Multiple Model Adaptive Control Research Articles

Multiple-model adaptive control for interval plants

Multiple-Model Adaptive Control (MMAC) is usually considered suitable for Interval Plants. This paper proposes a multiple model adaptive controller for first order interval plant with large delays. The number of models to be used is investigated and the controller parameters tuning is studied. The proposed scheme is applied to a numerical example for illustration. Simulation results show the applicability of the method.

Improved switching for multiple model adaptive controller in noisy environment

A new switching mechanism for multiple model adaptive controllers (MMAC) is suggested in this paper. The proposed method gives superior performance in comparison to the widely used method of switching based on performance function and hysteresis function in systems which experience high levels of measurement noise. This method acts as a complementary condition within the switching mechanism which checks the existence of excitation in system at every instant. The new method is evaluated by simulation studies on a nonlinear model of a pH neutralization plant.

Adaptive Control for a Class of Nonlinear System with Redistributed Models

Multiple model adaptive control has been investigated extensively during the last ten years in which the “switching” or “switching and tuning” have emerged as the mainly approaches. It is the “switching” that can improve the transient performance to some extent and also make it difficult to analyze the stability of the system with multiple models adaptive controller. Towards this goal, this paper develops a novel multiple models adaptive controller for a class of nonlinear system in parameter-strict-feedback form which not only improves the transient performance significantly, but also guarantees the stability of all the states of the closed-loop system. A simulation example is proposed to illustrate the effectiveness of the developed multiple models adaptive controller.

Multiple Model Adaptive Dynamic Positioning

This paper describes a procedure to design multiple model adaptive controllers for Dynamic Positioning (DP) of ships and offshore rigs subjected to the influence of sea waves, currents, and wind loads. To this effect, a linear design model is obtained, based on practical assumptions, describing the dynamics of the vessel. Four Linear Quadratic Gaussian (LQG) DP controllers (with the same structure) are designed, covering the different sea conditions from calm to extreme seas. Tools from multiple model adaptive control (MMAC) theory are used to exploit the information provided by the different observers (models) in order to select the appropriate controller. Numerical simulations carried out using a high fidelity nonlinear DP simulator illustrate the efficiency of the MMAC techniques proposed. To bridge the gap between theory and practice, the results of the simulations were experimentally verified by model testing with a DP operated ship, the Cybership III, under different simulated sea conditions in a towing tank equipped with a hydraulic wave maker.

Stable Weighted Multiple Model Adaptive Control With Improved Convergence Rate

The paper is concerned with the convergence rate of the weighting algorithm and the stability of weighted multiple model adaptive control (WMMAC). First a new weighting algorithms with convergence assurance is proposed. Second, the stability of the WMMAC system is proved based on virtual equivalent system (VES) concept and methodology, which is independent of specific 'local' control strategy. The obtained results indicate that the stability of a WMMAC system depends mainly on the 'stabilizing' property of each local controller and the convergence of weighting algorithm, i.e., the weight corresponding to the true (or the closest) model of the plant converges to 1, and others converge to 0.

Multiple model adaptive reconfiguration control of state delayed systems

In this paper, a new direct model reference adaptive control framework based on a multiple model approach is proposed. The main objective is to synthesize a fault-tolerant controller which reconfigures the faulty system with state delay. This approach provides stable adaptation for a class of linear systems with state delay in the presence of unknown actuator faults. The proof of stability is based on the Lyapunov−Krasovskii function, while appropriate switching of the multiple models ensures asymptotic tracking for the system outputs, and under the proposed algorithms, the tracking errors of the reconfigured system trend to zero rapidly. Simulation results are presented to illustrate the efficiency of the proposed method.

Fuzzy Multiple Reference Models Adaptive Control of Induction Motors

A new method of fuzzy multiple reference models adaptive control(FMRMAC) for dealing with significant and unpredictable system parameter variations is presented. In this method, different suitable reference model is chosen by fuzzy rules when changes occurred to the model parameters. A successful application to the speed servo system of dynamic model of induction motor (IM) shows this method works well with high dynamic performance under the condition of command speed change and load torque disturbance.

