The quadrotor aircraft has the characteristics of simple structure, high attitude maintenance performance and strong maneuverability, and is widely used in air surveillance, post−disaster search and rescue, target tracking and military industry. In this paper, a robust control scheme based on linear active disturbance rejection is proposed to solve the problem that the quadrotor is susceptible to various disturbances during the take−off process of non−horizontal planes and strong disturbances. Linear Active Disturbance Rejection Control (LADRC) is a product of a tracking differentiator (TD), a linear extended state observer (LESO) and an error feedback control law (PD) and is a control technique for estimating compensation for uncertainty. Radial Basis Function Neural Networks (RBFNN) is a well−performing forward network with best approximation, simple training, fast learning convergence and the ability to overcome local minima problems. Combined with the advantages and disadvantages of LADRC, Adaptive Control and Neural Network, the coupling force between each channel, gust crosswind disturbance and additional resistance of offshore platform jitter in the flight state of the quadrotor are optimized. In the control, the RBF neural network is designed, the nonlinear control signal is wirelessly approximated and the uncertain disturbance to the quadrotor is identified online. Finally, the real−time estimation and compensation are performed by LESO to realize the full−attitude take−off of the quadrotor. In addition, this paper uses adaptive control to optimize the parameters of LADRC to reduce the problem of many LADRC parameters and difficulty to integrate. Finally, the robust control system mentioned in this paper is simulated and verified, and the simulation results show that the control scheme has the advantages of simple parameter adjustment and stronger robustness.

According to the traditional voltage and current double closed-loop control mode, the inverter management strategy for photovoltaic grid connection has insufficient anti-interference ability and slow response. This paper proposes a control strategy that applies adaptive-linear active disturbance rejection control (A−LADRC) to the outer loop control to achieve the purpose of anti-interference. The control strategy uses the linear extended state observer (LESO) to evaluate external interference caused by the change of external conditions and the internal disturbance caused by parameter uncertainty. PD controller compensates the disturbances and adds adaptive control to simplify parameter adjustment. Finally, this paper takes advantage of Lyapunov theory to conduct stability analysis. Compared with the traditional linear active disturbance rejection control (LADRC), the superiority of this control strategy is verified. The experimental results show that the system has better control performance and anti-interference ability in the face of various disturbances.

This paper proposes a control scheme combining improved particle swarm optimization (IPSO) and adaptive linear active disturbance rejection control (ALADRC) to solve the high-speed train (HST) speed tracking control problem. Firstly, in order to meet the actual operation of a HST, a multi-mass point dynamic model with time-varying coefficients was established. Secondly, linear active disturbance rejection control (LADRC) was proposed to control the speed of the HST, and the anti-disturbance ability of the system was improved by estimating and compensating for the total disturbance suffered by the carriage during the operation of the HST. Meanwhile, to solve the problem of difficult parameter tuning of the LADRC, IPSO was introduced to optimize the parameters. Thirdly, the adaptive control (APC) was introduced to compensate for the observation error caused by the bandwidth limitation of the linear state expansion observer in LADRC and the tracking error caused by an unknown disturbance during the train’s operation. Additionally, the Lyapunov theory was used to prove the stability of the system. Finally, the simulation results showed that the designed control scheme is more effective in solving the problem of HST speed tracking.

In this paper, we propose a cascade control system design based on linear active disturbance rejection control (SMC&A-LADRC) to address the following quadrotor UAV problems: that the path is easily yawed when disturbed, the control parameters are difficult to optimize, and the tracking accuracy is low. The strategy can effectively eliminate external disturbances and adjust the controller parameters online so that the quadrotor UAV always flies on the optimal path to achieve energy optimization and long-endurance flight. The proposed cascade control system combines the advantages of sliding mode control (SMC) and linear active-disturbance rejection control (LADRC), using the linear extended state observer (LESO) to estimate the uncertain external disturbances and unmodeled internal dynamics of the quadrotor system, compensate for the uncertain signals under SMC chattering, optimize the PD controller parameters online using adaptive control to eliminate the effects of parameter deviations, and simplify the parameter adjustment process. Finally, a stability analysis of the quadrotor cascade control system is carried out by using the Lyapunov theorem of stability, and a simulation analysis is carried out using MATLAB to compare the results with those obtained with classical LADRC. The test results indicate that the control strategy of this paper is reasonable for utilization with a quadrotor attitude and displacement control system, allowing the quadrotor to fly on the optimal path with good anti-interference ability and a fast response speed.

Aiming at the problem of maximum power point tracking (MPPT) of photovoltaic arrays in photovoltaic power generation systems, a particle swarm optimization (PSO) MPPT method combined with adaptive linear active disturbance rejection control (A-LADRC) algorithm was proposed and designed. In this method, PSO is used to track the maximum power point (MPP), and then the A-LADRC controller was used to track the reference voltage. The controller enhances the anti-interference ability against various external disturbances in the MPPT process and accelerates the response speed of the system. Compared with the perturbation observation method (P&O), traditional PSO and LADRC, the proposed method has good tracking performance and an anti-interference ability under various external disturbances.