Adaptive High Gain Research Articles

Adaptive High Gain Observer Based MRAS for Sensorless Induction Motor Drives

Because of their advantages, speed sensorless induction motor drives have received a lot of attention in the academic and industrial communities over the last decade. Despite the recent developments in this area, the performance of sensorless variable-speed IM drives in ultra-low and zero electrical rotor speeds and regeneration mode remains a big challenge. To obtain acceptable results under unobservable conditions, an adaptive high gain observer-based MRAS speed estimator is proposed in this paper. The stability analysis of the proposed algorithm is proved using the Lyapunov stability theorem. Moreover, the efficiency and feasibility of the proposed estimator are verified by comparative experiments.

Fast fault detection for nonlinear interconnected systems via deterministic learning

In this paper, a fast fault detection scheme is developed for a class of nonlinear interconnected systems with output measurements. First, through combining an adaptive high gain observer with the deterministic learning theory, the system states and unknown dynamics are estimated simultaneously. However, large value of gain may let the estimator becomes noise sensitive. Thus, the observer structure is modified to avoid this issue. Second, by reusing the estimated knowledge which is stored in the constant radial basis function (RBF) neural networks, a bank of dynamic estimators are constructed. Then, the average [Formula: see text] norms of residuals are generated. The smallest residual principle is considered for decision-making in the detection phase. Third, the fault detection conditions and detection time are analyzed under the influence of observer gain. A simulation example is utilized to illustrate the effectiveness of this scheme.

Estimation‐Based Event‐Triggered Adaptive Terminal Sliding Mode Control without Pressure Sensors for a Polymer Electrolyte Membrane Fuel Cell Air Feeding System

Appropriate control for a polymer electrolyte membrane fuel cell air feeding system is vulnerable to load current disturbance, parameter uncertainties, measurement noise, faulty sensors, and limited communication resources. Considering these practical issues, this study presents a novel estimation‐based event‐triggered nonlinear control scheme without pressure sensors to regulate the oxygen excess ratio (OER) at its optimal reference. First, to exactly reconstruct the unmeasured OER, a uniform robust exact differentiator and an adaptive high gain observer are developed to estimate the supply manifold pressure and the cathode pressure, respectively, thus redundant sensors and development cost can be saved. Then, a reduced‐order high gain observer is constructed to approximate the unmodeled lumped disturbance consisting of external disturbances and system uncertainties, which helps to decrease the control gain and improve the robustness. Further, an event‐triggered adaptive terminal sliding mode controller with disturbance compensation is proposed to guarantee accurate and fast OER tracking control with integral absolute error (IAE) of 0.447 and average settling time of 0.9 s while avoiding chattering effect and unnecessary communication in the controller‐to‐actuator channel. The comparison results illustrate that the proposed approach achieves superior estimation and control of OER with satisfactory robustness to parameter perturbations (less than 4% deviation in IAE).

Sensorless Adaptive Observers for Linearly Parameterized Class of Induction Machine

In this paper a new adaptive flux, speed, load torque and rotor resistance observer is proposed for induction machine drive system. The structure of the proposed observer is simple and it is able to give rise to different observers among which adaptive high gain and adaptive sliding mode. We adopt a mild change of coordinates that allows to easily design of adaptive observer. Computer simulation results verify the validity of the proposed estimation algorithm.

Fault Identification for a Class of Nonlinear Systems of Canonical Form via Deterministic Learning.

In this article, through a combination of the deterministic learning (DL) method and the adaptive high gain observer (AHGO) technology, a fault identification approach for a class of nonlinear systems in canonical form is proposed. By using the DL method, the partial persistent excitation condition of the identification system is satisfied, and then, the AHGO technology is exploited to estimate the states and the neural network weights simultaneously. To analyze the convergence of the proposed method, we first analyze the uniformed completely observability (UCO) property of the linear part of the nonlinear identification system. Then, by using the Lipschitz property of the nonlinear item and the Bellman-Gronwall lemma, we show that the UCO property of the nonlinear identification system is depended on the UCO property of the linear part when the observer gain is chosen large. Therefore, by using the UCO property of the nonlinear identification system and the Lyapunov stability theorem, the convergence of the proposed learning observer is proven. The attraction of this article is based on the analysis of the UCO property of the identification system, and the convergence of the proposed learning observer can be directly proven. The simulation example is given to demonstrate the effectiveness of the proposed method.

Saturated Output Feedback Control for Robot Manipulators With Joints of Arbitrary Flexibility

Abstract This paper proposes a saturated output feedback controller for flexible-joint robot manipulators with joints of arbitrary flexibility. First, under the framework of singular perturbation theory, a saturated flexible compensator is introduced in the dynamics model to enlarge the joint stiffness and achieve a united control for the robot with joints of arbitrary flexibility. Meanwhile, the saturation problem of torque control is also addressed. Second, an extended Kalman filter (EKF) with adaptive high gain that combines the noise filter of EKF with global convergence of high-gain observer is presented to enable the output feedback tracking control without the velocity of the link side or the motor side. Third, explicit but strict stability analyses of the whole system in both full-state feedback and output feedback are given. Finally, simulation comparisons are provided to verify the effectiveness of the proposed approach.

