Adaptive Control

We consider the tracking problem of unknown, robustly stabilizable, multi-input multi-output (MIMO), affine in the control, nonlinear systems with guaranteed prescribed performance. By prescribed performance we mean that the tracking error converges to a predefined arbitrarily small residual set, with convergence rate no less than a prespecified value, exhibiting maximum overshoot as well as undershoot less than some sufficiently small preassigned constants. Utilizing an output error transformation, we obtain a transformed system whose robust stabilization is proven necessary and sufficient to achieve prescribed performance guarantees for the output tracking error of the original system, provided that initially the transformed system is well defined. Consequently, a switching robust control Lyapunov function (RCLF)-based adaptive, state feedback controller is designed, to solve the stated problem. The proposed controller is continuous and successfully overcomes the problem of computing the control law when the approximation model becomes uncontrollable. Simulations illustrate the approach.

An OSGi (Open Services Gateway Initiative) home gateway system manages the integration of heterogeneous home networks protocols and devices to develop ubiquitous applications. Wired and wireless heterogeneous home networks have different QoS concerns. For instance, jitter and latency are important concerns in web phones, while packet loss ratio is important in on-line video. This study adopts UPnP QoS specification version 1.0 to design an adaptive QoS management mechanism based on the RMD (Resource Management in DiffServ) architecture. This study monitors real-time network traffic, and adaptively controls the bandwidth, to satisfy the minimum but most important quality for each application in home network congestion. Simulation results indicate that the average jitter, latency and packet loss are reduced by 0.1391 ms, 0.0066 s, and 5.43%, respectively. The packet loss ratio is reduced by 4.53%, and the throughput is increased by 1.2% in high definition video stream; the packet loss ratio is reduced by 1.89% for standard definition video stream, and in VoIP (Voice over IP) the jitter and latency are reduced to 0.0407 ms and 0.0209 s, respectively.

Abstract— This paper presents an adaptive control scheme for suppressing jitter in laser beams. The variable-order adaptive controller is based on an adaptive lattice filter that implicitly identifies the disturbance statistics from real-time sensor data. The multi-channel adaptive ...

A globally convergent adaptive control scheme for robot motion control with the following features is proposed. First, the adaptation law possesses enhanced robustness with respect to noisy velocity measurements. Second, the controller does not require the inclusion of high gain loops that may excite the unmodeled dynamics and amplify the noise level. Third, we derive for the unknown parameter design a relationship between compensator gains and closed-loop convergence rates that is independent of the robot task. A simulation example of a two-DOF manipulator features some aspects of the control scheme.

In this paper a new intelligent identification method of uncertainty bound utilizes an adaptive neurofuzzy inference system (ANFIS) in a feedback scheme is proposed. The proposed ANFIS feedback structure performs better in determining the uncertainty bounds with minimum number of iterations and error. In our proposed technique, the intelligent identified uncertainty weighting function is validated utilizing v-gap to ensure the stability of the designed H controlled system. Our proposed intelligent identification of uncertainty bound is demonstrated on a servo motion system. Simulation and experimental results show that the new ANFIS identifier is more reliable and highly efficient in estimating the best uncertainty weighting function for robust controller design.

Introduction The purpose of this paper is to serve as an introduction to neural networks and to sug-gest that neural network architecture can be used for design and application of intelligent control systems [ 11. The neural network ap-proach is introduced by defining the problem ...

Petroleum industry production systems are highly automatized. In this industry, all functions (e.g., planning, scheduling and maintenance) are automated and in order to remain competitive researchers attempt to design an adaptive control system which optimizes the process, but also able to adapt to rapidly evolving demands at a fixed cost. In this paper, we present a multi-agent approach for the dynamic task scheduling in petroleum industry production system. Agents simultaneously insure effective production scheduling and the continuous improvement of the solution quality by means of reinforcement learning, using the SARSA algorithm. Reinforcement learning allows the agents to adapt, learning the best behaviors for their various roles without reducing the performance or reactivity. To demonstrate the innovation of our approach, we include a computer simulation of our model and the results of experimentation applying our model to an Algerian petroleum refinery.