Stability Analysis of Robust Multiple Model Adaptive Control

AbstractThe Robust Multiple Model Adaptive Control (RMMAC) methodology was first introduced in Fekri et al. [2006] for open-loop stable plants with parametric uncertainty and unmodeled dynamics subjected to external disturbances and measurement noise. This paper addresses the stability of RMMAC systems. We show, using concepts and analysis tools that borrow from Supervisory Control, that all closed-loop signals in a RMMAC system are bounded. It is further shown that robust performance is recovered in steady state.

Safety issues in adaptive control systems for automatic administration of vasoactive drugs

AbstractAutomatic administration of drugs to control cardiovascular function during and after surgery has received considerable attention. Although the potential benefits associated with such a technology remain unquestioned, the several adaptive control strategies proposed thus far have had very limited success in practice. In developing robust adaptive control methodologies for drug dosing in cardiovascular applications, we have analysed a well-known multiple-model adaptive control strategy for blood pressure control. The results reveal that no guarantee of protection against actually inserting a destabilising controller into the closed-loop is given and one cannot even put a global upper bound on the time during which the destabilising controller is attached. We advocate caution towards issues which in the past may have been either disregarded or not subjected to a systematic analysis as instability could be fatal in the context of a clinical application.

Robust multiple model adaptive control: Modified using ν‐gap metric

AbstractA new robust adaptive control method is proposed, which removes the deficiencies of the classic robust multiple model adaptive control (RMMAC) using benefits of the ν‐gap metric. First, the classic RMMAC design procedure cannot be used for systematic design for unstable plants because it uses the Baram Proximity Measure, which cannot be calculated for open‐loop unstable plants. Next, the %FNARC method which is used as a systematic approach for subdividing the uncertainty set makes the RMMAC structure being always companion with the µ‐synthesis design method. Then in case of two or more uncertain parameters, the model set definition in the classic RMMAC is based on cumbersome ad hoc methods. Several methods based on ν‐gap metric for working out the mentioned problems are presented in this paper. To demonstrate the benefits of the proposed RMMAC method, two benchmark problems subject to unmodeled dynamics, stochastic disturbance input and sensor noise are considered as case studies. The first case‐study is a non‐minimum‐phase (NMP) system, which has an uncertain NMP zero; the second case‐study is a mass‐spring‐dashpot system that has three uncertain real parameters. In the first case‐study, five robust controller design methods (H2, H∞, QFT, H∞ loop‐shaping and µ‐synthesis) are implemented and it is shown via extensive simulations that RMMAC/ν/QFT method improves disturbance‐rejection, when compared with the classic RMMAC. In the second case‐study, two robust controller design methods (QFT and mixed µ‐synthesis) are applied and it is shown that the RMMAC/ν/QFT method improves disturbance‐rejection, when compared with RMMAC/ν/mixed−µ. Copyright © 2010 John Wiley & Sons, Ltd.

Cooperative Multiple-Model Adaptive Guidance for an Aircraft Defending Missile

A cooperative guidance law, for a defender missile protecting an aerial target from an incoming homing missile, is presented. The filter used is a nonlinear adaptation of a multiple model adaptive estimator, in which each model represents a possible guidance law and guidance parameters of the incoming homing missile. Fusion of measurements from both the defender missile and protected aircraft is performed. A matched defender’s missile guidance law is optimized to the identified homing missile guidance law. It utilizes cooperation between the aerial target and the defender missile. The cooperation stems from the fact that the defender knows the future evasive maneuvers to be performed by the protected target and thus can anticipate the maneuvers it will induce on the incoming homing missile. Moreover, the target performs a maneuver that minimizes the control effort requirements from the defender. The estimator and guidance law are combined in a multiple model adaptive control configuration. Simulation results show that combining the estimations with the proposed optimal guidance law, that utilizes cooperation between the defending missile and protected target, yields hit-to-kill closed loop performance with very low control effort. This facilitates the use of relatively small defending missiles to protect aircrafts from homing missiles.