Output Feedback Control for a Quadrotor Aircraft Using an Adaptive High Gain Observer

This paper addresses the problem of quadrotor control under unknown and time-varying disturbances. It is assumed that such disturbances affect the entire quadrotor’s dynamics, i.e., in attitude and position. To stabilize the quadrotor, we propose an output-feedback control approach, in which we implement a Backstepping control together with an Adaptive High Gain Observer to estimate disturbances. The stability of the closed-loop system is proved via the Lyapunov theory. Furthermore, the effectiveness of the methodology is illustrated through numerical simulations and experiments.

Adaptive high gain observer extension and its application to bioprocess monitoring

The adaptive version of the high gain observer for the strictly triangular systems subjected to constant unknown disturbances is proposed here. The adaptive feature is necessary due to the fact that the unknown disturbance enters in a way that cannot be suppressed by the high gain technique. The developed observers are then applied to a culture of microorganism in a bioreactor, namely, to the model of the continuous culture of Spirulina maxima. It is a common practice that just the biomass (or substrate) concentration is directly measured as the output of the process for monitoring and control purposes. This paper thereby shows both by theoretical analysis and numerical simulation that the adaptive high-gain observers offer a realistic option of online software sensors for substrate estimation.

Adaptive observer design for a class of nonlinear systems. Application to speed sensorless induction motor

This paper investigates the problem of designing an adaptive high gain observer for a class of MIMO non uniformly observable systems involving some unknown constant parameters. The considered systems are not necessarily linear with respect to the unknown parameters up to a well defined parameter structure involving the parameter characteristic indices. The observer design model is elaborated from the state system dynamics augmented with the dynamics of the unknown parameters. An adaptive observer whose gain is derived from the resolution of a Lyapunov differential equation is proposed and its exponential convergence is established under an appropriate persistent excitation property up to the classical high gain state observer design assumptions. Moreover, it is shown that the equations of the observer can be put under an adaptive form emphasizing thereby its versatility to include several available adaptive observers. The main steps of the observer design and its convergence properties are illustrated through a typical problem involving an induction motor where one aims at estimating the mechanical speed, the load torque and rotor fluxes as well as the rotor inductance and resistance from the measurements of the stator currents.

An Integrated FDD Approach for an Intensified HEX/Reactor

In this paper, a fault detection and diagnosis (FDD) scheme is developed for a class of intensified HEX/reactor, in which faults caused by sensor, actuator, and process are taken into account in the unified framework. By considering overall heat transfer coefficient as a function of fouling and fluid flow rate, a dynamic model which is capable of identifying these two faults simultaneously is derived. Sensor measurements, together with estimation by adaptive high gain observers, are processed, aimed at identifying sensor faults and providing adequate estimation to substitute faulty measurements. Then reliable measurements are fed to several banks of interval filters to generate several banks of residuals; each bank of residuals is sensitive to a particular process parameter/actuator. By evaluating these residuals, process/actuator fault isolation and identification are achieved. The proposed strategy is applied to actual data retrieved from a new intensified heat exchanger reactor. Simulation results confirm the applicability and robustness of the proposed methodology.

Adaptive control of uncertain pure-feedback nonlinear systems

ABSTRACTAn adaptive control approach is proposed to solve the globally asymptotic state stabilisation problem for uncertain pure-feedback nonlinear systems which can be transformed into the pseudo-affine form. The pseudo-affine pure-feedback nonlinear system under consideration is with nonlinearly parameterised uncertainties and possibly unknown control coefficients. Based on the parameter separation technique, a novel backstepping controller is designed by adopting the adaptive high gain idea. The proposed control approach could avoid the drawbacks of the approximation-based approaches since no estimators are needed to estimate the virtual and the actual controllers. In addition, it could guarantee globally asymptotic state stabilisation even though there exist nonlinearly parameterised uncertainties in the considered system while comparing to the existing approximation-free approaches. A numerical and a realistic examples are employed to demonstrate the effectiveness of the proposed control method.

Experimental sensorless control for IPMSM by using integral backstepping strategy and adaptive high gain observer

In this paper a robust sensorless control for an Interior Permanent Magnet Synchronous Motor (IPMSM) is designed. The proposed control strategy uses a backstepping controller, whose robustness is improved by using integral actions added at each step of the original algorithm, and by a Maximum-Torque-Per-Ampere strategy (MTPA) to improve its energy efficient operation. Furthermore, to implement this controller in the framework of the mechanical sensorless control from the only measurements of the currents and voltages, an adaptive interconnected high gain observer is developed for estimating the rotor speed, the position and the load torque. Moreover, sufficient conditions are given to ensure the practical stability of the Observer-Controller system even if bounded uncertainties occur. Finally, the performance and the effectiveness of the designed method are tested experimentally throw a significant benchmark including different speed references and with significant robustness tests. A comparative evaluation of the computational effort of our scheme with respect to classical motor control is given.

