Multiple Model Adaptive Control Scheme Research Articles

Blending based multiple-model adaptive control of multivariable systems with application to lateral vehicle motion control

This paper develops multiple fixed model blending based adaptive parameter identification schemes for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification schemes are proven to be asymptotically stable for uncertain linear time-invariant (LTI) MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification schemes, a linear-quadratic (LQ) optimal multiple-model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is proven to be asymptotically stable for LTI MIMO systems and applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability, effectiveness, and comparison of the proposed MMAC scheme.

Weighted multiple model adaptive boundary control for a flexible manipulator.

In this article, a weighted multiple model adaptive boundary control scheme is proposed for a flexible manipulator with unknown large parameter uncertainties. First, the uncertainties are approximatively covered by a finite number of constant models. Second, based on Euler-Bernoulli beam theory and Hamilton principle, the distributed parameter model of the flexible manipulator is constructed in terms of partial differential equation for each local constant model. Correspondingly, local boundary controllers are designed to control the manipulator movement and suppress its vibration for each partial differential equation model, which are based on Lyapunov stability theory. Then, a novel weighted multiple model adaptive control strategy is developed based on an improved weighting algorithm. The stability of the overall closed-loop system is ensured by virtual equivalent system theory. Finally, numerical simulations are provided to illustrate the feasibility and effectiveness of the proposed control strategy.

Model reference adaptive control for switched linear systems using switched multiple models control strategy

In this paper, the multiple model strategy is applied to the adaptive control of switched linear systems to improve the transient performance. The solvability of the adaptive stabilization problem of each subsystem is not required. Firstly, the two-layer switching mechanism is designed. The state-dependent switching law with dwell time constraint is designed in the outer-layer switching to guarantee the stability of the switched systems. During the interval of dwell time constraint, the parameter resetting adaptive laws are designed in the inner-layer switching to improve the transient performance. Secondly, the minimum dwell time constraint providing enough time for multiple model adaptive control strategy to work fully and maintaining the stability of the switched systems is found. Finally, the proposed switched multiple model adaptive control strategy guarantees that all the closed-loop system signals remain bounded and the state tracking error converges to zero.

Multiple model adaptive control for switched linear systems: A two‐layer switching strategy

SummaryIn this paper, the multiple model adaptive control scheme is first introduced into a class of switched systems. A switched multiple model adaptive control scheme is proposed to improve the transient behavior by resetting the controller parameters. Firstly, a finite‐time parameter identification model is presented, which greatly reduces the number of identification models. Secondly, a two‐layer switching strategy is constructed. The outer layer switching mechanism is to ensure the stability of the switched systems. The inner layer switching mechanism is to improve the transient behavior. Then, by using the constructed jumping multiple Lyapunov functions, the proposed adaptive control scheme guarantees that all the closed‐loop system signals remain bounded and the state tracking error converges to a small ball whose radius can be made arbitrarily small by appropriately choosing the design parameter. Finally, a practical example about model reference adaptive control of an electrohydraulic system using multiple models is given to demonstrate the validity of the main results.

A multiple-model MRAC scheme for multivariable systems with matching uncertainties

This paper develops a multivariable multiple-model adaptive control scheme for adaptive state feedback state tracking control of systems whose plant-model matching conditions are uncertain and parameters are unknown. To deal with the uncertainty of plant-model matching conditions needed for adaptive control, multiple reference model systems are employed to generate multiple parameter estimation and feedback control signals from which a most suitable control input is selected by a control switching mechanism designed using multiple estimation errors. Such a new multiple-model control design is based on an expanded control system parametrization which has the capacity to cover system structural uncertainties. Stability analysis and simulation results ensure and verify the desired adaptive control system stability and tracking performance.

Multiple-Model Adaptive Control for Spacecraft Under Sign Errors in Actuator Response

This paper addresses the attitude tracking control problem for spacecraft with unknown inertia parameters and sign errors in actuator response that are uncertain in occurring time instants and patterns. Either a software or hardware error may cause actuation sign errors, which may lead spacecraft to fail. A new multiple-model adaptive control scheme is developed to compensate the uncertainties of actuation sign errors and inertia parameters, which ensures the desired system global stability and asymptotic attitude tracking properties. Such a control scheme employs a set of adaptive estimators to generate parameter estimates, estimator states, and estimation errors, in which the uncertainties of inertia parameters are parameterized. Then, a set of control signals are designed by using such estimates and estimation errors, and a switching mechanism is established based on calculating multiple cost functions from the estimation errors to select the most appropriate control signal with the minimum cost function as the applied control signal. Simulation results of an application to a spacecraft model with four reaction wheels and comparison of results with a Nussbaum gain-based control design verify the effectiveness of the proposed control scheme.

