Application Of Adaptive Control Research Articles

Application of direct adaptive fuzzy slidingmode control into a class of non-affine discrete nonlinear systems

Direct adaptive fuzzy sliding mode control design for discrete non-affine nonlinear systems is presented for trajectory tracking problems with disturbance. To obtain adaptiveness and eliminate chattering of sliding mode control, a dynamic fuzzy logical system is used to implement an equivalent control, in which the parameters are self-tuned online. Stability of the sliding mode control is validated using the Lyapunov analysis theory. The overall system is adaptive, asymptotically stable, and chattering-free. A numerical simulation and an application to a robotic arm with two degrees of freedom further verify the good performance of the control design.

Application of adaptive robust control for electro-hydraulic motion loading system

This paper investigates the problem of the high-performance motion loading control of an electro-hydraulic load simulator (EHLS). To begin with, the non-linear motion loading model of the EHLS was developed, by which the external disturbances caused by actuator active motion and the uncertainties arising from the EHLS were comprehensively considered. To address these uncertainties and disturbances, the adaptive robust torque control algorithm was developed with the motion loading model. In contrast to the available control methods concerning the EHLS, the developed method has two advantages. First, instead of performing the fixed coefficients-based linear feed-forward compensation using the actuator velocity, adaptive non-linear feed-forward compensation is achieved through the back-stepping design procedure. Second, the uncertainties and disturbances including system parametric uncertainties, un-modelled friction dynamics and unknown external disturbance are addressed comprehensively. Besides illustrating the theoretical proof, the effectiveness of the proposed method is verified through comparative experiments.

A Survey of Adaptive Fuzzy Controllers: Nonlinearities and Classifications

Adaptive fuzzy controllers (AFCs), i.e., fuzzy controllers using heuristic expert knowledge and equipped with adaption ability, are widely used in the industry in the present days as they are capable of dealing with ill-formulated nonlinear systems (where exact mathematical formulas are not known) or partially described nonlinear systems. AFCs have been widely deployed to control many nonlinear systems with different types of critical nonlinearities. Over the last two decades, many different approaches toward adaptive fuzzy control have been reported. In this paper, we recapitulate the latest research works related to AFCs and provide an up-to-date survey on the latest development made in adaptive fuzzy control theory and applications based on different types of fuzzy systems considered and nonlinearities associated with the nonlinear system to be controlled. We categorize the different approaches based on different types of nonlinearities and consider basic nonlinear systems, nonlinear systems with external disturbances, nonlinear systems with some special type of nonlinearities, nonaffine nonlinear systems, and strict-feedback nonlinear systems. It turns out that many existing methods are subject to various limitations, and none of them is capable of controlling a general nonlinear systems. Hence, it is still an open problem to design and develop a universal AFC for a general class of nonlinear systems.

Sampled-data feedback and stability for a class of uncertain nonlinear systems based on characteristic modeling method

In this paper, we investigate the tracking problem for a class of second-order uncertain nonlinear systems using sampled-data output feedback. Our controller is designed based on the characteristic modeling method. We first derive the corresponding characteristic model and then give the sampled-data feedback control law, which is referred to as ``golden-section adaptive control based on characteristic models. The closed-loop system is shown to be stable and, concurrently, it is demonstrated that the tracking error can be made arbitrarily small by taking a sufficiently small sampling period. Our results improve upon the findings of previous work by removing the persistent excitation condition, and also lay certain theoretical foundations for practical applications of golden-section adaptive control.

Application of Adaptive Starling-Like Controller to Total Artificial Heart Using Dual Rotary Blood Pumps.

