Design of active disturbance rejection controller for the system with unknown model using Laguerre functions

Aiming at the problem of anti-swing and positioning of underactuated crane, this paper proposes a control strategy of underactuated bridge crane based on Linear Active Disturbance Rejection Control and controller parameter optimization method. Distinguished from other conventional control strategies of bridge crane, this method does not require any approximate decoupling or linearization of the crane model and allows the model to have some certain uncertainty, friction and air resistance of the system are also considered. At the same time, for the parameter tuning problem of the sensors, the bird swarm optimization algorithm is applied to optimize the parameters. And the excellent control performance of this method has been proved by simulation.

Linear active disturbance rejection control (ADRC) is known for its simplicity and its performance in disturbance attenuation. Currently, tuning of linear ADRC is via the bandwidth idea proposed in [1]. It is first shown that there are some limitations using only two bandwidths to tune the linear ADRC controllers. In this paper, tuning of linear ADRC with extra model dynamic information is investigated. It is shown that the available model dynamic information used in generalized ADRC (GADRC) can be realized by the observer gain and the controller gain of the conventional linear ADRC, thus extends the applicability of conventional linear ADRC. Simulation results show that with extra model dynamic information incorporated, by tuning the observer gain and controller gain, the performance of a conventional linear ADRC can indeed be improved, especially for non-minimum phase and time-delayed processes.

Proportional Derivative Integral (PID) controllers are extensively used in most of the industrial applications. But often, PID frame work fails to address some serious issues like susceptibility to noise, instability due to integral term etc. On the other hand Active Disturbance Rejection Control (ADRC) addresses these issues quite successfully. The problem considered in this work is to control Automatic Voltage Regulator (AVR) with Linear ADRC (LADRC) under presence of plant parameter uncertainties and sudden load variations.A fifth-order Extended State Observer (ESO) was designed which estimates generalized disturbance which includes both system uncertainties and external disturbances. This information is used to actively cancel the effects of disturbances. The effectiveness of LADRC scheme has been illustrated by comparing with conventional PID tuning method in the presence of plant parameter and sudden load variations. Further to study the frequency response characteristics of control system, transfer function of LADRC has been derived. From the simulations it can be observed that there is significant improvement in system response which can be reconfirmed from the frequency response analysis.

Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based on a reinforcement learning active disturbance rejection controller. The linear active disturbance rejection controller has achieved satisfactory results in the field of underwater robot control. However, fixed-parameter controllers cannot achieve optimal control performance for the controlled object. Therefore, further exploration of the adaptive capability of control parameters based on the linear active disturbance rejection controller was conducted. The deep deterministic policy gradient (DDPG) algorithm was used to optimize the linear extended state observer (LESO). This strategy employs deep neural networks to adjust the LESO parameters online based on measured states, allowing for more accurate estimation of model uncertainties and environmental disturbances, and compensating the total disturbance into the control input online, resulting in better disturbance estimation and control performance. Simulation results show that the proposed control scheme, compared to PID and fixed parameter LADRC, as well as the double closed-loop sliding mode control method based on nonlinear observers (NESO-DSMC), significantly improves the disturbance estimation accuracy of the linear active disturbance rejection controller, leading to higher control precision and stronger robustness, thus demonstrating the effectiveness of the proposed control strategy.

To improve the robustness and the anti-disturbance of control system, regulate its stiffness, a scheme concluding linear active disturbance rejection controllers for pneumatic servo system is proposed. Two linear active disturbance rejection controllers are designed respectively to track the motion and pressure reference. The design of a linear active disturbance rejection controller is independent of precisely mathematical model of the system. The extended state observer can track successfully the states and extended state of the system, and it is possible to realize the states feedback and handle the model uncertainty and compensate external disturbance. The simulation results show that pneumatic servo system based on linear active disturbance rejection controllers is robust against modeling uncertainty and external disturbances. The dynamic performance of the system using linear active disturbance rejection controllers also can be improved as well.

Active disturbance rejection control for nanopositioning: A robust U-model approach

Owing to ship's high inertia, strong time lag, non-linearity and under-actuated characteristics, as well as sustaining external disturbances frequently, designing a high performance ship tracking controller is still one of the most important research topics of ship motion control. Active Disturbance Rejection Control (ADRC) is proved to be very effective control method because it is independent of mathematical model of the plant and can compensate the internal and external disturbances dynamically. This paper investigates the applications of ADRC in ship motion tracking control and presents the design method of ADRC controller appropriate for ship tracking control. Firstly, ship motion mathematical model is introduced briefly and the linear ADRC (LADRC) is analyzed. Then considering the nonlinear ship track design model as a third order plant after utilizing variable substitution, a LADRC controller is designed by using fourth order linear extended state observer (LESO). At last, the simulation is performed. Simulation results show that the controller has a good tracking performance and disturbance rejection ability and is robust to the ship motion nonlinear characteristic and the external disturbances. The tracking process is fast, smooth and minor energy consumption.