On Multiple Model Adaptive Control Strategy Research of Ball and Plate System

The ball and plate system is a multi-variable and strongly nonlinear-controlled object of which is a system of two-dimensional extension with the variables amid having strong coupling function, and simultaneously it demands the closed loop control system owning good transient process and transient response. From perspective of international and domestic scale, ball and plate system has not figured out effective-control scheme. Take into account the ball and plate system possesses trait of nonlinearity and high requirements for transient response, etc. This paper proposes multiple model adaptive control strategy which is very suitable for such system, as well as the direction and guidance of such research.

Weighted Multiple Model Adaptive PID Control with Crossover Frequency Shift Tuning for a Hydraulic Turbine Plant

Hydraulic turbines are known to have complex and highly nonlinear dynamics with some varying parameters, especially in the presence of large or abrupt disturbances. In this study, the weighted multiple model adaptive control scheme was used as a controller for a hydraulic turbine model, with the aim of providing desirable characteristics such as robustness and stability. The system of comprised four candidate plant models with four corresponding candidate PID controllers which were tuned according to the phase shifting method. The plant operation, subjected to both abruptly changing and smoothly changing load conditions, was simulated. From the results, it was found that the proposed controller showed very good performance for both types of disturbance changes.

A Combined Multiple Model Adaptive Control Scheme and Its Application to Nonlinear Systems With Nonlinear Parameterization

A combined multiple model adaptive control (CMMAC) scheme, which is a proper combination of the estimator-based MMAC scheme and the unfalsified MMAC scheme, has been proposed with the aim of taking advantage of the strength of each scheme while avoiding their weaknesses. The major novelty of the CMMAC scheme lies in the fact that it monitors not only the adequacy of candidate models in terms of their estimation performances but also the performance of the active candidate controller. As an application of the CMMAC scheme and one example of such new multiple model adaptive controllers, a CMMAC based controller has been designed for a class of nonlinear systems with nonlinear parameterization. Under some sufficient conditions, a strong finite time switching result (which provides a characterization on the maximum number of switching) and the closed-loop stability have been established. A constructive design based on back-stepping is provided for the adaptive control problem of a special class of nonlinearly parameterized systems, which can satisfy all the sufficient conditions to ensure closed-loop stability.

Enhancements to non-linear multiple model adaptive control schemes

There has been much interest in recent years on neural network based control of non-linear dynamic processes and also on their use in multiple model adaptive control schemes. This paper reports on performance enhancements to one class of such control laws, including their extension to multiple model adaptive control.

A practical multiple model adaptive strategy for single-loop MPC

This paper details a multiple model adaptive control strategy for model predictive control (MPC). To maintain performance of this linear controller over a wide range of operating levels, a multiple model adaptive control strategy for dynamic matrix control (DMC), the process industry's standard for MPC, is presented. The method of approach is to design multiple linear DMC controllers. The tuning parameters for the linear controllers are obtained using novel analytical expressions. The controller output of the adaptive DMC controller is a weighted average of the multiple linear DMC controllers. The capabilities of the multiple model adaptive strategy for DMC are investigated through computer simulations and an experimental system.

A practical multiple model adaptive strategy for multivariable model predictive control

Model predictive control (MPC) has become the leading form of advanced multivariable control in the chemical process industry. The objective of this work is to introduce a multiple model adaptive control strategy for multivariable dynamic matrix control (DMC). The novelty of the strategy lies in several subtle but significant details. One contribution is that the method combines the output of multiple linear DMC controllers, each with their own step response model describing process dynamics at a specific level of operation. The final output forwarded to the controller is an interpolation of the individual controller outputs weighted based on the current value of the measured process variable. Another contribution is that the approach does not introduce additional computational complexity, but rather, relies on traditional DMC design methods. This makes it readily available to the industrial practitioner.

Performance enhancements for a class of multiple model adaptive control schemes

Multiple model adaptive control schemes offer the potential of improved performance over conventional single model schemes in a number of cases of practical interest. This paper describes modifications to enhance the performance of one class of such schemes when applied to linear and (potentially) non-linear processes.

Computer Aided Design and Evaluation of Multiple Model Adaptive Control Schemes

Multiple model adaptive control schemes offer the potential of improved performance over conventional single model schemes in a number of cases of practical interest. This paper describes the development and basic testing of a computer aided environment for the design and evaluation of such schemes. A key feature is the justified use of parallel processing to simulate the resulting closed loop dynamics.