The successful clinical applicability of rotary left ventricular assist devices (LVADs) has led to research interest in devising a total artificial heart (TAH) using two rotary blood pumps (RBPs). The major challenge when using two separately controlled LVADs for TAH support is the difficulty in maintaining the balance between pulmonary and systemic blood flows. In this study, a starling-like controller (SLC) hybridized with an adaptive mechanism was developed for a dual rotary LVAD TAH. The incorporation of the adaptive mechanism was intended not only to minimize the risk of pulmonary congestion and atrial suction but also to match cardiac demand. A comparative assessment was performed between the proposed adaptive starling-like controller (A-SLC) and a conventional SLC as well as a constant speed controller. The performance of all controllers was evaluated by subjecting them to three simulated scenarios [rest, exercise, head up tilt (HUT)] using a mock circulation loop. The overall results showed that A-SLC was superior in matching pump flow to cardiac demand without causing hemodynamic instabilities. In contrast, improper flow regulation by the SLC resulted in pulmonary congestion during exercise. From resting supine to HUT, overpumping of the RBPs at fixed speed (FS) caused atrial suction, whereas implementation of SLC resulted in insufficient flow. The comparative study signified the potential of the proposed A-SLC for future TAH implementation particularly among outpatients, who are susceptible to variety of clinical scenarios.

Guest Editorial

Guest Editorial

A numerical and experimental implementation and integration of Steiglitz–McBride algorithm with the frequency domain IIR filtering technique for active vibration control

In adaptive control applications for noise and vibration, finite ımpulse response (FIR) or ınfinite ımpulse response (IIR) filter structures are used for online adaptation of the controller parameters. IIR filters offer the advantage of representing dynamics of the controller with smaller number of filter parameters than with FIR filters. However, the possibility of instability and convergence to suboptimal solutions are the main drawbacks of such controllers. An IIR filtering-based Steiglitz–McBride (SM) algorithm offers nearly-optimal solutions. However, real-time implementation of the SM algorithm has never been explored and application of the algorithm is limited to numerical studies for active vibration control. Furthermore, the prefiltering procedure of the SM increases the computational complexity of the algorithm in comparison to other IIR filtering-based algorithms. Based on the lack of studies about the SM in the literature, an SM time-domain algorithm for AVC was implemented both numerically and experimentally in this study. A methodology that integrates frequency domain IIR filtering techniques with the classic SM time-domain algorithm is proposed to decrease the computational complexity. Results of the proposed approach are compared with the classical SM algorithm. Both SM and the proposed approach offer multimodal vibration suppression and it is possible to predict the performance of the controller via simulations. The proposed hybrid approach ensures similar vibration suppression performance compared to the classical SM and offers computational advantage as the number of control filter parameters increases.

An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots

Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots.

A Behavioral Adaptive Fuzzy controller of multi robots in a cluster space

Cooperation between autonomous robot vehicles holds several promising advantages like robustness, adaptability, configurability, and scalability. Coordination between the different robots and the individual relative motion represent both the main challenges especially when dealing with formation control and maintenance. Cluster space control provides a simple concept for controlling multi-agent formation. In the classical approach, formation control is the unique task for the multi-agent system. In this paper, the development and application of a novel Behavioral Adaptive Fuzzy-based Cluster Space Control (BAFC) to non-holonomic robots is presented. By applying a fuzzy priority control approach, BAFC deals with two conflicting tasks: formation maintenance and target following. Using priority rules, the fuzzy approach is used to adapt the controller and therefore the behavior of the system, taking into accounts the errors in the formation states and the target following states. The control approach is easy to implement and has been implemented in this paper using SIMULINK real-time platform. The communication between the different agents and the controller is established through Wi-Fi link. Both simulation and experimental results demonstrate the behavioral response where the robot performs the higher priority tasks first. This new approach shows a great performance with a lower control signal when benchmarked with previously known results in the literature.

Unfalsified Adaptive PID Control for Time-Varying Systems Using a Fading Memory Cost Function

In this paper, we present a new cost function based on fading memory and time-window in order to decrease the influence of old data in unfalsified adaptive control applications, where the plant var...