Cooperative output regulation for a class of nonlinear multi-agent systems with arbitrarily large uncertainty

This paper studies the cooperative global robust output regulation problem for the nonlinear multi-agent systems in output feedback form with relative degree $r=2$ subject to arbitrarily large uncertainty. The robust control method alone cannot handle arbitrarily large uncertainty. By combining the robust and adaptive high gain technique, we develop a recursive procedure to construct a distributed output feedback control law to deal with the problem. The theoretical result of this paper is applied to the leader-following consensus problem of a family of hyper-chaotic Lorenz systems.

Adaptive observers for speed sensorless IMs

In this paper, one proposes an approach which allows the design adaptive observers for a class of uniformly observable nonlinear MIMO systems with linear parameterisations. The structure of the proposed observer is simple and it is able to give rise to different observers: 1 adaptive high gain observer for linearly parameterised; 2 adaptive sliding mode observer for linearly parameterised with tanh commutation function; 3 adaptive sliding mode observer for linearly parameterised with arctan commutation function. These observers are applied to jointly estimate states rotor flux, rotating speed and torque load and unknown constant parameters stator resistance.

Distributed Adaptive High-Gain Extended Kalman Filtering for Nonlinear systems

In this work, we propose a distributed adaptive high-gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high-gain extended Kalman filter is designed for each subsystem. The distributed Kalman filters communicate with each other to exchange subsystem state estimates. First, an implementation strategy which specifies how the distributed filters should communicate is designed. Second, the detailed design of the subsystem filter is described. Subsequently, the stability of the proposed distributed state estimation is analyzed. Finally, the effectiveness and applicability of the proposed design are illustrated via the application to a chemical process example.

An Adaptive High Gain Observer Design for Nonlinear Systems

This paper studies an adaptive high gain observer design for nonlinear systems which have lower triangular nonlinearity with Lipschitz coefficient, depending on the control input. Because the gain of the proposed observer is tuned automatically by a simple update law, our design approach doesn't need any information about the Lipschitz constant. Also, it is shown that under some assumptions, the dynamic gain of the proposed observer is bounded and its estimation error converges to zero asymptotically. Finally, a numerical example is given to verify the effectiveness of our design approach.

Adaptive high gain observer based output feedback predictive controller for induction motors

An accurate estimation of both rotor and stator resistances is usually required to achieve high performance control in induction machine drive systems. In our study, an output feedback predictive controller which is adaptive with respect to these parameters uncertainties is proposed. Such design attempts to a fully decoupling in speed and flux magnitude when conjoint estimation of the state and the critical parameters is performed. Precisely, two-stage of high gain observer are used to provide on-line update of rotor and stator resistances as well as following time evolution of the rotor flux and the load torque. Then, the instantaneous estimates are exploited in a constrained predictive controller for achieving the admissible tracking objective. The little number of the synthesis parameters and the ability of the control scheme to handle input constraints constitute the two main benefits of our design. Simulation results show that the proposed controller is suitable for high dynamics performance applications.

Adaptive High Gain Feedback Control of Nonholonomic Systems with Uncertain Control Coefficients

Adaptive High Gain Feedback Control of Nonholonomic Systems with Uncertain Control Coefficients

Sensorless speed control of PMSM via adaptive interconnected observer

In this article, a robust sensorless speed observer–controller scheme for a surface permanent magnet synchronous motor (PMSM) is proposed. First-of-all, following preliminary results in the framework of the induction motor that is less sensible to the position estimation error, an adaptive high gain interconnected observer is designed. It is only supplied by the electrical measurement: the motor currents and voltages. This observer estimates the rotor speed and position, the stator resistance and the load torque. A nonlinear backstepping controller is developed. The above observer is associated with this controller and the complete scheme practical stability proof is given. The overall system is tested by simulation in the framework of an industrial benchmark with a trajectory that particularly checks the motor unobservability condition. Some robustness tests have been carried out. The controller–observer scheme shows good performances in spite of the uncertainties and the unknown load torque.

Adaptive Interconnected Observer-Based Backstepping Control Design For Sensorless PMSM

AbstractIn this paper, a robust sensorless speed observer-controller scheme for a surface permanent magnet synchronous motor (SPMSM) is proposed. First-of-all, an adaptive high gain interconnected observer is designed. This observer estimates the rotor speed, the stator resistance and the load torque as well. A non linear backstepping controller is developed. The above observer is associated to this controller and the proof of the complete scheme is given. The overall system is tested by simulation in the framework of an industrial benchmark.