Direct Adaptive Control Research Articles

Enhancing the performance of a deregulated nonlinear integrated power system utilizing a redox flow battery with a self-tuning fractional-order fuzzy controller

Load frequency regulation is one of the most vital and complex ancillary services in a deregulated power system. Increasing penetration from renewable energy sources in an integrated power system (IPS) further escalates the related control complexity due to a considerable decrement in IPS’s effective inertia. This may incur additional costs and can even lead to the destabilization of IPS. To overcome these problems in frequency regulation, this work proposes and investigates the use of an intelligent, direct adaptive control scheme, i.e., self-tuning fractional order fuzzy PID (STFOFPID) controller with and without the presence of a recently devised energy storage unit, i.e., the redox flow battery. The IPS’ efficacy with the STFOFPID controller is validated for various contracts in a deregulated operation mode for considered three area IPS. Extensive simulation studies are carried out, and detailed comparative studies have been drawn with conventional PID and fractional order PID controllers for load frequency regulation in Poolco, bilateral, and contract-violation mode of operation. Robustness analysis in terms of parametric variations in different nonlinearities present in a reheated thermal power plant is also carried out, and the efficacy of the STFOFPID controller is established using a thorough quantitative comparative analysis. The real-time digital simulation validation of the investigated control structure has been carried out on OPAL-RT 4150 based on Xilinx Kintex-7 FPGA board with INTEL multi-core processor.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm

This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed to estimate the cost function in the design procedure. Furthermore, the direct adaptive optimal controllers are designed by combining the backstepping technique with the reinforcement learning algorithm. Comparing the existing reinforcement learning algorithm, the computational burden can be effectively reduced by using the method of less learning parameters. The adaptive auxiliary signals are established to compensate for the influence of the dead zones and actuator faults on the control performance. Based on the Lyapunov stability theory, it is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.

Gradient Descent Algorithm-Based Adaptive NN Control Design for an Induction Motor

This paper investigates the position tracking control problem for an induction motor with completely unknown nonlinearities. A novel control scheme is presented by using the gradient descent algorithm, adaptive backstepping technique, neural networks (NNs), and extended differentiators. Differing from some existing results which only designed the adaption of weights of NNs, our proposed control strategy provides training for all the parameters of NNs, including the basis functions' widths and centers. With the help of the gradient descent algorithm and Lyapunov stability criterion, the convergence of both the NN approximation error and the system tracking error can be guaranteed. Finally, a simulation example shows the advantages of our proposed method compared with direct adaptive NN control strategy.

Optimal tuned direct adaptive controller for seismic protecting of structures

In the past few years, many studies have proved the efficiency of Simple Adaptive Control (SAC) in mitigating earthquakes’ damages to building structures. Nevertheless, the weighting matrices of this controller should be selected after a large number of sensitivity analyses. This step is time-consuming and it will not necessarily yield a controller with optimum performance. In the current study, an innovative method is introduced to tuning the SAC’s weighting matrices, which dispenses with excessive sensitivity analysis. In this regard, we try to define an optimization problem using intelligent evolutionary algorithm and utilized control indices in an objective function. The efficiency of the introduced method is investigated in 6-story building structure equipped with magnetorheological dampers under different seismic actions with and without uncertainty in the model of the proposed structure. The results indicate that the controller designed by the introduced method has a desirable performance under different conditions of uncertainty in the model. Furthermore, it improves the seismic performance of structure as compared to controllers designed through sensitivity analysis.

An adaptive controller for human lower extremity exoskeleton robot

Exoskeleton robot-assisted physical therapy has recently been studied extensively due to its proven effectiveness in providing different forms of physical therapy at any stage of physical recovery. The efficacy of robot-assisted physical therapy depends on the maneuverability of the robot during the rehabilitation application. Robot dynamics is inherently nonlinear. Often, robot control algorithms are developed based on an approximate robot dynamic model which leads to system instability and tracking errors. Accurately determining a rehabilitation robot's payload (human limb masses and inertial properties) is frequently impractical. An adaptive control scheme can handle modeling errors very efficiently. In this paper, a 7 degrees of freedom (DOF) human lower extremity dynamic model was developed using the Newton Euler’s method. To simulate joint friction, a realistic friction model is included. A direct adaptive controller is designed so that the robot can follow the prescribed trajectory with high speed and accuracy. A total of 31 model parameters were considered for adaption. To ensure system stability, the controller's adaptive gains are determined based on the Lyapunov stability approach. Simulation results show excellent tracking performance of the developed controller in the presence of joint friction.

