Proposed Smith Predictor Research Articles

Smith predictor–based sliding mode control with hyperbolic tangent function for unstable processes

In this work, a modified Smith predictor–based sliding mode control (SP-SMC) method is proposed for unstable plus time-delay processes. Sliding mode control has a discontinuous part in its control law which may cause chattering in the control signal if conventional switching strategy is used. A hyperbolic tangent function is employed as a switching element in the suggested SP-SMC technique to minimize the chattering effect. Optimal values of the controller’s unknown parameters are obtained using whale optimization algorithm which is a meta-heuristic technique. The process model used in the proposed Smith predictor control architecture is identified using measurements of limit cycle output which is obtained from online relay test. The proposed controller is found to be robust towards process parameter uncertainties and load disturbance. Suggested control technique results in fast set-point tracking response and significantly improved disturbance rejection performance compared to some other related methods.

A Novel Passivity-Based Resonant Instability Suppression Method for Grid-Connected VSC

The interaction between the three-phase voltage source converters (VSCs) and the power grid may cause high-frequency resonant instable problems. Studies have indicated that the non-passivity of VSCs is the main reason that leads to the resonances. The above-mentioned passive means that the equivalent output admittance of the VSC has a positive real part. In this paper, a novel damped Smith predictor is proposed to suppress the resonant instability problem that caused by time delay. The research proved that the proposed Smith predictor can significantly improve the passivity of the VSC by reducing the width of the negative real part at the high-frequencies. Simulation and experimental results demonstrate that the proposed Smith predictor can distinctly improve the robustness of the VSC.

Neural network observer-based networked control for a class of nonlinear systems

A new neural network observer-based networked control structure for a class of nonlinear systems is developed and analyzed. The structure is divided into three parts: local linearized subsystem, communication channels and remote predictive controller. A neural-network-based adaptive observer is presented to approximate the state of the time-delay-free nonlinear system. The neural-network (NN) weights are tuned on-line and no exact knowledge of nonlinearities is required. The time delays considered in the forward and backward communication channels are constant and equal. A modified Smith predictor is proposed to compensate the time delays. The controller is designed based on the developed NN observer and the proposed Smith predictor. By using the Lyapunov theory, rigorous stability proofs for the closed-loop system are presented. Finally, simulations are performed and the results show the effectiveness of the proposed control strategy.

A fractional model for robust fractional order Smith predictor

In this paper, we propose to use a fractional order model to predict the process output in Smith predictor. The parameters of the model are determined by minimizing the error between its output and one of the processes using a genetic algorithm. After determining the model’s parameters, a fractional PID controller is proposed to improve the controlled system performances. The parameters of the controller are also determined in an optimal way by minimizing the position error taking into account the sensitivity and the complementary sensitivity conditions. Applications on a dead time and multiple lags processes have been performed, where the simulation results show that the proposed Smith predictor enhance the closed loop control system.

New Smith predictor control for the time delay systems

In the present paper, we examine a design method of Smith predictor control system with time delay. Almost past proposed Smith predictor control design methods cannot apply for the system having open right half plane poles, but those by De Paor et al. and by Kwak et al. can. Since the control structure of the modified Smith predictor control systems proposed by De Paor et al. and by Kwak et al. are limited, nothing has been reported whether or not, the modified Smith predictor control system can represents all of stabilizing controllers for the plant with time delay. If the modified Smith predictor control system can represent all of stabilizing controllers for the plant, the modified Smith predictor control system is effective. The purpose of the present paper is to give a new control structure of the modified Smith predictor control system for unstable minimum phase systems with time delay. Proposed structure can represent all of controllers for the unstable minimum phase systems with time delay.

Modified Smith Predictors for Integrating Processes: Comparisons and Proposition

We summarize and systematically analyze the strong and weak points of previous modified Smith predictors for integrating processes, and we improve previous approaches by introducing an enhanced Smith predictor. The proposed Smith predictor can accelerate the servo tracking response as much as we want without destabilizing the closed-loop response by separating the servo and regulatory control problems. Its structure is very compact so that it can realize the good control performances of the previous approaches while retaining simplicity. Moreover, its design strategy can incorporate much more general integrating processes than previous approaches.

A Modified Smith Predictor with a New Structure for Unstable Processes

In this paper, we propose a modified Smith predictor with a new structure for unstable processes. The proposed new structure makes it possible to use the unstable model itself as a process model, while the previous modified Smith predictors provided no option to use a mismatched stable process model to guarantee stability. Accordingly, the proposed Smith predictor can predict the dynamics of the actual process much better than previous approaches. It also guarantees good robustness to modeling errors by separating servo and regulatory problems. Moreover, it demonstrates good disturbance rejection performances because the adjustable parameters of the PID controller for the regulatory problem are tuned systematically with an internal feedback loop.