Application of ℒ1 Adaptive Controller for the Design of a Novel Decentralized Leader Follower Formation Algorithm

A novel ℒ1 adaptive based control, augmenting a fixed gain Linear Quadratic Regulator (LQR) control for leader-follower based decentralized formation flying is investigated. In particular, we propose this algorithm to maintain the triangle formation of three micro air vehicles. This work assumes minimal critical flight information exchange among the vehicles. The designed formation control algorithm generates normal and tangential acceleration commands which can be accurately tracked by the inner loop of the followers autopilot. The proposed controller having a combination of both adaptive control and a fixed gain control greatly enhances the tracking performance of the controller. To illustrate our algorithm, simulations results using a fixed wing MAV model are presented.

Anti-synchronization of fractional order chaotic and hyperchaotic systems with fully unknown parameters using modified adaptive control

AbstractThe objective of this article is to implement and extend applications of adaptive control to anti-synchronize different fractional order chaotic and hyperchaotic dynamical systems. The sufficient conditions for achieving anti–synchronization are derived by using the Lyapunov stability theory and an analytic expression of the controller with its adaptive laws of parameters is shown. Theoretical analysis and numerical simulations are shown to verify the results.

Применение метода автономного адаптивного управления для группы робототехнических устройств на примере модели кластера наноспутников

Применение метода автономного адаптивного управления для группы робототехнических устройств на примере модели кластера наноспутников

Geographic scheduling of directional transmission for periodic safety messages in IEEE WAVE

It is typically assumed that safety communication will use omnidirectional broadcast in vehicle-to-vehicle environments. However, in the IEEE Wireless Access in Vehicular Environment standard, some safety application messages will have directional semantics. For example, messages in the cooperative adaptive cruise control application are relevant to vehicles following the transmitter. In this paper, we investigate the impacts of using directional communication that is tailored to such applications. In particular, we show that the directional transmission indeed improves the message delivery probability by reducing message spillover in unnecessary directions. However, we also find that such benefit is not automatically obtained. Analysis reveals that it is crucial that the temporal ordering of the transmission is aligned with the vehicles’ spatial ordering to minimize the hidden terminal losses and fully garner the benefits of directional communication. For a method to ensure that the alignment takes place, we propose an application-level message scheduling solution that utilizes vehicle position information, and demonstrate that it leads to both lower channel utilization and higher message delivery rates than omnidirectional transmission.

Application of Adaptive Idle Speed Control on V2 Engine

<div class="section abstract"><div class="htmlview paragraph">This paper develops an adaptive idle speed control strategy for a V2, 1000 cc four-stroke, water-cooled, port injection SI engine. In order to verify the proposed strategy, the non-dimensional engine model including charging and torque dynamics is established in Matlab/Simulink software based on previously experimental verification. The integration of dynamics above will be a multi-input-single-output (MISO) system, which inputs are throttle angle and spark advance angle, and the output is engine speed. The proposed adaptive controller is developed on the model-based structure. The system parameters are updated by recursive least square (RLS) method so the system is able to represent the actual operation. The updated system parameters adjust control gain by derivation of closed-loop gain and pole placement. This research also applies RLS method in torque load identification, and uses feedforward control module to calculate the desired throttle opening for the corresponding engine torque. The feedforward throttle control makes controller less angle of spark advance, therefore, the tracking time could be reduced. In order to verify the proposed adaptive idle speed controller performance, the conventional proportional integral derivative (PID) idle speed controller is established. The results show the proposed feedforward adaptive controller can obviously improve about 40% of tracking time with ramps load. In addition, when the water pump load is added, the proposed strategy can also improve about 20%. Finally, the results of the proposed strategy are also improved comparing to the conventional PID control as deposits happen.</div></div>

Production & Process Optimization of Micro Alloyed Steel Roller Shaft of an Under Carriage