Direct Adaptive Fuzzy Control Scheme With Guaranteed Tracking Performances for Uncertain Canonical Nonlinear Systems

In our recent work, we propose an indirect adaptive fuzzy control scheme for uncertain unparametrizable nonlinear systems, which ensures that the number of adaptive laws does not increase with the number of fuzzy rules, and the time derivative of the chosen Lyapunov function is negative semidefinite. However, the scheme involves a class of high-order smooth functions and their time derivatives, which can make the controller structure become sophisticated especially when the relative degree of system is quite large. To overcome this problem, in the article, we propose a new direct adaptive fuzzy control scheme based on a class of reduced-order smooth functions. With the scheme, no partial-derivative term is involved in controller and virtual controllers, only one adaptive law is used regardless of the increase of fuzzy rules, and also the time derivative of Lyapunov function can be ensured negative semidefinite. It is proved that all closed-loop signals are bounded, and the output tracking error converges to a prescribed interval asymptotically. The transient tracking performance and robustness of the proposed control scheme are also considered. The effectiveness of the obtained results is illustrated by two practical control systems.

DAIC Design for an Electro-Hydraulic Actuator via FASS-NLMS Algorithm

In this paper, it is proposed a new version of Normalized Least Mean Square (NLMS) algorithm, performed through a linguistic description based on the expert’s knowledge and implemented in a fuzzy rule base. In order to obtain a good trade-off between convergence speed and steady-state Mean Square Error (MSE) during the update of the estimate of the weights vector, it is used a Mamdani Fuzzy Inference System (MFIS) to adapt the step size of NLMS algorithm, whose inputs are the squared error and the normalized time instant. The validation of the proposed optimization algorithm was performed in Direct Adaptive Inverse Control (DAIC) design and applied to a non-minimum phase plant referring to an Electro-Hydraulic Actuator (EHA) system, shuch that a disturbance signal was added to the control signal.

АДАПТИВНОЕ УПРАВЛЕНИЕ ТЕХНОЛОГИЧЕСКИМИ ПРОЦЕССАМИ С ЗАПАЗДЫВАНИЕМ ПО УПРАВЛЕНИЮ

A method for the synthesis of adaptive systems by technological objects with control delay is proposed in the presence of a significant uncertainty in the setting of the delay value and time-variable coefficients of the linear inertia part model, which ensures the robustness of the system with respect to the delay. Adaptive identification-type systems and direct adaptive control systems without predictor are considered. The system with roughness in relation to the change in the delay value is used as the main contour

Direct Adaptive Control of Neural Network of Magnetic Levitation System of CNC Machine Tool Linear Synchronous Motor

Direct Adaptive Control of Neural Network of Magnetic Levitation System of CNC Machine Tool Linear Synchronous Motor

A Lyapunov-based direct adaptive controller for the suppression and synchronization of a perturbed nuclear spin generator chaotic system

This article designs and synthesizes a new Lyapunov-based robust direct-adaptive controller (RDAC) and investigates the control and synchronization of chaos in the nuclear spin generator chaotic (NSGC) system. The inevitable time-varying external disturbances and model uncertainties perturb the NSGC system. The nonlinear terms, external disturbances, model uncertainties, and plant's parameters are unknown and bounded. Avoiding the cancelation of the nonlinear terms of the plant by the controller makes the closed-loop robust stable in the presence of unknown parametric uncertainties; this concept blooms base for efficient control law design. The proposed RDAC eradicates the effects of the time-varying unknwon external disturbances and model uncertainties and accomplishes quick and smooth convergence of the state varaible (error vector) trajectories to the origin with reduced oscillations. Based on the Lyapunov function principle, the article describes a detailed analysis of the closed-loop stability. It provides suitable adaptive laws that estimate the upper bound of unknown controller parameters, external disturbances, and model uncertainties. The computer simulation results endorse the theoretical analysis, and the comparative study highlights the benefits.

