Real-time Adaptive Control Research Articles

Optimal Adaptive Robust Controller for Hydroturbine Regulation

This paper presents an optimal adaptive robust control algorithm for the unknown systems with nonlinear mismatched uncertainties.The robustness condition of the developed adaptive control algorithm is obtained and it is shown that choosing the fast reference model is one of the ways of enhancing robustness of adaptive robust control systems. Using this principle, a real-time adaptive controller for the speed regulation of hydraulic turbine is built and the desirable experiment results is obtained.

Maximum likelihood estimation using square root information filters

The maximum likelihood parameter estimation algorithm is known to provide optimal estimates for linear time-invariant dynamic systems. However, the algorithm is computationally expensive and requires evaluations of the gradient of a log likelihood function and the Fisher information matrix. By using the square-root information filter, a numerically reliable algorithm to compute the required gradient and the Fisher information matrix is developed. The algorithm is a significant improvement over the methods based on the conventional Kalman filter. The square-root information filter relies on the use of orthogonal transformations that are well known for numerical reliability. This algorithm can be extended to real-time system identification and adaptive control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Real-time modelling and adaptive control of Czochralski growth

A controller based on experimental real-time modelling of the system was developed for Czochralski growth of Bi 4Ge 3O 12 and LiTaO 3 crystals. The dynamic behaviour of the growth process was characterized by a discrete-time linear ARMA (2, 3) model which relates the heating power to the crucible weight error. Different types of theoretically predicted behaviour were observed in our experiments. The pole and zero maps of the ARMA models show that unstable and oscillating behaviour may occur indicating different states of the system. The interpretation of the models in the time domain makes the effect of the meniscus force visible which has a particular importance during necking. The spectral sensitivity of the models can also be determined which is useful when adjusting the control parameters.

Neutral learning of constrained nonlinear transformations

Two issues that are fundamental to developing autonomous intelligent robots, namely rudimentary learning capability and dexterous manipulation, are examined. A powerful neural learning formalism is introduced for addressing a large class of nonlinear mapping problems, including redundant manipulator inverse kinematics, commonly encountered during the design of real-time adaptive control mechanisms. Artificial neural networks with terminal attractor dynamics are used. The rapid network convergence resulting from the infinite local stability of these attractors allows the development of fast neural learning algorithms. Approaches to manipulator inverse kinematics are reviewed, the neurodynamics model is discussed, and the neural learning algorithm is presented. >

Real-time adaptive controllers for a turbine generator

The design and practical application of series-parallel model following adaptive control techniques to a turbogenerator system is described, incorporating proportional-plus-integral and relay-plus-integral adaptation laws. An advantage of this scheme is that it does not require on-line identification, nor the solution of linear or non-linear equations in real time. The aim is to generate an adaptive command signal, such that the output of the turbogenerator will match the output of the model, even when the system is subjected to output disturbances or changes in operating conditions. The turbogenerator system consists of a synchronous generator connected to an infinite busbar through a transformer and two transmission lines in parallel. The control scheme has been thoroughly tested on a micromachine system, over a wide range of operating conditions. Typical simulation and experimental results are presented, illustrating the response to a short-circuit on the transmission network, and including comparison with a fixed-gain optimal controller. These show that the adaptive controllers are most effective, providing rapid convergence of the turbogenerator and model outputs, and an improved performance.

Modelling, simulation and control in a data-rich environment

This paper describes the use and potential use of mathematical modelling, computer simulation, real-time parameter identification and adaptive feedback control techniques in data-rich clinical environments. We consider a system to be operating in a data-rich environment when measurements of system inouts and outputs are frequently available. Two particular application examples which illustrate the power of these techniques will be examined. The first example that will be described involves the development and testing of a real-time adaptive controller for simulataneous regulation of mean arterial pressure and cardiac output using two drugs. The second application of these techniques involves the real-time control of electrical stimulation for the functional use of paralyzed muscles in neuroprosthetic devices.

Discrete Algorithms for Residential Heating Strategies an Application of Optimal Control Theory

The recent significant expansion of electric space heating calls for proper load management strategies for a northern climate. In this paper, a multimode system including heat pump technology with storage is developed. The variations of the room temperature setpoint are studied in relation to heating power and the setback pattern. A discrete-time solution to the problem of determining the optimal amount of energy required from an electrical source in order to minimize consumption and temperature droop has been obtained. The present solution is suboptimal in the sense that it is a linear approximation of a non-linear plant. The recursive Kalman filter was employed for the linear regulator solution. Pontryagin's minimum principle was used to express the singular algorithm. The implementation problems of the above feedback controllers are discussed. Numerical results from computer simulations are compared with the bang-bang regulator performances. A real-time adaptive control application using hourly predictions of climatic conditions is foreseen.