The power density and efficiency of high compression ratio (∼200:1) air compressors/expanders are crucial for the economical viability of a Compressed Air Energy Storage (CAES) system such as the one proposed in [1]. There is a trade-off between power density and efficiency that is strongly dependent on the heat transfer capability within compressor/expander. In previous papers, we have shown that the compression or expansion trajectory can be optimized so that for a given power, the efficiency can be optimized and vice versa. Theoretically, for high compression ratios, the improvement over ad-hoc trajectories can be significant-for example, at the same efficiency of 90%, the power can be increased by 3-5 folds [2], [3], [4], [5]. Yet, the optimal trajectories depend on the heat transfer coefficient profile that is often unknown. In this paper, we focus on the experimental study of an iterative control algorithm to track a compression trajectory that optimizes the efficiency-power trade-off in a liquid piston air compressor. First, an adaptive controller is developed to track any desired compression trajectory characterized by the temperature-volume profile. The controller adaptively estimates the unknown heat transfer coefficient. Second, the estimated heat transfer coefficient from one iteration is then used to estimate the optimal compression trajectory for the next iteration. As the estimate of the heat transfer coefficient improves from one iteration to the next, the quality of the estimated optimal trajectory also improves. This leads to successively improved efficiency. The experimental results of optimal trajectories show up to 2% improvement in compression efficiency compared to linear trajectories in a same power density.

The behavioral framework for modelling and control is a framework that does not distinguish a priori between input and output of a system. Control in the behavioral framework does not make use of the intelligent control scheme consisting of observation of system output and feedback. Instead it relies on the interconnection of the to-be-controlled system to an appropriate controller, yielding the desired overall behaviour. In this paper, such a scheme is presented and compared to a feedback control scheme based on a numerical study. A focussed introduction into the behavioral framework with the concepts important for this paper is presented.

In recent years, much attention has been focused upon predictive control of nonlinear systems. The implementation of such a control strategy for real processes has greatly improved their performance. This paper deals with a model-based predictive control (MBPC) strategy using a generalised Hammerstein model and its application to the temperature control of a semibatch reactor. Both unconstrained and constrained adaptive control problems are considered. A simple identification method based on the weighted recursive least squares method (WRLS) is used to estimate the model parameters on-line. An indirect adaptive nonlinear controller is designed by combining the predictive controller with an indirect parameter estimation algorithm. This adaptive scheme has been applied for the control of a semi-batch chemical reactor. Experimental results show that the performance of the generalised Hammerstein MBPC (NLMBPC) was significantly better than that of a linear model predictive controller (LMBPC).

This paper presents a control strategy for the coordination of multiple mobile robots. A combination of the virtual structure and path following approaches is used to derive the formation architecture. A formation controller is proposed for the kinematic model of two-degree-of-freedom unicycle-type mobile robots. The approach is then extended to consider the formation controller by taking into account the physical

This paper presents an adaptive position control scheme for DC motors based on an on-line closed loop continuous-time identiflcation method. A fast, non-asymptotic, algebraic identiflcation method is used to estimate simultaneously the unknown sys- tem parameters and the unmeasured states to update an adaptive position control scheme.

We consider the problem of force and position regulation for a robot finger with a soft tip in contact with a surface with unknown geometrical characteristics. An adaptive controller is proposed, and the asymptotic convergence of the applied force error and the estimated position error of the tip to zero is shown for the spatial case. Simulation results demonstrate the controller performance.

This paper presents an adaptive PWM control scheme of four-cell interleaved boost-type switching-mode rectifier, used as an input stage of auxiliary power supply of electrical vehicles, and its operation under regular, as well as under specific operating conditions-failure of one or more IGBTs.

Irrigation control strategies may be used to improve the site-specific irrigation of cotton via lateral move and centre pivot irrigation machines. A simulation framework 'VARIwise' has been created to aid the development, evaluation and management of spatially and temporally varied site-specific irrigation control strategies. VARIwise accommodates sub-field scale variations in all input parameters using a 1 m 2 cell size, and permits application of differing control strategies within the field, as well as differing irrigation amounts down to this scale. In this paper the motivation and objectives for the creation of VARIwise are discussed, the structure of the software is outlined and an example of the use and utility of VARIwise is presented. Three irrigation control strategies have been simulated in VARIwise using a cotton model with a range of input parameters including spatially variable soil properties, non-uniform irrigation application, three weather profiles and two crop varieties. The simulated yield and water use efficiency were affected by the combination of input parameters and the control strategy implemented.

A VLSI implementation of an adaptive controller performing gradient descent optimization of external performance metrics using parallel synchronous detection is presented. Real-time model-free gradient estimation is done by perturbation of the metrics' control parameters with narrow-band deterministic dithers resulting in fast adaptation and robust performance. A fully translinear design has been employed for the architecture, making the controller operation scalable within a very wide range of frequencies and control bandwidths, and, therefore customizable for a variety of systems and applications. Experimental results from a SiGe BiCMOS implementation are provided demonstrating the broadband and high-speed performance of the controller.