Neural Network based Direct MRAC Tech-nique for Improving Tracking Performance for Nonlinear Pendulum System

This paper investigates the application of a neural network-based model reference adaptive intelligent controller for controlling the nonlinear systems. The idea is to control the plant by minimizing the tracking error between the desired reference model and the nonlinear system using conventional model reference adaptive con-troller by estimating the adaptation law using a multilayer backpropagation neural network. In the conventional model reference adaptive controller block, the controller is designed to realize the plant output converges to reference model output based on the plant, which is linear. This controller is effective for controlling the linear plant with unknown parameters. However, controlling of a nonlinear system using MRAC in real-time is difficult. The Neural Network is used to compensate the nonlinearity and disturbance of the nonlinear pendulum that is not taken into consideration in the conventional MRAC therefore, the proposed paper can significantly improve the system behaviour and force the system to behave the reference model and reduce the error between the model and the plant output. Adaptive law using Lyapunov stability criteria for updating the controller parameters online has been formulated. The behaviour of the proposed control scheme is verified by developing the simulation results for a simple pendulum. It is shown that the proposed neural network-based Direct MRAC has small rising time, steady-state error and settling time for a different disturbance than Conventional Direct MRAC adaptive control.

Direct Adaptive Fuzzy Tracking Control of Non-affine Stochastic Nonlinear Time-Delay Systems

In this research, the problem of direct adaptive fuzzy control based on state feedback for a class of non-affine stochastic nonlinear systems with time delay is considered. Fuzzy logic systems (FLSs) and mean value theorem are utilized to overcome the design difficulties appeared in the unknown nonlinearities and non-affine structure, respectively. With the utilization of adaptive control and backstepping design framework, a direct adaptive fuzzy control method is developed. Through Lyapunov analysis, the presented controller can guarantee that the variables in the closed-loop system are all bounded in the fourth-moment, in the mean time, the output signal can track the given reference signal by appropriately selecting the design parameters. Simulation results are provided to illustrate the correctness of the theoretical results.

Distributed model reference adaptive containment control of heterogeneous multi-agent systems with unknown uncertainties and directed topologies

In this paper, the containment control problem of heterogeneous uncertain high-order linear Multi-Agent Systems (MASs) is addressed and solved via a novel fully-Distributed Model Reference Adaptive Control (DMRAC) approach, where each follower computes its adaptive control action on the basis of local measurements, information shared with neighbors (within the communication range) and the matching errors w.r.t. its own reference model, without requiring any previous knowledge of the global directed communication topology structure. The approach inherits the robustness of the direct model reference adaptive control (MRAC) scheme and allows all agents converging towards the convex hull spanned by leaders while fulfilling at the same time local additional performance requirements at single-agent level, such as prescribed settling time, overshoot, etc. The asymptotic stability of the whole closed-loop network is analytically derived by exploiting the Lyapunov theory and the Barbalat lemma, hence proving that each follower converges to the convex hull spanned by the leaders, as well as the boundedness of the adaptive gains. Extensive numerical analysis for heterogeneous MAS composed of stable, unstable and oscillating agent dynamics are presented to validate the theoretical framework and to confirm the effectiveness of the proposed approach.

A novel disturbance rejection factor based stable direct adaptive fuzzy control strategy for a class of nonlinear systems

AbstractThis paper proposes a unique disturbance rejection factor (DRF) based design of direct stable adaptive fuzzy logic controllers (AFLCs) for a class of non‐linear systems with large and fast disturbances. The proposed AFLCs are realized by employing hybrid combinations of Lyapunov theory based local adaptation and harmony search algorithm based global optimization technique. These hybrid AFLCs are designed with the objective of optimizing both the structure and free parameters of it with guaranteed stability and, at the same time, simultaneously achieving satisfactory tracking performance and disturbance rejection. The novelty of the proposed work lies in the fact that, in a bid to perform the disturbance rejection, the nature of the disturbance itself is used in designing the tracking control law. The proposed DRF based hybrid stable AFLCs are implemented for several benchmark case studies and extensive performance evaluations demonstrate their usefulness.

