Adaptive Control Methodology Research Articles

A Novel Helical Milling End-Effector and Its Application

In the aircraft industry, helical milling is utilized to generate holes in difficult-to-machine materials by means of operating a cutting tool on a helical path into the workpiece. This paper proposes a new helical milling device as an end-effector of industrial robots. The end-effector employs a direct drive rotary motor with dual-eccentric structure to achieve high position accuracy of spindle eccentricity (or radial offset), which indicates the distance between the spindle axis and the hole axis in the radial direction. In order to eliminate the adverse effects of spindle eccentric rotation, time-varying cutting force, and other uncertain disturbances during the machining process, a closed-loop servo control system is designed based on the adaptive robust control (ARC) methodology. Comparative experiments and machining experiments have been conducted to verify the effectiveness of the proposed helical milling end-effector. Compared with the PID and deterministic robust control controllers, the ARC controller guarantees higher accuracy of the spindle eccentricity in the presence of parametric uncertainties and uncertain disturbances. Average errors and maximum errors of the spindle eccentricity stay below 0.5 and 1.6 μm when machining holes with the eccentricity ranging from 0.5 to 4 mm, respectively. Surface roughness Ra and roundness of the machined holes in Ti-alloy and carbon fiber reinforced plastic materials are all below 3.2 and 7 μm, respectively. The results show that the proposed helical milling end-effector is suitable for hole-machining tasks in aircraft assembly applications.

Minimal-learning-parameter based simplified adaptive neural back-stepping control of flexible air-breathing hypersonic vehicles without virtual controllers

In this paper, a novel adaptive neural control methodology is addressed for a flexible air-breathing hypersonic vehicle (FAHV) by a fusion of improved back-stepping and a minimal-learning-parameter (MLP) scheme. To facilitate the control design, the vehicle dynamics is decomposed into the altitude subsystem and the velocity subsystem. Different from the traditional back-stepping design, in this paper, the virtual control laws for the altitude dynamics are artificial intermediate variables required only for analytic purpose while only the final actual controller is needed to be implemented. For each subsystem, only one neural network is employed to approximate the lumped uncertainty. Moreover, by the merit of the MLP technique, only one learning parameter is required for neural approximation in each subsystem. The novel contribution with respect to the existing literatures is that the proposed control strategy is concise and the computational load is low. Finally, the effectiveness of the exploited control approach is verified by simulation results in the presence of uncertain parameters.

IIR filtering based adaptive active vibration control methodology with online secondary path modeling using PZT actuators

Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.

Nonlinear Adaptive Output-Feedback Controller Design for Guidance of Flexible Needles

In this paper, a control methodology is proposed for guiding and stabilizing flexible bevel-tip needles to an arbitrary planar slice. The proposed controller is an adaptive version of the previously proposed nonlinear output feedback controller, which was built upon the well-known nonholonomic kinematic model of needle steering. On the grounds that such a model is subject to parametric uncertainty, an adaptive controller is necessary for adjusting the model parameters. In addition, a nonlinear observer is required due to the existence of some unmeasurable state variables. Although the original form of the studied system is linearly parameterized, its canonical form is not, preventing the application of conventional adaptive control schemes. In this paper, a nonlinear adaptive control methodology is proposed and applied to the guidance problem of steerable needles. Through simulation results, it is illustrated that the proposed methodology greatly outperforms the existing feedback linearization-based approach.

Feedback linearisation control of an induction machine augmented by single-hidden layer neural networks

We consider adaptive output feedback control methodology of highly uncertain nonlinear systems with both parametric uncertainties and unmodelled dynamics. The approach is also applicable to systems of unknown, but bounded dimension. However, the relative degree of the regulated output is assumed to be known. This new control strategy is proposed to address the tracking problem of an induction motor based on a modified field-oriented control method. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modelling errors. The network weight adaptation rule is derived from the Lyapunov stability analysis, that guarantees boundedness of all the error signals of the closed-loop system. Computer simulations of an output feedback controlled induction machine, augmented via single-hidden-layer neural networks, demonstrate the practical potential of the proposed control algorithm.

Sector-condition-based results for adaptive control and synchronization of chaotic systems under input saturation

This paper addresses the design of adaptive feedback controllers for two problems (namely, stabilization and synchronization) of chaotic systems with unknown parameters by considering input saturation constraints. A novel generalized sector condition is developed to deal with the saturation nonlinearities for synthesizing the nonlinear and the adaptive controllers for the stabilization and synchronization control objectives. By application of the proposed sector condition and rigorous regional stability analysis, control and adaptation laws are formulated to guarantee local stabilization of a nonlinear system under actuator saturation. Further, simple control and adaptation laws are developed to synchronize two chaotic systems under uncertain parameters and input saturation nonlinearity. Numerical simulation results for Rössler and FitzHugh–Nagumo models are provided to demonstrate the effectiveness of the proposed adaptive stabilization and synchronization control methodologies.

