Direct Adaptive Control Scheme Research Articles

Stable Auto-Tuning of Adaptive Fuzzy/Neural Controllers for Nonlinear Discrete-Time Systems

In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, approximators such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as either the parameterized nonlinear controller or identifier model. In this paper, we present algorithms to tune some of the parameters (e.g., the adaptation gain and the direction of descent) for a gradient-based approximator parameter update law used for a class of nonlinear discrete-time systems in both direct and indirect cases. In our proposed algorithms, the adaptation gain and the direction of descent are obtained by minimizing the instantaneous control energy. We will show that updating the adaptation gain can be viewed as a special case of updating the direction of descent. We will also compare the direct and indirect adaptive control schemes and illustrate their performance via a simple surge tank example.

A direct adaptive neural control scheme with integral terms

A direct adaptive neural control scheme with single and double integral-plus-state (IPS) actions is proposed. The control scheme contains two recurrent trainable neural network (RTNN) models, which are a plant parameter identifier and state estimator, an IPS feedback/feedforward controller, and one or two I-terms. The good performance of the adaptive IPS control scheme is confirmed by closed-loop systems analysis and by simulation results obtained with a MIMO plant, corrupted by noise. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 213–224, 2005.

A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems

A hybrid indirect and direct adaptive fuzzy output tracking control schemes are developed for a class of nonlinear multiple-input-multiple-output (MIMO) systems. This hybrid control system consists of observer and other different control components. Using the state observer, it does not require the system states to be available for measurement. Assisted by observer-based state feedback control component, the adaptive fuzzy system plays a dominant role to maintain the closed-loop stability. Being the auxiliary compensation, H/sup /spl infin// control and sliding mode control are designed to suppress the influence of external disturbance and remove fuzzy approximation error, respectively. Thus, the system performance can be greatly improved. The simulation results demonstrate that the proposed hybrid fuzzy control system can guarantee the system stability and also maintain a good tracking performance.

Direct adaptive fuzzy output tracking control of nonlinear systems

A stable direct adaptive fuzzy output tracking control scheme is developed for the single-input–single-output (SISO) unknown nonlinear systems. Using a high-gain observer, the proposed adaptive fuzzy algorithm does not require the state variables to be measurable. First, a direct adaptive fuzzy state controller is constructed with the aid of its H ∞ control technique to achieve the H ∞ tracking performance. Afterwards, a high-gain observer is used to estimate the system states, by which the above adaptive fuzzy state controller becomes an adaptive fuzzy output feedback control. The proposed control scheme can guarantee the stability of the closed-loop system and the good tracking performance as well.

Hybrid adaptive fuzzy identification and control of nonlinear systems

We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system.

AN ADAPTIVE NEURAL CONTROL SYSTEM OF A DC MOTOR DRIVE.

An improved parallel recurrent neural network with canonical architecture, named Recurrent Trainable Neural Network (RTNN), and an error based back-propagation through time learning algorithm, are applied to a D.C. motor drive identification and control. The unknown nonlinear dynamics of the motor together with the load are identified by the RTNN. The trained RTNN identifier is combined with a desired reference model and a RTNN controller in a direct adaptive control scheme, so in order to achive a desired trajectory tracking of the motor speed and position. Finally, the applicability of the RTNN topology and learning is illustrated by experimental results.

Direct Adaptive Control of Chemical Process Systems

This paper presents a simple direct adaptive control strategy for the operation of chemical processes. An efficient and easy-to-implement learning algorithm is proposed that enables a three-paramet...

