Design of I-PD Controller Based Modified Smith Predictor for Processes With Inverse Response and Time Delay Using Equilibrium Optimizer

Abstract

In the process control industry, it is arduous to control some integrating or unstable processes since they involve time delays and have an inverse response. Conventional controllers such as PID cannot provide sufficient control performance alone in the control of these systems. This article proposes a control algorithm based on an I-PD-based Smith predictor for the control of time-delayed integrating or unstable inverse processes. The controller parameters are tuned by using the Equilibrium optimizer (EO) algorithm, which is presented in the literature in 2020, in the proposed control approach. The EO algorithm aims to determine the optimal controller parameters by minimizing the error and control signal using a multiobjective function based on ITAE performance criterion. Thus, the controller parameters that will provide the set-point tracking and disturbance rejection control most properly can be determined. Simulation studies are conducted based on different process structures to evaluate the performance of the proposed method. The proposed method is compared with studies from the literature in terms of set-point tracking, parameter uncertainties, control signals, and disturbance rejection. It is seen that the transient responses and disturbance rejection of the time-delayed and inverse response integrating or unstable processes are improved with the proposed method.INDEX TERMS Equilibrium optimizer algorithm, I-PD controller design, inverse response, non-minimum phase system, Smith predictor, and time delay systems.

Repositioned Internal Model Control Strategy on Time-Delayed Industrial Processes With Inverse Behavior Using Equilibrium Optimizer

“…The PI-PD structure is further modified to I-PD DCL scheme by a few researchers [9], [32], [33]. Very recently, Doğruer [34] used a modified SP-based I-PD scheme for controlling inverse response processes. However, the abovecited work involves two heuristically-tuned PD controllers in contrast to traditional DCLs.…”

“…Among the metaheuristic techniques available of late, EO has emerged as a prominent algorithm since its inception by Faramarzi et al [43]. Predominantly used for load frequency control methods [44], [45], EO has now gained popularity in process control applications [1], [2], [34], [46] with its ability to simultaneously tune multi-parameters to achieve the desired performance.…”

Direct synthesis approach to design PDμ${\text{PD}}^\mu$ based smith predictor for integrating plants: Studied on a quadrotor UAV

“…One of the recent articles proposes a control algorithm based on an I-PD-based SP to control time-delayed integrating and unstable inverse processes. The controller parameters were tuned using the equilibrium optimizer algorithm and provided significantly improved performance compared to other methods in the literature [6]. A modified Smith predictor (MSP) with IMC-based tuning having only one tuning parameter has been proposed for second-order delay-dominated processes with right-hand pole [7].…”

Advanced Flowrate Control of Petroleum Products in Transportation: An Optimized Modified Model Reference PID Approach

“…The Ziegler-Nichols method is a frequently employed approach for adjusting the parameters of a PID controller by providing initial parameter values based on open-loop response data [13]- [15]. However, the Ziegler-Nichols method may not result in optimal parameters for a transportation lag system due to its linear and time-invariant nature [15]- [19]. To overcome these limitations, several studies have explored PID controller design methods for transportation lag systems with first order plus time delay [20] [21], such as the PID controller based on Ziegler-Nichols tuning, Internal Model Control tuning, and Shams Internal Model Control tuning [22], PID controller design based on the minimum error tunning rules [23] [24], enhanced fractional filter PID controller design [25], PID controller design based on fractional order tuning [26], directly synthesized parallel PID controller [27], active disturbance rejection PID controller [28], optimized PID controller based on linear programming [29], PI-PD controller design by considering both the maximum sensitivity principles and the Routh-Hurwitz stability criteria [30], particle swarm optimization-based PID controller [31], PID controller design based on disturbance observer [32], and filter-based PID with low-gain internal model control [33].…”