Manufacturing or production is one of the most important sectors of any field. It involves various steps or processes to convert raw materials into finished products. With the more precise demands of modern engineering products and competition to provide good quality, the surface finish, dimensional accuracy along with metal removal rate (MRR) plays a very important role. The selection of optimum cutting conditions (depth of cut, feed and speed) is an important element of process planning for every machining operation. In order to optimize the output parameters i.e., M R R , power consumption and surface roughness, the process variables are varied. Inspite of major advancements in metal cutting practices, the metal cutting industries continues to suffer from major drawback of not running the machine tools at their optimum operating condition. Furthermore, their prediction helps in the analysis of optimization problems in machining economics, in adaptive control applications, in the formulation of simulation models used in cutting databases. In the present work full factorial

A Statistical Analysis of the NLMS and NLMS Algorithms for Correlated Gaussian Signals DOI: 10.14209/jcis.2005.8

The Nonnalized LMS adaptive algorithm is widely used in real life adaptive filtering and control applications mainly due to its simplicity and robustness to input signal power variations. This last characteristic is a consequence of its intrinsic nonnalization procedure. However, in practical applications it is usual the addition of a small positive constant to the nonnalization factor in order to avoid divisions by zero or overflow. In this case, the adaptive algorithm is named e-NLMS. This work presents a statistical analysis of the e-NLMS for Gaussian input signals. Deterministic recursive equations are obtained for the mean weight behavior and mean square error for a large number of adaptive coefficients. In addition, a closed expression is provided for the steady state misadjustment. Monte Carlo simulations show an excellent agreement between theoretical predictions and the algorithm's behavior in steady state. During transient, the new model is conservative and more accurate than the existing models. The developed equations can be lIsed with e =0 to predict the behavior of the NLMS algorithm. Simulations illustrate the better quality of the new NLMS model when compared to others already available in the literature.

Design and Implementation of Adaptive Neural PID for Non Linear Dynamics in Mobile Robots

In this work, it will be reported original results concerning the application of PID Adaptive Neural controller in mobile robot in trajectory tracking control. In this control strategy the exact dynamical model of the robot will not need to be known and identified. To implement this strategy, two controllers are implemented separately: a kinematic controller and an adaptive neural PID controller. The uncertainty and dynamics variations in the robot dynamic are compensated by an adaptive neural PID controller. The resulting adaptive neural PID controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance. The stability of the proposed technique (based on Lyapunov's theory) was demonstrated. Finally, experiments on a mobile robot have been developed to show the performance of the proposed technique, including the comparison with other controllers.

Application of fuzzy adaptive control to a MIMO nonlinear time-delay pump-valve system

In this paper, a control strategy to balance the reliability against efficiency is introduced to overcome the common off-design operation problem in pump-valve systems. The pump-valve system is a nonlinear multi-input–multi-output (MIMO) system with time delays which cannot be accurately measured but can be approximately modeled using Bernoulli Principle. A fuzzy adaptive controller is applied to approximate system parameters and achieve the control of delay-free model since the system model is inaccurate and the direct feedback linearization method cannot be applied. An extended Smith predictor is introduced to compensate time delays of the system using the inaccurate system model. The experiment is carried out to verify the effectiveness of the control strategy whose results show that the control performance is well achieved.

Adaptive fuzzy control design and applications of uncertain stochastic nonlinear systems with input saturation

In this paper, the problem of adaptive fuzzy output feedback control is investigated for a class of uncertain stochastic nonlinear systems. The stochastic nonlinear systems under study have unknown nonlinear uncertainties, unmodeled dynamics, input saturation and without the direct measurement of state variables. Fuzzy logic systems and a smooth function are first utilized to approximate the unknown nonlinear functions and the input saturation, respectively, and then a fuzzy state observer is designed to estimate the unmeasured states. By combining the adaptive backstepping design technique with the dynamic surface control (DSC) design technique and using the concept of the dynamical signal, a new adaptive fuzzy output feedback tracking control approach is developed. It is proved that all the variables involved in the closed-loop system are semi-globally uniformly ultimately bounded (SUUB) in probability, and also that both observer errors and tracking errors converge to a small neighborhood of the origin by appropriate selecting the design parameters. The proposed adaptive fuzzy control approach is finally applied to the two-stage chemical reactor with recycle streams, and achieves the satisfactory control performance.