With the increasing number of vehicles on the road, it has become difficult to properly control the flow of traffic especially in large cities like Lahore. The proposed system uses a digital camera mounted on a stepper motor to sense the traffic on the road. The decision to open the lane is done after monitoring the traffic load. The heavily loaded side is turned on for a longer time. Thus the system is intelligent because it is not using the fixed time frame. The system is also integrated with GSM, thus the signals can also be ...

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The present study deals with the analysis of a Lotka-Volterra model describing competition between tumor and immune cells. The model consists of differential equations with piecewise constant arguments and based on metamodel constructed by Stepanova. Using the method of reduction to discrete equations, it is obtained a system of difference equations from the system of differential equations. In order to get local and global stability conditions of the positive equilibrium point of the system, we use Schur-Cohn criterion and Lyapunov function that is constructed. Moreover, it is shown that periodic solutions occur as a consequence of Neimark-Sacker bifurcation.

This paper proposes a novel optimal adaptive event-triggered
control algorithm for nonlinear continuous-time systems. The goal
is to reduce the controller updates, by sampling the state only
when an event is triggered to maintain stability and optimality.
The online algorithm is implemented based on an actor/critic
neural network structure. A critic neural network is used to
approximate the cost and an actor neural network is used to
approximate the optimal {event-triggered controller}. Since in the
algorithm proposed there are dynamics that exhibit continuous
evolutions described by ordinary differential equations and
instantaneous jumps or impulses, we will use an impulsive system
approach. A Lyapunov stability proof ensures that the closed-loop
system is asymptotically stable. Finally, we illustrate the effectiveness of the proposed solution compared to a time-triggered controller.

Citation: Kyriakos G. Vamvoudakis. Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems.  IEEE/CAA Journal of Automatica Sinica, 2014, 1(3): 282-293

The paper investigates the design of Adaptive Feedforward Cancellation (AFC) algorithms with sinusoidal regressors for repetitive control. Such adaptive algorithms are equivalent to linear controllers based on the Internal Model Principle (IMP). Using this equivalence and root locus rules, the phase advance of the regressor of the adaptive algorithm can be chosen to maximize the phase margin at low gains. A surprising result is that selecting the optimal phase advance is equivalent to placing a zero in the open right half-plane in certain cases.

A multiple-model adaptive controller is developed using the Self-Organizing Map (SOM) neural network. The considered controller which we name it as Multiple Controller via SOM (MCSOM) is evaluated on the pH neutralization plant. An improved switching algorithm based on excitation level of plant has also been suggested for systems with noisy environments. Identification of pH plant using SOM is discussed and performance of the multiple-model controller is compared to the Self Tuning Regulator (STR) controller.

The requirement for high accuracy in dynamic positioning of remotely operated vehicles (ROV), especially when tasks close to underwater structures have to be performed, demands high precision of sensor systems. Taut-wire and passive arm systems can satisfy this demand in measuring ROVs positions and orientations relative to a structure. However, the main drawback of these sensor systems is that additional forces act on ROVs due to the mechanical connection. In order to solve this problem, an adaptive PD controller is proposed and designed for dynamic positioning of ROVs working in close proximity of structures. Invoking the adaptation law, these additional forces caused by the passive arm and umbilical, and even by the uncertainties in gravity and buoyancy can be identified and compensated. By choosing an adequate Lyapunov candidate function, the system's stability is proven. The effectiveness of this design control method is demonstrated by means of numerical simulations and experiments.

The maneuvering problem involves two tasks. The first, called the geometric task, is to force the system output to converge to a desired path continuously parametrized by a scalar θ. The second task, called the dynamic task, is to satisfy a desired dynamic behavior along the path. In this paper, this dynamic behavior is further specified as a speed assignment for θ(t). While the main concern is to satisfy the geometric task, the dynamic task ensures that the system output follows the path with the desired speed. An adaptive recursive design technique is developed for a parametrically uncertain nonlinear plant describing the dynamics of a ship. First the geometric part of the problem is solved. Then an update law is constructed that bridges the geometric design with the dynamic task. The design procedure is performed and tested by several experiments for a model ship in a marine control laboratory. c°2004 Elsevier Science Ltd. All rights reserved.

