In this article, an adaptive control approach–Adaptive Generic Model Control (AGMC) for a class of nonlinear time-varying processes with input time delay is proposed. First, a nonlinear state predictor (NSP) is introduced, which extends the conventional generic model control (GMC) to a class of nonlinear processes with input time delay. Then a class of nonlinear time-varying processes with input time delay is further considered. A modified strong tracking filter (MSTF) is adopted to estimate the timevarying parameters of the nonlinear processes, and the state estimates are then utilized to update the plant models used in the NSP and MSTF, this results in an adaptive generic model control scheme for a class of nonlinear time-varying processes with input time delay. A modified mathematical model of a three-tank-system is used for computer simulations, the results show that the proposed AGMC algorithm is satisfactory, and it has definite robustness against model/plant mismatch in the measurement noise.

In this article, an adaptive control method-adaptive generalized generic model control (AGGMC) is proposed for a class of nonlinear time-varying processes by use of a modified strong tracking filter (MSTF). It inherits all of the advantages of generic model control (GMC) and extends GMC to nonlinear time-varying processes with relative orders larger than ones. Sufficient conditions under which the MSTF is asymptotically convergent are first derived, then, with these conditions, the resultant closedloop system under the AGGMC is proved to be Lyapunov stable. Finally, simulation studies are provided to validate the effectiveness of the proposed approach.

Combining the repetitive learning strategy and the optimality principle, this Letter proposes a time-delayed method to control chaotic maps. This method can effectively stabilize unstable periodic orbits within chaotic attractors in the sense of least mean square. Numerical simulations of some chaotic maps verify the effectiveness of this method.

Nonlinear state prediction is of crucial importance to design controllers for nonlinear processes with input time delay. In this paper, the extended nonlinear state predictor (ENSP) we proposed is first outlined, which is used to predict the future states of a class of nonlinear processes with input time delay. A new concept of strong tracking predictor (STP) is then proposed, and an rthogonality principle is given as a criterion to design the STP. On the basis of the orthogonality principle, the ENSP is modified, which results in a STP. After the detailed STP algorithm is presented, we prove that the STP is locally asymptotically convergent for a class of nonlinear deterministic processes if some sufficient conditions are satisfied. In the presence of measurement noise, it is further proved that the proposed STP is exponentially bounded under certain conditions. Finally, computer simulations with a MIMO nonlinear model are presented, which illustrate that the proposed STP can predict accurately the future states of a class of nonlinear time delay processes no matter whether the states change suddenly or slowly, in addition, it has definite robustness against model/plant mismatches.

This paper presents a solution to the joint time-varying time delay and parameter estimation of NARX (nonlinear autoregressive with exogenous inputs) processes, where only pure time delay in input signal is considered. A modified strong tracking filter (MSTF) is proposed, and is adopted as an adaptive estimation algorithm. Three kinds of specific NARX processes are considered. The first is also the simplest, the output signal is the input with time delay plus disturbance; The second one is a simple NARX process plus disturbance; The third NARX process even has unknown time-varying parameters. For each of the NARX processes, we set up a specific estimation model, with these models the proposed MSTF algorithm can be applied to the real-time time delay and parameter estimation of the above three NARX processes. Computer simulation results demonstrate the effectiveness of the proposed approach. Moreover the robustness of the proposed algorithm against some model:process parameter mismatches is also tested via computer simulations.