Adaptive Control of Solar Energy Collector Systems [Book News

The excellent book Adaptive Control of Solar Energy Collector Systems presents, in a rigorous and comprehensive way, the adaptive control methodologies to deal with the DCSF-specific dynamic structure. The authors are world-known specialists in intelligent control, systems identification, and decision-making technology. The textbook clearly explains the main mathematical tools to apply adaptive and predictive control to industrial plants, including power conversion and distributed renewable energy resources. It contributes to disseminate advanced control expertise while encouraging technology transfer in control engineering.

Robustness and perfect tracking in simple adaptive control

SummaryAdaptive controllers have been developed to guarantee stability and asymptotically perfect tracking under ideal conditions. In particular, the simple adaptive control methodology has been developed to avoid the use of identifiers, observer‐based controllers and, in general, to avoid using large order adaptive controllers in the control loop. In spite of initially successful applications, it is known that the basic adaptive algorithm may lead to divergence of the adaptive gains in such non‐ideal conditions as the presence of disturbances. A sigma‐term adjustment has been used to maintain boundedness with disturbance, yet is known that it seems to also eliminate perfect following even in ideal situations. Furthermore, bursting and other chaotic‐like phenomena observed in connection with the sigma‐term may also give pause to potential users of adaptive control. This paper revisits and modifies the use of various components of the simple adaptive control approach and shows how one can use passivity concepts such that, while it maintains robustness with disturbances, it also allows asymptotically perfect tracking in ideal conditions. Copyright © 2015 John Wiley & Sons, Ltd.

Nonlinear Robust Adaptive Tracking Control of a Quadrotor UAV Via Immersion and Invariance Methodology

This paper presents a novel asymptotic tracking controller for an underactuated quadrotor unmanned aerial vehicle using the robust integral of the signum of the error (RISE) method and an immersion and invariance (I&I)-based adaptive control methodology. The control system is decoupled into two parts: the inner loop for attitude control and the outer loop for position control. The RISE approach is applied in the inner loop for disturbance rejection, whereas the I&I approach is chosen for the outer loop to compensate for the parametric uncertainties. The asymptotic tracking of the time-varying 3-D position and the yaw motion reference trajectories is proven via the Lyapunov-based stability analysis and LaSalle's invariance theorem. Real-time experiment results, which are performed on a hardware-in-the-loop simulation testbed, are presented to illustrate the performance of the proposed control scheme.

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.

Lyapunov-design for a super-twisting sliding-mode controller using the certainty-equivalence principle

In this article, a Lyapunov-based control concept is presented combining variable structure and adaptive control. The considered system class comprises single-input systems which are affected by structured and unstructured uncertainties. The design is based on a Lyapunov function for the super-twisting algorithm. This Lyapunov function is exploited to derive the adaptive part of the proposed controller using the certainty equivalence principle. It is demonstrated that this combination of sliding-mode and adaptive control methodology allows to relax the boundedness condition known from the super-twisting algorithm while reducing the sliding-mode gain significantly. The effectiveness of the presented concept is demonstrated in detail, using results from numerical simulations and experimental data of a laboratory testbed.

Set theoretic performance verification of low‐frequency learning adaptive controllers

SummaryAlthough adaptive control has been used in numerous applications, the ability to obtain a predictable transient and steady‐state closed‐loop performance is still a challenging problem from the verification and validation standpoint. To that end, we considered a recently developed robust adaptive control methodology called low‐frequency learning adaptive control and utilized a set of theoretic analysis to show that the transitory performance of this approach can be expressed, analyzed, and optimized via a convex optimization problem based on linear matrix inequalities. This key feature of this design and analysis framework allows one to tune the adaptive control parameters rigorously so that the tracking error components of the closed‐loop nonlinear system evolve in a priori specified region of the state space whose size can be minimized by selecting a suitable cost function. Simulation examples are provided to demonstrate the efficacy of the proposed verification and validation architecture showing the possibility of performing parametric studies to analyze the interplay between the size of the tracking error residual set and important design parameters such as the adaptation rate and the low‐pass filters time constant of the weights adaptation algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

Augmented L<sub>1</sub> adaptive tracking control of quad-rotor unmanned aircrafts

This paper addresses a new trajectory tracking controller for quad-rotor aircrafts in the presence of time-varying aerodynamic effect and bounded external disturbance using the novel L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> adaptive control methodology augmented with nonlinear feed-forward compensations. The proposed augmented L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> adaptive controller achieves uniformly bounded transient and asymptotic tracking of the output signal for any designated bounded reference trajectory. Finally, simulations of tracking a circular reference trajectory are performed to illustrate the validity of the proposed controller.

