Multi-input Multi-output Adaptive Control Research Articles

Robust H∞ Adaptive Fuzzy Tracking Control for MIMO Nonlinear Stochastic Poisson Jump Diffusion Systems.

Recently, stochastic Poisson jump diffusion system has attracted much attention in stochastic control. Poisson jump process has been used to model the random discontinuous jump behavior of the intrinsic discontinuous perturbation in stochastic system. Wiener process also called diffusion process represents the continuous random fluctuation to the system. In this paper, a robust adaptive control is introduced for multi-input multi-output (MIMO) nonlinear stochastic Poisson jump diffusion system with continuous and discontinuous random fluctuations to achieve the H∞ tracking performance with a prescribed disturbance attenuation level. The system structure is of a strict-feedback form. Based on the backstepping design technique and H∞ control theory, a robust adaptive control law is constructed for MIMO nonlinear stochastic Poisson jump diffusion system to achieve the H∞ tracking performance with a prescribed attenuation level of external disturbance, despite of fuzzy approximation error and the effect of continuous and discontinuous random fluctuations. The proposed H∞ adaptive control law combines both merits of H∞ tracking control and adaptive control scheme to sufficiently solve the robust H∞ adaptive tracking control problem for MIMO stochastic nonlinear systems with continuous and discontinuous random fluctuations. In addition, the uniformly positive definite assumption of control coefficient matrix is relaxed for the proposed MIMO adaptive control as well. A stochastic quadrotor trajectory tracking control simulation is provided to show the effectiveness of the proposed H∞ robust adaptive control law.

Computer-Aided System for Analysis and Design of Multivariable Adaptive Control Using an Object-Oriented Language and Its Application.

When we design control systems, a computer-aided design (CAD) system is very useful for adjusting design parameters and repeated simulations. Especially in the case of multi-input multi-output (MIMO) systems, the tool is indispensable because it is otherwise complicated to analyze controlled objects and design controllers. In this study, we developed a CAD system which can create real objects and instruments in a computer virtual area by using an object-oriented language. In particular, our proposed system has schemes for both system analysis and parameter estimation for the design of MIMO adaptive control systems. In addition, a friendly man-machine interface is realized. To discuss the usefulness of this CAD system, we apply it to the control system of a robot manipulator. This CAD system is shown to be very useful for researching and developing adaptive control systems.

Design of G-interactor and its application to direct multivariable adaptive control

In the design of multi-input multi-output (MIMO) adaptive control systems an interactor plays an important role. The derivation of the interactor requires system parameter values unless the interactor is in diagonal form. This is a considerable restriction when the interactor is applied to the adaptive control system design. In MIMO adaptive control many parameters must be estimated to achieve good tracking. Over-parametrization causes many problems, e.g. bad tracking performance and long calculation time of control input. A design of interactor, called a G-interactor, for unknown MIMO plants is given. It is applied to MIMO adaptive control systems. The G-interactor requires minimum information on the plant, such as the relative degree of each element of the plant transfer matrix. For this design a canonical form, called the G-canonical form, of a matrix A is investigated. The canonical form is also used to eliminate redundant parameters of MIMO adaptive control systems. Numerical examples show the effect...

On the role of the interactor matrix in multiinput-multioutput adaptive control

Recent papers have shown that the system interactor matrix plays an important role in the multiinput-multioutput adaptive control. In these papers, however, the impression may have been inadvertently conveyed that the interactor matrix can be taken to be diagonal in almost all cases. This note shows that this impression is false and that, in general, a nondiagonal interactor matrix should be considered.