The International Journal of Chaos, Control, Modelling and Simulation is a Quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Chaos Theory, Control Systems, Scientific Modelling and Computer Simulation. In the last few decades, chaos theory has found important applications in secure communication devices and secure data encryption. Control methods are very useful in several applied areas like Chaos, Automation, Communication, Robotics, Power Systems, and Biomedical Instrumentation. The journal focuses on all technical and practical aspects of Chaos, Control, Modelling and Simulation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on chaotic systems and applications, control theory, process control, automation, modelling concepts, computational methods, computer simulation and establishing new collaborations in these areas.

Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Chaos Theory, Control Systems, Modelling and Simulation Techniques.

Continuously increasing energy standards have driven the need for increasing the efficiency of buildings. Most enhancements to building efficiency have been a result of changes to the heating/cooling systems, improvements in construction materials, or building design code improvements. These approaches neglect the way in which air is dispersed into individual rooms or in a building -i.e., the ducting system. This opens up the possibility of significant energy savings by making ductwork systems lighter and better insulating while ensuring cost effectiveness.

5th International Conference on Applied Mathematics and Sciences (AMA 2021)will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Mathematics and Sciences. The conference documents practical and theoretical results which make a fundamental contribution for the development of Mathematics and Sciences. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on Mathematics and Sciences advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Mathematics.

In this study, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL strategy consists of intelligent and conventional controllers in feedforward and feedback paths, respectively. In this strategy, a conventional feedback controller (CFC), i.e. proportional, integral and derivative (PID) controller, is essential to guarantee global asymptotic stability of the overall system; and an intelligent feedforward controller (INFC) is adopted to learn the inverse of the controlled system. Therefore, when the INFC learns the inverse of controlled system, the tracking of reference signal is done properly. Generally, the CFC is designed at nominal operating conditions of the system and, therefore, fails to provide the best control performance as well as global stability over a wide range of changes in the operating conditions of the system. So, in this study a supervised controller (SC), a lookup table based controller, is addressed for tuning of the CFC. During abrupt changes of the power system parameters, the SC adjusts the PID parameters according to these operating conditions. Moreover, for improving the performance of overall system, a recurrent fuzzy neural network (RFNN) is adopted in INFC instead of the conventional neural network, which was used in past studies. The proposed FEL controller has been compared with the conventional feedback error learning controller (CFEL) and the PID controller through some performance indices.

We investigate the problem of adaptive traffic light control of multiple intersections using real-time traffic data collected by a wireless sensor network (WSN). Previous studies mainly focused on optimizing the intervals of green lights in fixed sequences of traffic lights and ignored the traffic flow's characteristics and special traffic circumstances. In this paper, we propose an adaptive traffic light control scheme that adjusts the sequences of green lights in multiple intersections based on the real-time traffic data, including traffic volume, waiting time, number of stops, and vehicle density. Subsequently, the optimal green light length can be calculated from the local traffic data and traffic condition of neighbor intersections. Simulation results demonstrate that our scheme produces much higher throughput, lower average waiting time and fewer number of stops, compared with three control approaches: the optimal fixed-time control, an actuated control and an adaptive control.

This paper presents the Static Synchronous Compensator's (Stat-Com) voltage regulation by a B-Spline neural network. The fact that the electric grid is a non-stationary system, with varying parameters and configurations, adaptive control schemes may be advisable. Thereby the control technique must guarantee its performance on the actual operating environment where the Stat-Com is embedded. An artificial neural network (ANN) is trained to foresee the device's behavior and to tune the corresponding controllers. Proportional-Integral (PI) and B-Spline controllers are assembled for the StatCom's control, where the tuning of parameters is based on the neural network model. Results of the lab prototype are exhibited under different conditions.

This paper has implemented an adaptive control technique for Continuous Stirred Tank Reactor (CSTR) process. The transfer function representing the dynamics of a CSTR process was obtained. The system was modeled in Simulink and simulated without being compensated. The output response in terms of concentration to unit step input revealed that a compensator was needed to improve the transient characteristics performance. A Model Reference Adaptive Controller (MRAC) was designed based on the MIT rule and integrated with CSTR process. The Simulink model of the MRAC control for CSTR process is developed. The performance of the MRAC controller was analyzed in terms of Integral Square Error (ISE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). The simulation results obtained showed that the controller provided robust and improved tracking error for the range of gain selected, with gain adjusted to 25 providing the most effective and robust tracking error performance with ISE of 0.3324, IAE of 0.4408, and ITAE of 0.3945.