Hierarchical adaptive and supervisory control of continuous venovenous hemofiltration

Continuous venovenous hemofiltration (CVVH) is a lifesaving renal replacement therapy used in clinical intensive care settings. Patients undergoing a life threatening illness often develop kidney failure. CVVH performs the blood filtering process while the kidneys recover. Hemofiltration devices which are currently available operate through open-loop fluid flows induced by peristaltic pumps, and possess precisions of /spl plusmn/30 ml of fluid removed per hour. This precision is inappropriate for a neonate, who can have a total blood volume of only 150 ml, and a 15 ml blood volume reduction could cause hypovolemic shock. Because of the patient risk involved in using such equipment, most medical centers measure the fluid removed from the patient on an hourly basis. This measurement step leads to a significant increase in nursing care and therefore increases the cost of the therapy. Presented in the paper is an intelligent CVVH system that deals with accuracy and cost issues while improving the care delivered by timely responding to changes in a patient's cardiovascular state. The intelligence aspect of the system is achieved through a hierarchical control architecture for CVVH. The control methodology uses a direct adaptive control scheme for the peristaltic pumps and a supervisory control algorithm for high-level decisions on the safe operation of the system. Adaptive control of the pumps results in improved accuracy of fluid flow, while the supervisory controller provides greater autonomy which reduces the burden on clinical personnel. The performance of the proposed hierarchical controller is illustrated by experiments on a hemofiltration machine using a simulated patient. These experimental simulations showed that the measured cumulative ultrafiltration after one hour of simulation was only 1.7 ml different from the target value, which is well within the acceptable medical range.

Enhancing the estimation of plant Jacobian for adaptive neural inverse control

To implement the specialized learning of the inverse dynamic neuro-controller for controlling nonlinear plants with noise, it is strongly desirable that an on-line trained neural plant emluator may provide a reasonably good estimation of the plant Jacobian under noise environments. This paper presents an approach for enhancing the estimation of the plant Jacobian which is on-line used in direct adaptive neural inverse control schemes. The estimated teaching signals are obtained by using the input–output data available at each time step, and then they are used to train the neural plant emluator by a new cost function introduced in this work for training the plant emluator. Convergence theorem for the adaptive back-propagation algorithm and stability of the closed-loop control system are established by using the Lyapunov theory. Simulations are conducted for demonstrating the effectiveness of the proposed strategy.

Decentralized model reference adaptive control without restriction on subsystem relative degrees

When the direct model reference adaptive control (MRAC) scheme with first-order local estimators is employed to design totally decentralized controllers, the stability result can only be applied to a system with all of its nominal subsystem relative degrees less than or equal to two. In this paper, this restriction is relaxed and it is achieved by employing the parameter projection together with static normalization. To implement the local controllers, no a priori knowledge of the subsystem unmodeled dynamics and no information exchange between subsystems are required. Global stability is established for the closed-loop system and small in the mean tracking error is ensured. With this analysis, the class of interactions and subsystem unmodeled dynamics can be enlarged to include those having infinite memory.

A new robust direct adaptive controller for discrete-time system with unmodelled uncertainties

Abstract In this paper, a new robust discrete-time direct adaptive control scheme with respect to bounded disturbance and unmodelled dynamics is presented. To cope with the residual error arising from the model-plant mismatch, the integrator is incorporated into controller. The parameters of reduced-order plant model and upper bounds on disturbance and unmodelled dynamics are estimated by utilizing a modified dead zone with adjustable size. The convergence and sufficient condition of robust stability established. This condition determines the admissible upper bound on unmodelled uncertainty, which can be tolerated. The simulation results demonstrating the effectiveness of the proposed adaptive controller are given.

Feedforward-feedback adaptive control design and stability analysis

In this paper, we present the continuous time feedforward-feedback adaptive control problem, and develop a direct adaptive control scheme based on certainty-equivalence. Global stability and convergence is established when the unknown plants satisfy some standard assumptions.

Direct adaptive control of wind energy conversion systems using Gaussian networks

Grid connected wind energy conversion systems (WECS) present interesting control demands, due to the intrinsic nonlinear characteristics of windmills and electric generators. In this paper a direct adaptive control strategy for WECS control is proposed. It is based on the combination of two control actions: a radial basis zfunction network-based adaptive controller, which drives the tracking error to zero with user specified dynamics, and a supervisory controller, based on crude bounds of the system's nonlinearities. The supervisory controller fires when the finite neural-network approximation properties cannot be guaranteed. The form of the supervisor control and the adaptation law for the neural controller are derived from a Lyapunov analysis of stability. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution.