Application of Weighted Multiple Models Adaptive Controller in the Plate Cooling Process

In the controlled cooling process,medium plates usually contain some large scale of uncertainty in the thickness.So,if only one fixed model is utilized constantly,it may not work correctly when the thickness of the plate differs largely from the real one.In this paper,weighted multiple models adaptive controller (WMMAC) is proposed.Multiple models are established and their corresponding controllers are designed according to the characteristics of the thickness of plates under certain circumstance.At each moment,the output error of each model is calculated and used to achieve their weighting coefficients.Finally,a global controller is designed by multiplying the controllers output with the corresponding weighting coefficients.A simulation result shows that the proposed WMMAC is superior to the conventional controller in the medium plate controlled cooling process and has wide application prospect.

Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process

Multi-loop (De-centralized) Proportional-IntegralDerivative (PID) controllers have been used extensively in process industries due to their simple structure for control of multivariable processes. The objective of this work is to design multiple-model adaptive multi-loop PID strategy (Multiple Model Adaptive-PID) and neural network based multi-loop PID strategy (Neural Net Adaptive-PID) for the control of multivariable system. The first method combines the output of multiple linear PID controllers, each describing process dynamics at a specific level of operation. The global output is an interpolation of the individual multi-loop PID controller outputs weighted based on the current value of the measured process variable. In the second method, neural network is used to calculate the PID controller parameters based on the scheduling variable that corresponds to major shift in the process dynamics. The proposed control schemes are simple in structure with less computational complexity. The effectiveness of the proposed control schemes have been demonstrated on the CSTR process, which exhibits dynamic non-linearity. Keywords—Multiple-model Adaptive PID controller, Multivariable process, CSTR process.

Multiple Model Adaptive Control With Mixing

Despite the remarkable theoretical accomplishments and successful applications of adaptive control, the field is not sufficiently mature to solve challenging control problems where strict performance and robustness guarantees are required. Critical to the design of practical control systems for these challenging applications, and currently lacking in parameter estimation-based adaptive control schemes, is an approach that explicitly accounts for robust-performance and stability specifications. Towards this goal, this paper describes a robust adaptive control approach called <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive mixing control</i> that makes available the full suite of powerful design tools from LTI theory, e.g., mixed-μ synthesis. The stability and robustness properties of adaptive mixing control are analyzed. It is shown that the mean-square regulation error is of the order of the modeling error provided the unmodeled dynamics satisfy a norm-bound condition. And when the parameter estimate converges to its true value, which is guaranteed if a persistence of excitation condition is satisfied, the adaptive closed-loop system converges exponentially fast to a closed-loop system comprising the plant and some LTI controller that satisfies the control objective. A benchmark example is presented, which is used to compare the adaptive mixing controller with other adaptive schemes.

Application of Weighted Multiple Models Adaptive Controller in the Plate Cooling Process

In the controlled cooling process, medium plates usually contain some large scale of uncertainty in the thickness. So, if only one fixed model is utilized constantly, it may not work correctly when the thickness of the plate differs largely from the real one. In this paper, weighted multiple models adaptive controller (WMMAC) is proposed. Multiple models are established and their corresponding controllers are designed according to the characteristics of the thickness of plates under certain circumstance. At each moment, the output error of each model is calculated and used to achieve their weighting coefficients. Finally, a global controller is designed by multiplying the controllers' output with the corresponding weighting coefficients. A simulation result shows that the proposed WMMAC is superior to the conventional controller in the medium plate controlled cooling process and has wide application prospect.