—Adaptive traffic control system is the latest generation of traffic control systems that operates, manages and control the signalized intersections with/without coordination. ATAK is one of the adaptive traffic control system that is developed by ISBAK Inc., in Turkey. It uses the genetic and fuzzy-logic algorithm as an optimization tool. The genetic algorithm is used for optimizing signal timings and fuzzy logic is used for controlling the exceptional cases during the operation of traffic control devices. This study defines the ATAK system as an traffic management tool at signalized intersections. The performance of the system is outlined. Application of the ATAK system at isolated and coordinated intersection showed that the performance improvement is about 10% in terms of cycle time and travel time improvement is about 15%.

In Part I of this paper, we presented the fundamentals of the theory of IDA-PBC. In this part, we discuss the main new results and the practical applications of this control design approach as well as the current open problems and future directions.

This work presents a novel predictive model-based proportional integral derivative (PID) tuning and control approach for unknown nonlinear systems. For this purpose, an NARX model of the plant to be controlled is obtained and then it used for both PID tuning and correction of the control action. In this study, for comparison, neural networks (NNs) and support vector machines (SVMs) have been used for modeling. The proposed structure has been tested on two highly nonlinear systems via simulations by comparing control and convergence performances of SVM-and NN-Based PID controllers. The simulation results have shown that when used in the proposed scheme, both NN and SVM approaches provide rapid parameter convergence and considerably high control performance by yielding very small transient-and steady-state tracking errors. Moreover, they can maintain their control performances under noisy conditions, while convergence properties are deteriorated to some extent due to the measurement noises.

This thesis is focused on modeling, control, stability, and power management of electronically interfaced Distributed Energy Resource (DER) units for microgrids. Voltage amplitude and frequency regulation in an islanded microgrid is one of the main control requirements. To that end, first a mathematical model is developed for an islanded DER system and then, based on the developed model, amplitude and frequency control schemes are proposed for (i) balanced and linear loads and (ii) unbalanced and nonlinear loads. The proposed control strategy for unbalanced and nonlinear loads, utilizes repetitive control scheme to reject the effects of unbalanced and/or distorted load currents. Moreover, a new approach is proposed to maintain the effectiveness of the repetitive control under variable-frequency operational scenarios. The thesis also presents an adaptive feedforward compensation strategy to enhance the stability and robustness of the droop-controlled microgrids to droop coefficients and network uncertainties. The proposed feedforward strategy preserves the steady-state characteristics that the conventional droop control strategy exhibits and, therefore, does not compromise the steadystate power shares of the DER systems or the voltage/frequency regulation of the microgrid. Finally, a unified control strategy is proposed to enable islanded and grid-connected operation of DER systems, with no need to detect the microgrid mode of operation or to switch between different controllers, simplifying the control of the host microgrid. The effectiveness of the proposed control strategies are demonstrated through time-domain simulation studies conducted in the PSCAD/EMTDC software environment.

In this paper we present a method for adaptive feedback linearization contorl of CSTR. Because of the nature of CSTR, the parameters of the system, like feed flowrate, feed temperature, concentration of components in the feed and inlet temperature of coolant, may change with time. So for detecting these changes, online system identification is performed and the NARMA-L2 model of

The problem of balancing an inverted pendulum has been a benchmark problem in demonstrating various control design techniques. The principal reasons for it's popularity are its nonlinear and unstable characteristics. In this paper a single link inverted pendulum system has been chosen and adaptive control algorithm has been implemented for stabilization of the system so that pendulum is always maintained erected in its upright (inverted) position. Model Reference Adaptive Control (MRAC) method is implemented using MIT rule. Minimization of cost function is performed and system response has been observed for different adaption gain. After system modeling the whole process is done in MATLAB Simulink.

In recent years, it is remarkable to see the increasing number of studies related to the theory and application of fractional order controller (FOC), specially controller, in many areas of science and engineering. Research activities are focused on developing new analysis and design methods for fractional order controllers as an extension of classical control theory. In this paper, a new tuning method for fractional order proportional and derivative ( ) or FO-PD controller is proposed for a class of typical second-order plants. The tuned FO-PD controller can ensure that the given gain crossover frequency and phase margin are fulfilled, and furthermore, the phase derivative w. r. t. the frequency is zero, i.e., the phase Bode plot is flat at the given gain crossover frequency. Consequently, the closed-loop system is robust to gain variations. The FOC design method proposed in the paper is practical and simple to apply. Simulation and experimental results show that the closed-loop system can achieve favorable dynamic performance and robustness.