Improvement of transient performance in MRAC by memory regressor extension

The paper addresses the problem of transient performance improvement of direct model reference adaptive control (MRAC) of discrete linear time-invariant (LTI) plants. Two solutions to the problem are proposed and use the certainty equivalence principle and the idea of regressor recording over a past period of time. The first solution is based on the principle of augmented error, while the second one uses new scheme of high order tuner generating predicted values of adjustable parameters. The recording of the past regressor is provided by application of a special SISO filter (linear operators with “memory”) to the closed-loop error model. It is proven and demonstrated by numerical examples that the proposed solutions can provide asymptotic (not exponential) convergence of the adjustable parameters under some simple condition which is weaker than the persistent excitation one. The proposed solution can be considered as a generalization of the identification/adaptation algorithm proposed by Kreisselmeier for continuous systems [21, 22].

Combined indirect & direct adaptive fuzzy decentralised control of large-scale systems with strong interconnection and its application

To achieve better adaptation properties, the proposed controller is a weighted combination of indirect and direct adaptive fuzzy controls such that both fuzzy descriptions and control rules can be incorporated under a united controller design at the same time. By using fuzzy systems and robust control technique, the interconnections between subsystems are extended to general unknown nonlinear functions. No a priori knowledge of lower and upper bounds on lumped uncertainties is required to construct each local controller. The resulting closed-loop large-scale system is proved to be asymptotically stable. The controller design is applicable to an automated highway system, and simulation results confirm its practical usefulness.

Data-driven adaptive predictive control for an activated sludge process

Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.

Adaptive Actuator Failure Compensation Control Schemes for Uncertain Noncanonical Neural-Network Systems.

In this article, direct adaptive actuator failure compensation control is investigated for a class of noncanonical neural-network nonlinear systems whose relative degrees are implicit and parameters are unknown. Both the state tracking and output tracking control problems are considered, and their adaptive solutions are developed which have specific mechanisms to accommodate both actuator failures and parameter uncertainties to ensure the closed-loop system stability and asymptotic state or output tracking. The adaptive actuator failure compensation control schemes are derived for noncanonical nonlinear systems with neural-network approximation, and are also applicable to general parametrizable noncanonical nonlinear systems with both unknown actuator failures and unknown parameters, solving some key technical issues, in particular, dealing with the system zero dynamics under uncertain actuator failures. The effectiveness of the developed adaptive control schemes is confirmed by simulation results from an application example of speed control of dc motors.

Robust output voltage control of a high gain DC–DC converter under applied load and input voltage uncertainties

In this study, using robust and adaptive approaches, two controllers are designed to compensate for the applied load and input voltage uncertainties of a high voltage gain dc–dc converter. These controllers are designed based on measuring only the output voltage of the converter. The permissible ranges of the uncertainties are calculated using dynamics equations analysis while practical limitations are considered. In the first approach, an input multiplicative uncertainty is considered for the model of the converter by linearisation of non-linear equations as well as using permissible ranges of the uncertainty. Then, a robust H∞ controller is designed. In the second approach, the converter model is considered as a first-order dynamics model with the unknown parameters and an unknown additive term while a direct robust model reference adaptive controller is designed for it. Finally, the non-linear equations of the converter are simulated and the performances of the controllers are compared with each other. The control methods are then implemented on a 200 W prototype of a converter for evaluating the simulations results.

Direct fractional order MRAC adaptive control design for a class of fractional order commensurate linear systems

In this paper, we introduce a direct fractional order adaptive control design based on model reference adaptive control (MRAC) structure for a class of commensurate fractional order linear systems with an arbitrary relative degree, and whose parameters are unknown. By generalising the application of standard direct MRAC strategy to plants described by fractional order models, we develop a fractional adaptive control scheme (FOMRAC) based on the output feedback. We also define an adaptation control law ensuring the stability of the closed-loop system and the good tracking of the reference trajectory. The asymptotic stability of the fractional order control system is proven using an extension of the Lyapunov theorem. Simulation results show the effectiveness of the proposed control method even for plants with model parametric variations and additive noises.