstly, rotor speed and rotor flux amplitude of an induction machine. The control law will be extended and applied, secondly, to answer a second objective which is the control of the rotor position and rotor flux amplitude of the same machine. The non-linear dynamical model of the machine is described in the stator fixed reference frame. The GPC algorithm is combined with the non-linear input–output linearizing control properties; moreover, we establish a new formulation of the reference signals, which enables one to track flux and speed/position nominal profiles, satisfying motor constraints. The global controller synthesis is given under an RST form which introduces a novel definition of the prefiltering polynomial. Copyright

Estimation of microalgal photobioreactor production based on total inorganic carbon in the medium

Microalgae biotechnology has been focusing on the use of algae in the production of high value compounds. During the last few decades, the intense research effort has been aiming at improving new controls and supervising tools as well as on a good process understanding. This requirement involves a large diversity and a better accessibility to process measurements. Probes or sensors are required to control the process. They are however relatively limited. Classical photobioreactors are usually equipped with temperature, dissolved oxygen and pH probes. These sensors actually provide very little online information on cell growth, viability, metabolic state and production. For the time being, sensors reliability cannot meet industrial bioprocessing requirements. In this context software sensors show numerous potentialities. The central axis of this work is the development of an extended Kalman filter (EKF) for the estimation of biomass concentration based on a dynamic process model in combination with total inorganic carbon measurement. A microalga Porphyridium purpureum was used as a model organism in this study. Numerical simulations and real-life experiments (batch and continuous mode) have been carried out and corresponding results are given in order to highlight the performance of the proposed estimator.

A new nonlinear multivariable control strategy of induction motors

Abstract A new real-time control strategy for induction motors is introduced. This paper shows that rotor speed and rotor flux magnitude reference tracking of an induction machine can be originally obtained. The proposed control law is based on two points: an open-loop reference control which allows to obtain perfect tracking since outputs are planned and on a closed-loop strategy based on PI controllers for the stabilization around the desired trajectories. This innovative structure ensures rotor speed and rotor fluxes tracking despite uncertainties on rotor resistance and load torque variations. Simulation and experimental results, including an estimation of the rotor flux norm, are given to illustrate the originality and effectiveness of this control law.

Robustification of CNC Controllers for Machine Tools Motor Drives

Abstract This paper presents a methodology for enhancing the robustness of a CNC controlled system by convex optimisation of the Youla parameter. The methodology requires as a first step the design of an initial polynomial controller. This controller is then robustifled considering temporal and frequency constraints, which are formulated by means of the Youla parameterisation within a convex optimisation framework. The optimal Youla parameter is finally obtained by solving this optimisation problem. In this way, a compromise between robustness and closed loop behaviour can be easily managed. An application to the position control of an induction motor drive is presented, where the robustness of different controllers (PID or GPC) regarding model uncertainties in high frequency is enhanced while respecting a temporal template for the disturbance rejection.

Direct Adaptive Generalized Predictive Control. Application to Motor Drives with Flexible Modes

Abstract Predictive control has proved to be an efficient control strategy, and its application in the motor drives field has provided good results, even with small sampling rates. However, in case of important parameters variations, a high level of performance may be impossible to maintain with a fixed controller, so that an adaptive version becomes necessary. This paper presents a direct adaptive generalized predictive control structure, including a least squares-type strategy for the controller parameters on-line identification, and a conditional updating test. An application to motor drives with flexible transmissions is finally developed, taking into account some variations of inertia.

Comfort control in residential housing using predictive controllers

Generalised predictive control (GPC) is an effective strategy for high performance applications, with good temporal and frequency properties. This paper presents an application of GPC to climate control in buildings, with particular attention on strict temperature setpoint specifications. Specifically, the future temperature setpoint (which is step-like) is prespecified or known and the objective is to attain leading edge temperatures (positive increase) to within /spl plusmn/0.5/spl deg/C at the specified time. The controller must also regulate the temperature to within /spl plusmn/0.5/spl deg/C throughout the duration of the setpoint pulse and turn off the heater coincident with the trailing edge of the step change. The controller developed consists of a standard RST regulator derived from GPC and an empirical anticipative rule which shifts the future setpoint backward in time in order to launch the heater sufficiently in the past to attain the leading edge setpoint value. The regulator is initialized only at start up; no switching in or out of controllers is needed. The validity and workability of such a predictive structure for thermal control in buildings is finally pointed out with the results of the predictive controller in the CLIM 2000 environment, coupled with an automatic design of the tuning parameters for a simplified implementation.