Direct adaptive control of partially known nonlinear systems

A direct adaptive control strategy for a class of single-input/single-output nonlinear systems is presented. The major advantage of the proposed method is that a detailed dynamic nonlinear model is not required for controller design. The only information required about the plant is measurements of the state variables, the relative degree, and the sign of a Lie derivative which appears in the associated input-output linearizing control law. Unknown controller functions are approximated using locally supported radial basis functions that are introduced only in regions of the state space where the closed-loop system actually evolves. Lyapunov stability analysis is used to derive parameter update laws which ensure (under certain assumptions) the state vector remains bounded and the plant output asymptotically tracks the output of a linear reference model. The technique is successfully applied to a nonlinear biochemical reactor model.

Direct neural adaptive control applied to synchronous generator

Application of neural networks to control a synchronous generator based on a direct adaptive control scheme is investigated in this paper. Use of a neural network to model the dynamic system is avoided by making use of the sign of the Jacobian of the plant. This substantially reduces the complexity and the computation time of the control algorithm. The controller is trained on-line using the back-propagation algorithm which gives an adaptive attribute to it. Moreover, the controller does not need the state information and only employs the plant outputs. Simulation results are presented to complement the theoretical discussion.

Multiple neural-network-based adaptive controller using orthonormal activation function neural networks

A direct adaptive control scheme is developed using orthonormal activation function-based neural networks (OAFNN's) for trajectory tracking control of a class of nonlinear systems. Multiple OAFNN's are employed in these controllers for feedforward compensation of unknown system dynamics. Choice of multiple OAFNN's allows a reduction in overall network size reducing the computational requirements. The network weights are tuned on-line, in real time. The overall stability of the system and the neural networks is guaranteed using Lyapunov analysis. The developed neural controllers are evaluated experimentally and the experimental results are shown to support theoretical analysis. The effects of network parameters on system performance are experimentally evaluated and are presented in this research. The superior learning capability of OAFNN's is demonstrated through experimental results. The OAFNN's were able to model the true nature of the nonlinear system dynamics characteristics for a rolling-sliding contact as well as for stiction.

A direct adaptive neural-network control for unknown nonlinear systems and its application

In this paper a direct adaptive neural-network control strategy for unknown nonlinear systems is presented. The system considered is described by an unknown NARMA model, and a feedforward neural network is used to learn the system. Taking the neural network as a neural model of the system, control signals are directly obtained by minimizing either the instant difference or the cumulative differences between a set point and the output of the neural model. Since the training algorithm guarantees that the output of the neural model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the set point. An application to a flow-rate control system is included to demonstrate the applicability of the proposed method and desired results are obtained.

Optimal controller properties from closed-loop experiments

A model-free approach to linear quadratic (LQ) performance assessment is presented in this paper. First a test for LQ optimality, based on closed-loop signals, is presented. Its result indicates whether the controller should be adjusted. From the same test it is possible to determine the closed-loop pole positions which would be obtained by using the LQ optimal controller. These may then be used in a direct adaptive control scheme to adjust the controller. Both state and output feedback are addressed for discrete-time, single-input-single-output (SISO), linear, time-invariant processes.

Direct Adaptive Control of Partially Known Nonlinear Systems

A direct adaptive control strategy for minimum-phase nonlinear systems is presented. The major advantage of the proposed method is that controller design is not based on a detailed nonlinear model. The only information required about the plant is the state vector, the relative degree, and the sign of a Lie derivative which appears in the associated input-output linearizing control law. Unknown controller functions are approximated via locally supported radial basis functions that are introduced only in regions of the state space where the closed-loop system actually evolves. Lyapunov stability analysis is used to derive parameter update laws which ensure (under certain assumptions) the state vector remains bounded and the plant output asymptotically tracks the output of a linear reference model. The technique is successfully applied to a nonlinear bioreactor model.

Nonlinear control structures based on embedded neural system models

This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper.