Analysis and experimental results for a Multivariable Adaptive digital Controller

The paper presents a design method of multivariable adaptive digital tracking PID controllers using genetic algorithms (GA) and its application to the attitude control and momentum management (ACMM) system for a space station. The controller comprises two parts: (i) a fast online recursive identifier using the least squares method with variable forgetting factors to provide the updated dynamic parameters of the plant; and (ii) a genetic tuner based on artificial genetic algorithms to tune online the parameter matrices of the controller. The application of the controller using GA to the ACMM system for the space station yields remarkable results in stability robustness and setpoint tracking behaviour with respect to the large moments of inertia variations.

All adaptive controllers require a certain amount of prior information regarding the plant to be controlled to achieve the desired control objectives. In the context of multivariable adaptive control, not only is the prior information quite significant, but also the complexity of the adaptive controller is too high. In this paper, the authors introduce low order multivariable adaptive controllers which are easy to implement, and require less prior information than most other adaptive controllers. The performance of the resulting adaptive system is evaluated in the context of vibration suppression in large flexible structures.

Multivariable adaptive control based consensus flight control system for UAVs formation

The majority of motion control systems in use today employ fixed controllers, such as PID, which are tuned for nominal system conditions. Such systems, however, suffer from performance degradation as the system dynamics vary from the nominal conditions. Furthermore, cross-coupling among axes of motion also affect the positioning accuracy in contouring, particularly as the speed increases. This paper describes adaptive control strategies to alleviate this problem by adapting controller parameters on-line to compensate for changing system dynamics. Control strategies for both minimum and nonminimum phase systems are presented including cross-coupling control between axes. Part I of two papers presents the design of the multivariate adaptive control laws with stability proofs and part II describes the implementation results for an end milling process. The control schemes presented here can be utilized for a wide range of tracking control problems.

MODEL SIMPLIFICATION AND DIGITAL DESIGN OF MULTIVARIABLE SAMPLED-DATA CONTROL SYSTEMS VIA A DOMINANT-DATA MATCHING METHOD

Combining neural network identification technique and internal model control (IMC) strategy, a novel nonparametric design for nonlinear plants is presented. Based on this idea, a multivariable adaptive decoupling internal model controller (DIMC) is developed to deal with multivariable nonlinear coupling systems with unknown structure and parameters. A neural network is used to detect the unknown nonlinear internal model. One advantage is that the design does not require the computation of the inverse model of the IMC parameters, but only depends on system input-output data and neural network output.

Application of Multivariable Model Reference Adaptive Control to a Binary Distillation Column

Underwater glider platform represents the maturing technology with a large cost saving over current underwater sampling process. It can survey and monitor the sea environment cost-effective manner combining survey capabilities, simultaneous water sampling and environmental data gathering capacities. It can perform a wide range of fully automated monitoring data measurement over an extended period of time. This paper will focus on the design of multivariable underactuated nonlinear adaptive control using U-model methodologies. Underwater glider control, modelling and identification approach was reviewed in order to formulate the design, development and control approach of underwater glider development using multivariable adaptive U-model nonlinear control approach. U-model methodology simplifies the control synthesis with the influence of the uncertainties and external disturbances by selecting appropriate control structures. Most of the autonomous underwater vehicle (AUV) neglected the coupling effect of the dynamics during process modelling while U-model enables to include the coupling effect using the inverse Jacobian matrix. Thus contributes towards the underactuated nonlinear adaptive control development and process modelling.

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] There are many areas where optimal designs are applied to, for example, the development of a new drug, where a conventional dose- finding study involves learning about the dose-response curve in order to bring forward right doses of drug to late-stage development. The first part of this dissertation focus on three pharmacodynamics sigmoid Emax models, we derive the corresponding simple formats of the adaptive optimal designs regardless of the optimality criteria or parameters of interest. An algorithm for deriving a specific adaptive optimal design is developed. A simulation study comparing the adaptive optimal designs and the uniform designs is also performed. The second part of this dissertation focuses on the statistical process control, we proposed an adaptive approach for the multivariate CUSUM statistical process control chart for signaling a range of location shifts. This method is based on the multivariate CUSUM control chart proposed by Pignatiello and Runger in 1990. We used the exponentially moving weighted average (EMWA) statistic to estimate the current process mean shift and change the reference value adaptively in each run. By specifying the minimal magnitude of the mean shift through the non-centrality parameter, our proposed control chart can achieve an overall good performance for detecting a range of shifts rather than a single value.

This paper derives a stable multivariable model reference adaptive control (MRAC) scheme for systems with abrupt parameter variations (which may cause uncertain sign changes in the system’s high frequency gain matrix), motivated by the application to in-flight aircraft systems with damages. Such sign changes are illustrated by an aircraft model with asymmetric abrupt damages, and their uncertainty is handled by a Nussbaum gain based adaptive control design to control the aircraft for both healthy and post-damage situations, by adapting controller parameters autonomously after the damages occur, for which the knowledge of time instants, structures and values of the damages is not required. A piecewise continuous Lyapunov function is utilized to prove the desired system stability and tracking properties in the presence of damages.

A multivariable model reference adaptive control algorithm is presented for the case when the high-frequency gain matrix is unknown. Only an upper bound on the norm of the matrix needs to be known a priori. A transformation of the parameters, with a sort of hysteresis, is used to guarantee that a controller matrix, which is normally the inverse of the estimate of the high-frequency gain matrix, remains nonsingular. It is shown that all the signals in the adaptive system are bounded and that the tracking error and the regressor error converge to zero for all bounded reference inputs. Furthermore, exponential convergence is achieved when the regressor vector is persistently exciting.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Stability is improved for a multivariable model reference adaptive control algorithm when the high-frequency gain matrix is unknown. Only an upper bound on the norm of the matrix is required. A transformation of the parameters, with a sort of 'hysteresis', is used to guarantee that a controller matrix, which is nominally the inverse of the high-frequency gain matrix, remains nonsingular. It is shown that all the signals in the adaptive system are bounded and that the tracking error and the regressor error converge to zero for all bounded reference inputs. Furthermore, exponential convergence is achieved when the regressor vector is persistently exciting. >

The landing of a civil transport aircraft is one of the most critical phases due to parametric uncertainties and strong crosswind conditions. In this paper, separate controllers are designed for longitudinal and lateral-directional channels for the landing phase, which is divided into the final approach, flare, and decrab. A multivariable model reference adaptive control scheme is implemented with state feedback for output tracking. The safety and flight performance of the autolanding control system are demonstrated through Monte Carlo simulations of a nonlinear civil transport aircraft model.

Robust adaptive multivariate Hotelling's T2 control chart based on kernel density estimation for intrusion detection system

In view of the degradation of predictive control performance caused by model mismatch, a multi-variable adaptive predictive control system framework which is composed of zone model predictive control (MPC), identification module and performance monitoring module, is presented. The proposed framework synthesizes the traditional control mode and the test mode to construct a unified form, which is convenient to implement with the MPC software packages. Traditional setpoint control is switched to zone control to ensure that the process constraints remain satisfied in testing, while multi-variable test signals are introduced to guarantee the sufficient excitation of the plant. In addition, in order to maximize the signal-to-noise ratio, an adaptive method of determining the amplitude of test signals is proposed. All the online open-loop identification methods are suitable for this framework, as the testing is treated as “open-loop,” which solves the problem of the correlation between input signals and noises in the closed-loop identification. These characteristics of the proposed framework are illustrated via a simulation.