Analytical tuning rules for second-order reduced ADRC with SOPDT models.

This paper presents a set of tuning rules for second-order reduced linear active disturbance rejection control for second-Order Plus-Dead-time (SOPDT) models. Rules are developed with a target to achieve a good compromise between tracking and disturbance rejection performances. Subsequently, it is formulated as a multi-objective optimization problem consisting of Integral Absolute tracking and regulatory errors as its objectives with a specified robustness level and a stability condition as constraints. The optimization problem is solved by a Multi-objective Quasi-Oppositional Rao-1 (MOQO-Rao-1) algorithm to generate the required Pareto optimal solutions. A compromised solution is chosen among these Pareto optimal solutions using Grey Relational Analysis (GRA). Finally, the resulting best solutions are used to fit a polynomial model using regression resulting in analytical tuning rules. Separate tuning rules are presented for lag-dominated and delay-dominated SOPDT models. The proposed tuning rules are validated through simulations on standard benchmark systems, power-system load frequency control problems, and experimentally on a temperature control system and DC motor control system. Furthermore, a condition on tuning parameters for closed-loop system stability is presented using the dual-locus method; the same is incorporated as one of the constraints in the proposed tuning framework.

Analytical tuning rules for Reduced-order Active Disturbance Rejection Control with FOPDT models through Multi-Objective optimization and multi-criteria decision-making.

Active Disturbance Rejection Control (ADRC) emerged as a promising control solution in various engineering domains. However, increased ADRC order makes it difficult to implement and tune in practice. On the other hand, Reduced-order ADRC (RADRC) structure solves this issue with the appropriate tuning of its parameters to achieve the desired performance. This paper aims to develop analytical tuning rules for RADRC for processes approximated as First-order plus dead-time models (FOPDT). These rules meet the conflicting goals of tracking and disturbance rejection restricted by robustness specification. The tuning rules are derived based on a multi-stage approach. In the first stage, the tuning problem is formulated as a multi-objective optimization problem with appropriate constraints. A Multi-objective Quasi-Oppositional Rao-1 (MOQO-Rao-1) Algorithm solves the optimization problem to produce a collection of Pareto-optimal solutions (alternatives) in the second stage. In the third stage, using the Best-Worst based PROMETHEE method, the best one is chosen among the available options. Finally, using linear regression, analytical tuning rules are developed. Separate tuning rules are proposed for lag-dominated and dead-time dominated cases. Simulation experiments on benchmark industrial processes are performed, and the findings assess the efficacy of the suggested tuning rules relative to the methods recently published. The proposed tuning rules are experimentally validated to assess their applicability in the practical scenario. Besides, the closed-loop system's stability with the suggested tuning rules is confirmed by the small-gain theorem and the dual-locus process.

An AHP based optimized tuning of Modified Active Disturbance Rejection Control: An application to power system load frequency control problem.

This paper presents Modified Active Disturbance Rejection Control (MADRC) scheme and a systematic procedure to tune its tuning variables via. observer and controller gains. The problem of tuning is formulated as a constrained multi-objective optimization problem. Depending upon the application appropriate objective functions are chosen, and a unified objective is developed based on weighted sum approach. Five different weight combinations are considered, which result in five objective functions. Each objective function is individually optimized using Teaching-Learning Based Optimization (TLBO), this leads to five sets of tuning variables. Best among five alternatives will be selected based on Analytical Hierarchy Process (AHP). The effectiveness of this approach is validated through its application to a power system with load frequency control problems. Different simulation examples involving single-area and multi-area power systems are considered in the presence of sudden load disturbances, model uncertainties, and parameter variations. Further stability analyses are carried out in a standard two-degree of freedom Internal Model Control (IMC) framework.