To address time delay and noise problems in control systems, in this study, we integrated an extended state filter for signal filtering into an active disturbance rejection control (ADRC) system and derived an improved ADRC approach. In addition to the active anti-disturbance and active tracking estimation functions of the existing ADRC, the proposed approach also includes active filtering and active advance prediction functions, which can filter out the effect of measurement noise on system state observation while reducing the delay between the system control output and the detection of the sensor input. We verified through an evaluation in a simulation environment that the proposed approach may be expected to achieve improved control accuracy and increase the stability of closed-loop control systems.

https://www.mdpi.com/1996-1073/15/16/5799/pdf?version=1660215411

Improved Active Disturbance Rejection Control (ADRC) with Extended State Filters

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 second-order reduced ADRC with SOPDT models.

The extended state observer (ESO) is an inherent element of robust observer-based control systems that allows estimating the impact of disturbance on system dynamics. Proper tuning of ESO parameters is necessary to ensure a good quality of estimated quantities and impacts the overall performance of the robust control structure. In this paper, we propose a neural network (NN) based tuning procedure that allows the user to prioritize between selected quality criteria such as the control and observation errors and the specified features of the control signal. The designed NN provides an accurate assessment of the control system performance and returns a set of ESO parameters that delivers a near-optimal solution to the user-defined cost function. The proposed tuning procedure, using an estimated state from the single closed-loop experiment produces near-optimal ESO gains within seconds.

Tuning of extended state observer with neural network-based control performance assessment.

The paper presents a stability analysis of a closed-loop system with an active disturbance rejection control (ADRC) algorithm and a reduced-order extended state observer (RESO). The controller is designed for first-order plus delay time processes, and one of the input signals to the RESO is delayed to compensate for the effect of the system delay. If the process delay is known, perfect synchronization between the observer input signals can be achieved. In a more realistic case, the process delay is not precisely known, and the resulting imperfect synchronization may lead to instability of the closed-loop system. The perfect synchronization case shows that the closed-loop system can be stabilized for any delay. For the imperfect synchronization case, delay-dependent stability conditions are derived for a simple tuning method.

On the stability of active disturbance rejection control for first-order plus delay time processes

Industrial processes often involve a long time delay, which adversely affects the stability of closed-loop control systems. The traditional Smith Predictor (SP) is a model-based controller used in processes with large time delays. The variation of system parameters and load disturbance situations are disadvantages of the traditional SP, and researchers have, therefore, proposed modified SP structures. In this paper, a design method based on the direct synthesis approach on a modified SP structure is discussed. In the design, an I-PD controller structure is used on the set-point tracking side of the SP, and a cascading PD lead–lag controller is used on the disturbance rejection side. In contrast with other studies in the literature, the use of simpler controllers enables the mathematical expressions that arise in the direct synthesis method to be significantly reduced. The proposed method is examined under the disturbance input effects for normal and parameter-changing conditions on system models with unstable second-order plus time-delay processes. The first plant model has two unstable poles, the second has one stable and one unstable pole, and the third has one unstable and one zero pole. When the results obtained using the proposed method were compared with other methods, significant improvements were achieved in terms of set-point tracking, disturbance rejection, and robustness conditions.

https://www.mdpi.com/2227-9717/11/3/941/pdf?version=1679791724

Smith Predictor Controller Design Using the Direct Synthesis Method for Unstable Second-Order and Time-Delay Systems

In this article, increment count method (ICM) is proposed as a novel approach for simultaneous steady-state detection and transportation time delay estimation for industrial processes. The concept of ICM is presented, and its implementation issues are discussed. Especially, the simplicity and very low computational complexity of ICM are addressed, which support its practical implementation. Then, as a practical example, ICM is used as a part of the autotuning algorithm dedicated for reduced-order active disturbance rejection controller with Smith predictor (RADRC+SP). Jointly with dedicated optimization algorithm, it allows for identification between the first or second order of process dynamics. The results of this identification allow for effective tuning of RADRC+SP based on the novel tuning rules. The practical aspect includes programmable logic controllers (PLC) based implementation of both autotuning and RADRC+SP controller in the form of the general-purpose function blocks. The experimental validation shows the practical applicability and superiority of the proposed methods.

Increment Count Method and Its PLC-Based Implementation for Autotuning of Reduced-Order ADRC With Smith Predictor

The use of Gramme coils in a 2-phase system for generation of a high frequency rotating magnetic field

This paper presents a new approach to the general-purpose self-improving controller based on Q-learning control strategies. The previous approach was based on a three-dimensional Q-matrix, significantly slowing down the learning process and limiting the ability of practical implementation in industrial practice. The proposed new algorithm solves this problem by reducing the size of the matrix and the number of tuning parameters of the method without sacrificing its accuracy and learning capabilities. The algorithm is validated by simulation on the two second-order dynamics models and the results show significant improvement compared to the previous version of the developed method.

Implementation aspects of Q-learning controller for a class of dynamical processes

This paper presents how Q-learning algorithm can be applied as a general-purpose selfimproving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller.

Jakub Musial

Papers

Increment Count Method and Its PLC-Based Implementation for Autotuning of Reduced-Order ADRC With Smith Predictor

The use of Gramme coils in a 2-phase system for generation of a high frequency rotating magnetic field

Implementation aspects of Q-learning controller for a class of dynamical processes

Self-improving Q-learning based controller for a class of dynamical processes