Optimization Design and Application of Active Disturbance Rejection Controller Based on Intelligent Algorithm

An active disturbance rejection control design for the distillation process with input saturation via quantitative feedback theory

A two-stage Active Disturbance Rejection Control (ADRC) design procedure is presented for the output trajectory tracking control problem in Single Input Single Output (SISO) nonlinear systems. Aside from the given scalar system output, the plant is assumed to exhibit a second, physically measurable, internal scalar output variable. The system output is controlled in an ADRC manner from an implicit static, or dynamic, nonlinear feedback controller using, in general, a nonlinear differential expression of the internal output variable. A desired internal output trajectory is thus generated. The internal output variable, in turn, is controlled from the original input and forced to track the desired trajectory synthesized in the previous step by the auxiliary controller. Each ADRC controller design is based on a simplified model of the corresponding subsystem, usually relegating interaction variables and additive nonlinearities to be part of a local (total) disturbance input term. The control scheme is reduced to a set of linear ADRC controllers with, possibly, cancellation of nonlinear input gains which are assumed to be known. Two illustrative aerospace examples are presented where the proposed procedure is illustrated via digital computer simulations.

This paper proposes a parameter tuning method of fractional-order active disturbance rejection controller (FOADRC) for permanent magnet synchronous motor (PMSM) servo system with frequency-domain analysis. In this method, a fractional-order proportional derivative (FOPD) and a fractional-order [proportional derivative] (FO[PD]) controllers design combining with active disturbance rejection control (ADRC) are proposed. A systematic parameter tuning scheme is presented, and a flat phase constraint is applied as a design specification, which guarantees the phase is flat around the gain crossover frequency point. The closed-loop system is robust to gain variations. The simulation results illustrate that the response of the PMSM servo system using FO[PD]-ADRC has a smaller overshoot, less tracking error, and better resistance to load disturbance than that using fractional-order proportional derivative ADRC (FOPD-ADRC).

In order to improve the stability and sensitivity of particle swarm optimization (PSO) algorithm and to solve the problem of premature convergence, in this re-search, a computer PSO algorithm based on thermodynamic motion mechanism is proposed based on the principle of thermodynamic motion mechanism. Firstly, the thermodynamic motion phenomenon, the diffusion law in kinematics and the standard PSO algorithm are introduced. Then, according to the basic idea of thermodynamic motion mechanism, the standardized PSO algorithm is optimized and its optimization process is introduced. Finally, the experimental results are analysed by setting the test function. The results show that among the five test functions, the computer PSO algorithm based on thermodynamic motion mechanism has a higher probability of jumping out of the local optimal solution. Its robustness and stability are much better than standard PSO algorithms. The evolution ability of the computer PSO algorithm based on thermodynamic motion mechanism is better than that of the standard PSO algorithm. The standard PSO algorithm is superior because it is based on thermodynamic motion mechanism. The research in this paper can provide good guidance for improving the performance of PSO algorithm.

The intelligent motion control technology of Autonomous Underwater Vehicle (AUV) is the basis of long-distance voyage for AUV. The motion control capability in vertical plane is an important technical for AUV to complete multifarious missions. So, it is important and realistic significance to research on motion control in vertical plane of AUV. Motion control problem of AUV in vertical plane, especially depth control problems is researched based on active disturbance rejection control (ADRC) method in this paper. The kinetic and dynamic model in vertical plane of AUV is established and simplified. The active disturbance rejection controller design method is introduced. Detailed algorithms of each part for active disturbance rejection controller are discussed according to separability principle of ADRC. Based on ADRC, the depth controller is designed and applied in AUV. At last, simulation results indicate that the method is feasibility and effectiveness for AUV depth control.

International audience

<div>In this dissertation, methods for real-time trajectory generation and autonomous obstacle avoidance for fixed-wing and quad-rotor unmanned aerial vehicles (UAV) are studied. A key challenge for such trajectory generation is the high computation time required to plan a new path to safely maneuver around obstacles instantaneously. Therefore, methods for rapid generation of obstacle avoidance trajectory are explored. The high computation time is a result of the computationally intensive algorithms used to generate trajectories for real-time object avoidance. Recent studies have shown that custom solvers have been developed that are able to solve the problem with a lower computation time however these designs are limited to small sized problems or are proprietary. Additionally, for a swarm problem, which is an area of high interest, as the number of agents increases the problem size increases and in turn creates further computational challenges. A solution to these challenges will allow for UAVs to be used in autonomous missions robust to environmental uncertainties.</div><div><br></div><div>In this study, a trajectory generation problem posed as an optimal control problem is solved using a sequential convex programming approach; a nonlinear programming algorithm, for which custom solver is used. First, a method for feasible trajectory generation for fast-paced obstacle-rich environments is presented for the case of fixed-wing UAVs. Next, a problem of trajectory generation for fixed-wing and quad-rotor UAVs is defined such that starting from an initial state a UAV moves forward along the direction of flight while avoiding obstacles and remaining close to a reference path. The problem is solved within the framework of finite-horizon model predictive control. Finally, the problem of trajectory generation is extended to a swarm of quad-rotors where each UAV in a swarm has a reference path to fly along. Utilizing a centralized approach, a swarm scenario with moving targets is studied in two different cases in an attempt to lower the solution time; the first, solve the entire swarm problem at once, and the second, solve iteratively for a UAV in the swarm while considering trajectories of other UAVs as fixed.</div><div><br></div><div>Results show that a feasible trajectory for a fixed-wing UAV can be obtained within tens of milliseconds. Moreover, the obtained feasible trajectories can be used as initial guesses to the optimal solvers to speed up the solution of optimal trajectories. The methods explored demonstrated the ability for rapid feasible trajectory generation allowing for safe obstacle avoidance, which may be used in the case an optimal trajectory solution is not available. A comparative study between a dynamic and a kinematic model shows that the dynamic model provides better trajectories including aggressive trajectories around obstacles compared to the kinematic counterpart for fixed-wing UAVs, despite having approximately the same computational demands. Whereas, for the case of quad-rotor UAVs, the kinematic model takes almost half the solution time than with a reduced dynamic model, despite having approximately the similar range of values for the cost function. When extended to a swarm, solving the problem for each UAV is four to seven times computationally cheaper than solving the swarm as a whole. With the improved computation time for trajectory generation for a swarm of quad-rotors using centralized approach, the problem is now reasonably scalable, which opens up the possibility to increase the number of agents in a swarm using high-end computing machines for real-time applications. Overall, a custom solver jointly with a sequential convex programming approach solves an optimization problem in a low computation time.</div>

