In this study, a delay‐dependent memory state‐feedback controller for linear systems with input time‐delay in the presence of external disturbance has been investigated. At first, a new formula is introduced to obtain the prediction vector from the system dynamics and then by using this formula, the effect of input time‐delay on the original delayed system is reduced. To guarantee the prescribed disturbance attenuation level of the closed‐loop system, Lyapunov theory and linear matrix inequality (LMI) approaches are used. In the case of feasibility, sufficient LMI conditions provide the stabilising gain of the predictor‐based controller. To illustrate the effectiveness of the proposed method, it is applied to a quarter‐car model of an active suspension system considering the actuator time‐delay.

In this paper we aim to survey the performance of a non-linear H_∞ method and a new proposed controller on a magnetic levitation model (maglev). The proposed controller is a new model-free adaptive design approach using an adaptive-fuzzy procedure based on feedback linearization. The main idea of the new controller comprises two steps: first, by means of the feedback linearization method, a measured signal is taken to a specific level with an error less than a defined value and second, proposed rules are applied to the system to keep the error near zero. The major advantage of new controller is that there is no need for identification of the system dynamics and only the output error is required.

In this paper, constrained memory state-feedback H_∝ control for a half-car model of an active vehicle suspension system with input time-delay in the presence of external disturbance has been investigated. Its prime goal is to improve the inherent trade-offs among power consumption, handling performance, ride quality, and suspension travel. The tire deflections and the suspension deflections are constrained by their peak response values in time domain using the generalized H₂ ( GH_∝) norm (energy-to-peak) performance, while the ride comfort performance of the suspension system is optimized by notion of the H_∝ control (energy-to-energy) to measure the body accelerations including both the heaving and the pitching motions. Similar to the well-known prediction-based methods, the prediction vector of the system is achieved to construct the memory state-feedback controller. Using the prediction vector, sufficient conditions guaranteeing closed-loop system stability as well as disturbance attenuation are obtained as some delay-dependent linear matrix inequalities (LMIs). In addition, some LMIs are added to limit the gain of the controller. In the case of feasibility, obtained LMIs provide the stabilizing gain of the memory controller. The proposed approach is applied to a half-car model of an active suspension system considering the actuator time-delay to illustrate the effectiveness of the proposed method.

The performance of robot manipulators with nonadaptive controllers might degrade significantly due to the open loop unstable system and the effect of some uncertainties on the robot model or environment. A novel Neural Network PID controller (NNP) is proposed in order to improve the system performance and its robustness. The Neural Network (NN) technique is applied to compensate for the effect of the uncertainties of the robot model. With the NN compensator introduced, the system errors and the NN weights with large dispersion are guaranteed to be bounded in the Lyapunov sense. The weights of the NN compensator are adaptively tuned. The simulation results show the effectiveness of the model validation approach and its efficiency to guarantee a stable and accurate trajectory tracking process in the presence of uncertainties.