On‐line adaptive control of a fed‐batch fermentation of Saccharomyces cerevisiae

The feasibility of applying an adaptive control technique to a fermentation process is investigated. The nonlinear, time-variant parameters of a fermentation process were estimated on-line as a series of linearized describing matrices. The matrices were used to update a suboptimal feedback law which controlled the process in real time over the linear region. Experiments were performed on a small-scale fully instrumented fermenter with the online, real-time adaptive control package. Results are presented for both single- and multivariable control, and indicate successful control of yeast cell growth.

An adaptive control algorithm for robotic deburring

This paper presents the topic of adaptive control for robotic deburring by presenting some nonreal-time and some real-time schemes found in literature. Thereafter, a real-time adaptive control algorithm for robotic deburring is presented. Experiments are presented in which an IBM RS/1 robot is used to simulate the operation of the algorithm.

Adaptive Control of Tubular Chemical Reactors

Real time adaptive control for a type of tubular chemical reactors is designed. The system dynamics is considered to be described by a first order partial differential equation system. A verified integral reactor model, taking off the time delay influance is derived. This model is suitable for a multi variable adaptive control system design. The model output is used to generate the control based on the discrete linear time - optimal control strategy (deadbeat servo problem), designed for a replacing second order model. The parameters of the model are estimated using recursive least squares estimation at each sampling period. As an example a one-dimensional control problem of a packed-bed catalytic tubular chemical reactor with lumped and boundary control actions is considered.

Adaptive Control of a Nonlinear System

This paper presents a brief analysis of an electromagnetic suspension system with linear and nonlinear magnet force-distance chacteristics. This analysis is then used to develop a real-time adaptive control algorithm for single-degree of freedom suspension system with nonlinear force characteristics. The algorithm is realised through a simulated microcomputer on a PRIME-550 digital computer. Effectiveness of this method of controlling the nonlinear system is demonstrated and some practical aspects of implementing this control scheme briefly discussed.

An Integrated Distributed Control Structure for Global Communications

The worldwide communications resources of the Defense Communications System (DCS) are controlled by a multilevel control complex, with distribution of control functions among many control facilities. The control structure is going through substantial changes, driven by considerations of reliability, survivability, crisis management capability, and of course, cost. This paper introduces the control structure, the ongoing and programmed improvements for the control capabilities, and how the Automated Technical Control (ATEC) equipments will form the basis of a distributed, processor based communications control utility. Finally, the unique features of the Real Time Adaptive Control System (RTACS) for the third generation Defense Satellite Communications System (DSCS) will be described, along with the integration of RTACS into the distributed global control structure.

Adaptive optimization of a time-sharing system's performance

The complex task of managing a virtual memory multiprogramming system is considered as one which can be achieved by allowing the operating system to make use of measurement data gathered on-line in the scheduling decisions it has to make. System performance optimization is achieved by continuous monitoring of critical system parameters and workload characteristics and by use of this information in a real-time adaptive feedback control policy. As a specific application of this approach, the maximization of system throughput by the regulation of the degree of multiprogramming in a virtual memory system is examined. The specific form of this performance measure as a function of the number of active processes sharing main memory is used in the design of an adaptive and statistical maximum-seeking algorithm designed to respond to abrupt changes in program locality. The data gathering and smoothing procedures and the optimization policy are then implemented in a simulator of a virtual memory time-sharing system and evaluated in simulation runs with a random and time-varying workload. These experiments are used to tune the various parameters of the algorithm and to demonstrate its ability to maintain the system at an optimal level of performance. Statistical confidence intervals for these simulation runs are given in order to provide a measure of significance to the experiments.

Adaptive Global Search by the Process of Competitive Evolution

A probabilistic automaton based on competing evolutionary search processes is considered as an on-line real-time adaptive controller for complex plants with multimodal performance index structures that are modified under measurable but uncontrollable random environmental variations. The scheme described includes mechanisms of storage and retrieval to take advantage of control experience gained prior to any current situation.

Real time-adaptive control of systems containing inaccessible state variables†

This paper describes an adaptive scheme for controlling a non-linear process which is attractive from the point of view of implementation in a real time mode. Effective process control can be maintained, even when certain of the state variables are inaccessible, without the need for excessive computation. The process is repeatedly identified as a series of quasi-linear systems. The nature of the control is defined by a model, static or dynamic, set a priori. A ‘sub-optimal’ controller is modified by each successive identification.

Adaptive Global Search in a Time-Variant Environment Using a Probabilistic Automaton with Pattern Recognition Supervision

A probabilistic automaton with pattern recognition supervision is considered as an on-line real-time adaptive controller for a complex plant with a multimodal performance-index structure and subjected to an environment which randomly fluctuates in time. This environment is considered to be partially measurable but entirely uncontrollable. The automaton discussed is capable not only of learning the optimum control parameters in any given environmental situation but also of acting as an internal teacher in the formation of pattern associations between the measurable state of the environment and the control situation, so that approximately recurrent conditions can be taken advantage of in future relearning situations. These pattern associations, once developed, are used to supervise the future action of the automaton. Furthermore, the pattern associations between the measurable state of the environment and the control situation must themselves be adaptively formed to allow for variations caused by unknown and/or unmeasurable factors in the total environment.