Multivariate Cross-Coupling Adaptive Control Schemes to Reduce Contour Error in Multi-Axis Positioning Systems: Part I — Design and Stability

Multivariable adaptive control based consensus flight control system for UAVs formation

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

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.

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 Adaptive digital Controller to optimize DC-DC converter switching frequency and dead-time at the same time, for maximum efficiency under variable conditions, is presented in this paper. The Multivariable Adaptive digital Controller (MVAC) adaptively adjusts the DC-DC converter switching frequency and dead-time while tracking the converter minimum input power (maximum efficiency) point under variable conditions including variable load, variable input voltage and variable temperatures. In this paper, the MVAC method is discussed and analyzed. MVAC digital control algorithm and experimental implementation are presented.

1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Introduction. Preliminaries. Input/Output Stability. Lyapunov Stability. Positive Real Functions and Stability. Stability of LTI Feedback System. Problems. 4. On-Line Parameter Estimation. Introduction. Simple Examples. Adaptive Laws with Normalization. Adaptive Laws with Projection. Bilinear Parametric Model. Hybrid Adaptive Laws. Summary of Adaptive Laws. Parameter Convergence Proofs. Problems. 5. Parameter Identifiers and Adaptive Observers. Introduction. Parameter Identifiers. Adaptive Observers. Adaptive Observer with Auxiliary Input. Adaptive Observers for Nonminimal Plant Models. Parameter Convergence Proofs. Problems. 6. Model Reference Adaptive Control. Introduction. Simple Direct MRAC Schemes. MRC for SISO Plants. Direct MRAC with Unnormalized Adaptive Laws. Direct MRAC with Normalized Adaptive Laws. Indirect MRAC. Relaxation of Assumptions in MRAC. Stability Proofs in MRAC Schemes. Problems. 7. Adaptive Pole Placement Control. Introduction. Simple APPC Schemes. PPC: Known Plant Parameters. Indirect APPC Schemes. Hybrid APPC Schemes. Stabilizability Issues and Modified APPC. Stability Proofs. Problems. 8. Robust Adaptive Laws. Introduction. Plant Uncertainties and Robust Control. Instability Phenomena in Adaptive Systems. Modifications for Robustness: Simple Examples. Robust Adaptive Laws. Summary of Robust Adaptive Laws. Problems. 9. Robust Adaptive Control Schemes. Introduction. Robust Identifiers and Adaptive Observers. Robust MRAC. Performance Improvement of MRAC. Robust APPC Schemes. Adaptive Control of LTV Plants. Adaptive Control for Multivariable Plants. Stability Proofs of Robust MRAC Schemes. Stability Proofs of Robust APPC Schemes. Problems. Appendices. Swapping Lemmas. Optimization Techniques. Bibliography. Index. License Agreement and Limited Warranty.

Robust Adaptive Control

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

Singularity-Free Adaptive Pole Placement for 2nd Order Systems

This paper presents the application of a multivariable adaptive control strategy to a typical automotive air conditioning system. An experimentally validated physical model for the air conditioning (a/c) cycle is first presented and is subsequently used to choose a relevant model structure for indirect adaptive control. Recursive identification of this model structure is carried out using a multi‐input multi‐output (MIMO) parameter estimation algorithm to obtain an equivalent discrete‐time state space model of the a/c system. Linear quadratic regulator (LQR) design is implemented on the estimated model with the objectives of reference tracking and disturbance rejection. Simulation studies are presented to evaluate the advantages of using the electronic expansion valve and the air flow rate over the evaporator to control the efficiency and the capacity of a general automotive a/c unit using this adaptive control approach. The results demonstrate the efficacy of the MIMO controller and motivate further research in this area. Copyright © 2004 John Wiley & Sons, Ltd.

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.

Data-driven model based adaptive feedback-feed forward control schemes for open cycle - OTEC process

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.

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.

SummaryA novel multivariable composite model reference adaptive control scheme is developed for multivariable fractional‐order systems with arbitrary relative degree. Firstly, by introducing right gain matrix to substitute left gain matrix, the stringent symmetry assumption is no longer required. The design procedures of controller with certain and uncertain high‐frequency gain matrix are then provided, respectively. The (robust) stability of the resulting closed‐loop control system is investigated by indirect Lyapunov method. It is shown that the composite model reference adaptive control can achieve better performance on output tracking than that of model reference adaptive control. Finally, the effectiveness and applicability of the proposed control scheme are demonstrated in 3 numerical examples.