Target Maneuver Adaptive Guidance Law for a Bounded Acceleration Missile

A new estimator-guidance law is presented, for a bounded acceleration interceptor pursuing a target performing a sudden step maneuver. The linearized end-game scenario is investigated. The missile’s acceleration saturation is represented by the equivalent random input describing function. The estimator proposed for this problem features a mechanism constructed to e‐ciently identify a sudden target maneuver. Due to the bound on the missile acceleration, the certainty equivalence property is not valid, and so, the resulting controller (guidance law) depends on the measurement noise level, statistics of the target maneuver, and the saturation limit. The derivation of this guidance law proceeds in two stages. In the flrst simplifled stage, it is assumed that the instance that the target issues a step command in its acceleration is known. However, the size of the target maneuver jump must still be estimated. Simulation results show signiflcant improvement over the deterministic optimal guidance law when the jump times vary from 10 to 3 missile time constants prior to the intercept time. If the jump occurs earlier or later than this duration no improvement is gained. In the second stage, a suboptimal guidance law is derived using the multiple model adaptive control formulation for a target performing a single step maneuver near the intercept time. Note that this sub-optimal guidance law does not require a-priory knowledge of the time of the jump in the target acceleration command. Performance improvement of the same order of magnitude as was observed for the known jump time scheme (that also serves as a performance bound) is achieved, verifying the validity of this approach. Sensitivity analysis to various noise levels and expected target maneuvers was carried out emphasizing the robustness and advantages of this scheme.

A dynamic-reliable multiple model adaptive controller for active vehicle suspension underuncertainties

The inherent uncertainties of vehicle suspension systems challenge not only the capabilityof ride comfort and handling performance, but also the reliability requirement. In thisresearch, a dynamic-reliable multiple model adaptive (MMA) controller is developed toovercome the difficulty of suspension uncertainties while considering performance andreliability at the same time. The MMA system consists of a finite number of optimalsub-controllers and employs a continuous-time based Markov chain to guide the jumpingamong the sub-controllers. The failure mode considered is the bottoming and topping ofsuspension components. A limitation on the failure probability is imposed to penalizethe performance of the sub-controllers and a gradient-based genetic algorithmyields their optimal feedback gains. Finally, the dynamic reliability of the MMAcontroller is approximated by using the integration of state covariances and a judgingcondition is induced to assert that the MMA system is dynamic-reliable. In numericalsimulation, a long scheme with piecewise time-invariant parameters is employed toexamine the performance and reliability under the uncertainties of sprung mass,road condition and driving velocity. It is shown that the dynamic-reliable MMAcontroller is able to trade a small amount of model performance for extra reliability.

Validating Controllers for Internal Stability Utilizing Closed-Loop Data

We introduce novel tests utilizing a limited amount of experimental and possibly noisy data obtained with an existing known stabilizing controller connected to an unknown plant for verifying that the introduction of a proposed new controller will stabilize the plant. The tests depend on the assumption that the unknown plant is stabilized by a known controller and that some knowledge of the closed-loop system, such as noisy frequency response data, is available and on the basis of that knowledge, the use of a new controller appears attractive. The desirability of doing this arises in iterative identification and control algorithms, multiple-model adaptive control, and multi-controller adaptive switching. The proposed tests can be used for SISO and/or MIMO linear time-invariant systems.

The Effect of Tuning in Multiple-Model Adaptive Controllers: A Case Study

In this paper, two types of multiple-model adaptive controllers are practically evaluated on a laboratory-scale pH neutralization process. The first one is supervisory switching multiple-model adaptive controller (SMMAC) whose model bank is fixed and selected a priori, and another one is a controller based on multiple models, switching, and tuning strategy (MMST) which uses the possibility of model bank tuning. In addition to investigation of the effect of tuning, the advantage of a disturbance rejection supervisor is studied. Various experiments and exhaustive numerical analyses are provided to assess the abilities of the proposed algorithms.

Fault tolerant flight control system design using a multiple model adaptive controller

This article presents a fault tolerant flight control system using multiple model adaptive control (MMAC). To apply this method to the aircraft, model reference adaptive control is extended to a linear discrete-time multiple-input multiple-output system. Multiple models can be determined by various flight conditions with respect to possible aircraft fault situations. In this article, the damages of control effectors are considered as aircraft faults. The aerodynamic coefficients of fault model are derived by a damaged control surface area. In the MMAC, how to select a proper fault model and how to switch into the selected model are significant issues. The conventional mode switching method, however, has a tendency to make the system fluctuate because of frequent mode switching, and therefore may deteriorate the performance of the aircraft. In this study, the modified adaptive mode switching scheme and new decision logic are proposed to improve the adaptiveness in the transient state dynamics of system. Numerical simulations are performed for a linear discrete-time aircraft model to validate the effectiveness of the proposed method.