A new approach for online T-S fuzzy identification and model predictive control of nonlinear systems

This paper proposes a new unsupervised fuzzy clustering algorithm (NUFCA) to construct a novel online evolving Takagi–Sugeno (T-S) fuzzy model identification method and an adaptive predictive process control methodology. The proposed system identification approach consists of two main steps: antecedent T-S fuzzy model parameters identification and consequent parameters identification. The NUFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modelling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system; then the recursive least squares method is exploited to obtain initialization type consequent parameters and to construct a method for on-line fuzzy model identification. The integration of the proposed adaptive identification method with the generalized predictive control results in an effective adaptive predictive fuzzy control methodology. For better demonstration of the robustness and efficiency of the proposed methodology, it is applied to the identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment plant (WWTP); and to control a simulated continuous stirred tank reactor (CSTR), and a real experimental setup composed of two coupled DC motors. The results show that the developed evolving T-S fuzzy model methodology can identify nonlinear systems satisfactorily and can be successfully used for a prediction model of the process for the generalized predictive controller. It is also shown that the algorithm is robust to changes in the initial parameters, and to unexpected disturbances.

Nonlinear Adaptive Output Feedback Control of Flexible-Joint Space Manipulators with Joint Stiffness Uncertainties

Growing research interest in space robotic systems capable of accurately performing autonomous manipulation tasks within an acceptable execution time has led to an increased demand for lightweight materials and mechanisms. As a result, joint flexibility effects become important and represent the main limitation to achieving satisfactory trajectory-tracking performance. This paper addresses the nonlinear adaptive output feedback control problem for flexible-joint space manipulators. Composite control schemes in which decentralized simple adaptive control-based adaptation mechanisms to control the quasi-steady-state robot model are added to a linear correction term to stabilize the boundary-layer model are proposed. An almost strictly passivity-based approach is adopted to guarantee closed-loop stability of the quasi-steady-state model. Simulation results are included to highlight the performance and robustness of the proposed adaptive composite control methodologies to parametric and dynamics modeling uncertainties.

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

In various dynamic parameters systems that need to be training on-line adaptive control methodology is used. In this paper fuzzy model-base adaptive methodology is used to tune the linear Proportional Integral Derivative (PID) controller. The main objectives in any systems are; stability, robust and reliability. However PID controller is used in many applications but it has many challenges to control of continuum robot. To solve these problems nonlinear adaptive methodology based on model base fuzzy logic is used. This research is used to reduce or eliminate the PID controller problems based on model reference fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.

Indirect Adaptive Attenuation of Multiple Narrow-Band Disturbances Applied to Active Vibration Control

In this brief, an indirect adaptive control methodology for attenuation of multiple unknown time varying narrow-band disturbances is proposed. This method is based on the real time estimation of the frequency of narrow-band disturbances using adaptive notch filters followed by the design of a controller using adjustable band-stop filters for the appropriate shaping of the output sensitivity function. A Youla-Kučera parametrization of the controller is used for reducing the computation load. This approach is compared on an active vibration control system with the direct adaptive control scheme based on the internal model principle proposed. Real time experimental results are provided.

Decentralized simple adaptive control of nonlinear systems

SUMMARYRecently, the passivity results for linear time‐invariant systems were successfully extended to nonlinear and nonstationary systems, thus guaranteeing stability of adaptive control of nonlinear square systems. Based on this theoretical development, this paper presents the development of a new class of direct adaptive controllers, which employ a new decentralized adaptation law mechanism that is developed from the simple adaptive control technique. The resulting direct adaptive control methodology is referred to as decentralized simple adaptive control. A simplification of this new control algorithm, referred to as decentralized modified simple adaptive control, is also presented. In addition, it is shown that both control methodologies can be modified to avoid divergence in practical situations, where the trajectory tracking errors cannot reach zero. Using Lyapunov direct method and Lasalle's invariance principle for nonautonomous systems, the formal proof of stability is established. As well, a numerical simulation study for a trajectory tracking problem by a rigid‐joint manipulator is presented to illustrate the new adaptive control approaches. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Almost Passivity and Simple Adaptive Control in Discrete‐Time Systems

AbstractSuccessful implementations of simple direct adaptive control techniques in various domains of application have been presented over the last two decades in the technical literature. The theoretical background concerning basic conditions needed for stability of the controller and the open questions relating the convergence of the adaptive gains have been clarified recently, yet only for the continuous‐time algorithms. Apparently, asymptotic tracking in discrete time systems has been possible only with step input commands and the scope of the so called “almost strictly positive real (ASPR)” condition has also remained not clear. This paper expands the feasibility of discrete simple adaptive control methodology to include any desired input commands and almost all real‐world systems. The proofs of stability are also rigorously revised to solve the ultimate adaptive gain values question that has remained open until now. Finally, a complex algebraic loop that seemed to be inherent in discrete ASPR systems and might prevent the use of passivity properties in discrete systems has also been eliminated.

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.