Two new active disturbance rejection control (ADRC) structures for nonlinear systems are introduced: a locally linearized variant and a fully nonlinear formulation. Both approaches incorporate model knowledge to enhance performance but differ in how nonlinear dynamics are integrated into the control and observer design. The first proposed structure employs a state-dependent local approximation of the nonlinear model to generate dynamic controller and observer gains, aiming to balance robustness and accuracy. In contrast, the second one directly embeds the full nonlinear dynamics into both the control law and extended state observer, tightly coupling performance to model fidelity. The proposed methods were experimentally validated on a magnetic levitation system, known for its strong nonlinearity, and compared with a classical linear ADRC (LADRC). Furthermore, stability analysis of the methods was conducted using Lyapunov theory. Results show that the linearized structure consistently improves regulation performance over LADRC and, in most cases, achieves similar results to nonlinear ADRC with lower computational effort. However, the performance of the nonlinear approach may degrade under modeling inaccuracies and limited observer bandwidth. This study highlights that the way model information is integrated–rather than its level of detail–has a decisive impact on control quality. Finally, practical design guidelines are provided to assist in selecting an appropriate ADRC structure for nonlinear applications where robustness, computational efficiency, and limited model knowledge must be balanced.

The stability of the boiler’s main steam temperature is very important for the safe operation of the unit. At present, the mainstream PID control scheme is simple to use and convenient to set, but it cannot accomplish a acceptable ascendancy effect in the face of the main steam temperature control structure; with a long time delay, large inertia. In view of this situation, first of all, this paper introduces the linear active disturbance rejection control (LADRC) technology in a steam temperature control structure. The main loop adopts the linear active disturbance rejection controller and the subloop adopts the P-controller. Then, the particle swarm optimization algorithm is improved and used to modify the parameter of the LADRC, so that the LADRC system adjusted according to the theory can better complete the control task. Finally, a simulation to control the temperature is given, and the results show that the LADRC control system can track quickly and stably; in addition, as against with this traditional PID control strategy, it can achieve strong anti-interference performance.

Decentralized Active Disturbance Rejection Control for the Benchmark Refrigeration System

Linear active disturbance rejection control (LADRC) is applied as power system stabilizer (PSS) in power systems to damp the low-frequency oscillations. A tuning method for second-order LADRC is proposed specifically for damping the low-frequency oscillations. The tuning method is based on its own structure (zeros and poles) and the oscillation frequencies that need to be damped, thus it is independent of system models and not restricted to single-machine-infinite-bus (SMIB) systems. Moreover, a method to analyze the robustness is proposed for SMIB systems using the structured singular value method. Simulation studies on a SMIB system and two multi-machine power systems show that LADRC is easy to tune and can achieve good performance in damping both the local modes and the inter-area modes, thus it has great potential in power system stability control.

There exists a steady-state error in the closed-loop system output with some conventional linear active rejection control approaches when the pitch channel control variable of the near space vehicle is the angle of attack. In this study, a cascaded linear active disturbance rejection control approach based on the cascaded control idea is proposed. Both the inner-loop controller and the outer-loop controller are designed by the linear active disturbance rejection control method. The inner-loop is built by angular rate feedback to improve dynamic performance of the closed-loop system, and the outer-loop is built by angle of attack feedback to remove the undesired steady-state error. The approach is employed to a hypersonic near space vehicle autopilot. The numerical simulations show that the proposed approach preserves the performances on time domain and frequency domain as good as those of the conventional active disturbance rejection control approaches, and it has the better performances on disturbance rejection and angle of attack command tracking accuracy than those of some previous active disturbance rejection control approaches.

Generalized separation principle (GSP) is a main reason why active disturbance rejection control (ADRC) is easy to use. In this short note, GSP is interpreted from the respective of robust control. Linear active disturbance rejection control (LADRC) is described as an interconnection of two subsystems, while to design LADRC is a process to tuning the gains of them. Small gain theorem guarantees separately tuning is possible. That is why GSP works.

This paper presents a robustness study of a Linear Active Disturbance Rejection Control (LADRC) framework. A dynamic simulative study is carried out on a two-wheeled differential drive mobile robot (DDMR) velocity control. A dynamic model of the robot with actuators has been considered, including uncertain parameters and a coupling between the linear and angular velocities, and the two angular velocities, the robot motion is also a subject of external disturbances which can degrade the performance of classical controllers. However, In LADRC, the unmeasured dynamics and external disturbances are considered as an additional state variable, it is then estimated by a linear Extended State Observer (LESO) and compensated in the control law. The simulation results show the efficiency of proposed LADRC-based controller in the reduction of the effect of dynamic variations. The LADRC robustness is compared with a proportional-integral-derivative controller (PID). Better results in terms of disturbance rejection have been reported for the LADRC.

In this paper, linear active disturbance rejection control (LADRC) is investigated for the load frequency control (LFC) systems with wind turbine. The wind power is considered as a stochastic load to the power grid. To compensate the stochastic disturbance with LADRC, we adopt a two-stage design method. First an LQG controller is designed to handle the stochastic nature of the load, then the intrinsic link between LQG and LADRC is analyzed and the designed LQG controller is converted to a conventional LADRC so that the LADRC maintains the same property of rejecting stochastic disturbance with the LQG controller. Simulation results show that the designed LADRC with the proposed method can damp the stochastic load disturbance very well.