This chapter deals with the active disturbance rejection control (ADRC) method to the lateral control of small air-to-ground missiles for helicopters. The main difficulties that arise in lateral control design of small missiles are related to the highly uncertain, nonlinear aerodynamic forces and force movements, thus it is difficult to obtain an accurate dynamic model. Besides, the huge variations of dynamic pressure make controller parameters, and are difficult to adapt to the whole flight envelope. To handle them, the design of the controller is based on ADRC, a nonlinear control design approach which is based on the extended state observer (ESO), and can estimate and compensate the uncertainty of the system, including structural errors, random, and other system errors. Six-degree freedom equations are established for the lateral channel, the equations are linearization with small disturbance theory, then the aerodynamic derivatives are obtained through wind tunnel test, the dynamic model is made and the aerodynamic features are analyzed. One-order ESO equations of lateral channel are deduced, the controller of the lateral channel which is based on the ESO is designed and parameters are adjusted, and finally, semi-physical simulations are made. Through the comparison with classical proportion integral differential (PID) controller, the controller based on ADRC has strong robustness and it could track control command rapidly and accurately.

In this paper, we propose a continuous finite-time-convergent active disturbance rejection control design scheme for a class of second order uncertain nonlinear systems. A continuous finite-time-convergent extended state observer is first developed to estimate the total disturbance which includes the nonlinear time-varying uncertain dynamics and the external disturbances. A continuous finite-time active disturbance rejection control design scheme is proposed by using saturation method. The key idea is using the estimate of the total disturbance to compensate the uncertainty such that the output tracks a given setpoint in finite time and stays at the setpoint afterwards, and all states of the closed-loop system remain uniformly bounded. By this dynamic compensation and saturation strategy, the finite-time stability of the error system is established under some weak restrictions on nonlinearities of the system. Experiments on a DC motor platform are carried out to illustrate the effectiveness of the proposed method.

A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

In order to improve the scanning performance of an atomic force microscopy (AFM), advanced position control of the piezoelectric actuator along the z-axis should be carefully designed to enhance the control precision, speed and robustness. In this paper, active disturbance rejection controller (ADRC) is applied to replace the traditional Proportional-Integral-Derivative (PID) controller to improve the control performance of an AFM along its z-axis. Specifically, firstly we set up the transient profile generator to obtain the error signal more reasonably. Then, the tracking differentiator is designed to replace the traditional differentiator to reduce the noise amplification of output signals and realize fast tracking of input signals. Afterwards, we design an extended state observer to implement the state estimation. In this way, the entire ADRC is designed for the AFM in z-axis to improve the control speed, precision, as well as the robustness of the AFM system. Simulation results demonstrate the effectiveness of the proposed controller.

An active disturbance rejection control design with actuator rate limit compensation for the ALSTOM gasifier benchmark problem

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

The quad tilt rotor (QTR) has complex dynamics characteristics, especially in transition mode. It is difficult to model the QTR dynamics and the environmental factors have a great influence on it. To solve the problem of control in transition mode of QTR, this paper carries out the design of the controller based on active disturbance rejection control (ADRC). ADRC has many parameters to be tuned, and the coupling effect is more serious, so it is very difficult to tune parameters. Particle swarm optimization (PSO) algorithm has faster computing speed and better global search ability in dynamic and multi-objective optimization. So, using PSO algorithm to realize self-tuning of ADRC parameters in ADRC parameters tuning. By comparing the control performance of ADRC before and after optimization, the rationality and effectiveness of ADRC parameters tuning algorithm in QTR are verified in both time domain and frequency domain.

Active disturbance rejection control (ADRC) technique was applied for attitude and trajectory control of the flying-wing unmanned aerial vehicle (UAV). Considering the characteristic of static instability in longitudinal and directional, a nonlinear ADRC controller was designed for the flying-wing UAV. After tuning the parameters, altitude command with step change, and gust disturbance were given when it was in simulation at the state of a straight and level path flight. The results showed the steady-state error of altitude reduced by 0.0235m, and the setting time of altitude reduced by 8.2s. To some extent, it is because this controller can estimate and compensate for the disturbance. Comparing with proportion-integration and differentiation (PID) controller, the ADRC controller we designed can solve the problem of oscillations, and the oscillations are often generated by step change of state variable or input command. Relevant results indicate that ADRC controller can not only meet the control accuracy requirement, but also satisfy the requirement of the rapid response, robustness and insensitivity to perturbations.