Information Of Reference Signal Research Articles

Hybrid Optimal Tracking Control for Linear Continuous-Time Systems by Simultaneous Optimization Approach of System States and Mode Distributions

In this paper, we study optimal LQG tracking control problems for linear continuous-time hybrid systems. We adopt an approach to prepare some candidates of mode distributions and minimize value functions at each time for the systems and performance indices. By the approach, system states and mode distributions are simultaneously optimized so that the performance indices are minimized for available information of reference signals. We consider optimization problems for averaged systems and averaged performance indices throughout mode distributions. Finally we give numerical examples and verify that we can obtain both optimal tracking performance and optimal candidate of mode distributions by the algorithm presented in this paper.

On output regulation for linear systems

For both continuous- and discrete-time systems, we revisit the output regulation problem for linear systems. We generalize the problem formulation in order � to expand the class of reference or disturbance signals, � to utilize the derivative or feedforward information of reference signals whenever it is available. Our study of the so-called generalized regulation problem depends on the classical notions of exact and almost disturbance decoupling. These classical notions are reexamined while considering a very broad category of controllers beyond the classical state and measurement feedback. Such controllers include classical state feedback and measurement feedbacks but also extensions where part of the disturbance signal can be used for feedback. We consider the case where, for a part of the disturbance signal, either all its derivatives up to a certain jth order or derivatives of arbitrary order are available. This is combined with state and with measurement feedback.

LQG predictive optimal controller for continuous-time systems

The LQG optimal continuous-time control philosophy is extended so that use may be made of future setpoint or reference signal information. The tracking error is defined in terms of an ideal response model so that a model following capability may also be introduced. A dual criterion cost function is employed that enables the feedback and reference input controllers to be designed independently. The solution of the problem is obtained in the s-domain following a polynomial-based optimisation procedure. This type of controller should be useful for continuous-time adaptive systems which require a predictive capability.

LQG predictive optimal control for adaptive applications

The LQG optimal control philosophy is extended so that use may be made of future set-point or reference signal information. The tracking error is defined in terms of an ideal response model so that a model following capability may be introduced. The cost function may also be augmented by the addition of prediction error terms. The controller was derived for adaptive control applications and has many of the desirable features of the new “Predictive Control” philosophy. However, there are fewer variables to choose and guaranteed stability properties are obtained for all weightings or prediction intervals. The self-tuning controller briefly described is almost as simple as existing LQG industrial self-tuners.