Q-Learning-based parameters adaptive algorithm for active disturbance rejection control and its application to ship course control

Active disturbance rejection control for optoelectronic stabilized platform based on adaptive fuzzy sliding mode control

This paper proposes a method that combines active disturbance rejection control (ADRC) and adaptive fuzzy sliding mode control (AFSMC), which is beneficial for the optoelectronic platform to enhance the target tracking capability. A servo system model based on the LuGre friction is first established. The AFSMC controller estimates the unknown part of the platform, and the fuzzy approximator can reduce chattering. Then, an ADRC controller based on Back-Propagation neural network tuning is designed and compared with the empirical method, which makes the system's tracking accuracy higher. Lyapunov's theorem and Barbara's lemma are forceful methods to prove engineering stability. Simulations illustrate that the influence of external disturbances on the optoelectronic platform can be suppressed, thereby enhancing the disturbance isolation of the controller.

Fractional-Order PID Controllers for Temperature Control: A Review

Fractional-order proportional integral derivative (FOPID) controllers are becoming increasingly popular for various industrial applications due to the advantages they can offer. Among these applications, heating and temperature control systems are receiving significant attention, applying FOPID controllers to achieve better performance and robustness, more stability and flexibility, and faster response. Moreover, with several advantages of using FOPID controllers, the improvement in heating systems and temperature control systems is exceptional. Heating systems are characterized by external disturbance, model uncertainty, non-linearity, and control inaccuracy, which directly affect performance. Temperature control systems are used in industry, households, and many types of equipment. In this paper, fractional-order proportional integral derivative controllers are discussed in the context of controlling the temperature in ambulances, induction heating systems, control of bioreactors, and the improvement achieved by temperature control systems. Moreover, a comparison of conventional and FOPID controllers is also highlighted to show the improvement in production, quality, and accuracy that can be achieved by using such controllers. A composite analysis of the use of such controllers, especially for temperature control systems, is presented. In addition, some hidden and unhighlighted points concerning FOPID controllers are investigated thoroughly, including the most relevant publications.

Reinforcement learning-based optimization of locomotion controller using multiple coupled CPG oscillators for elongated undulating fin propulsion

This article proposes a locomotion controller inspired by black Knifefish for undulating elongated fin robot. The proposed controller is built by a modified CPG network using sixteen coupled Hopf oscillators with the feedback of the angle of each fin-ray. The convergence rate of the modified CPG network is optimized by a reinforcement learning algorithm. By employing the proposed controller, the undulating elongated fin robot can realize swimming pattern transformations naturally. Additionally, the proposed controller enables the configuration of the swimming pattern parameters known as the amplitude envelope, the oscillatory frequency to perform various swimming patterns. The implementation processing of the reinforcement learning-based optimization is discussed. The simulation and experimental results show the capability and effectiveness of the proposed controller through the performance of several swimming patterns in the varying oscillatory frequency and the amplitude envelope of each fin-ray.

Constrained Path Planning for Unmanned Aerial Vehicle in 3D Terrain Using Modified Multi-Objective Particle Swarm Optimization

This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness.

Adaptive Fuzzy Active-Disturbance Rejection Control-Based Reconfiguration Controller Design for Aircraft Anti-Skid Braking System

The aircraft anti-skid braking system (AABS) is an essential aero electromechanical system to ensure safe take-off, landing, and taxiing of aircraft. In addition to the strong nonlinearity, strong coupling, and time-varying parameters in aircraft dynamics, the faults of actuators, sensors, and other components can also seriously affect the safety and reliability of AABS. In this paper, a reconfiguration controller-based adaptive fuzzy active-disturbance rejection control (AFADRC) is proposed for AABS to meet increased performance demands in fault-perturbed conditions as well as those concerning reliability and safety requirements. The developed controller takes component faults, external disturbance, and measurement noise as the total perturbations, which are estimated by an adaptive extended state observer (AESO). The nonlinear state error feedback (NLSEF) combined with fuzzy logic can compensate for the adverse effects and ensure that the faulty AABS maintains acceptable performance. Numerical simulations are carried out in different runway environments. The results validate the robustness and reconfiguration control capability of the proposed method, which improves AABS safety as well as braking efficiency.

Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic

To ensure the safe operation of an interconnected power system, it is necessary to maintain the stability of the frequency and the tie-line exchanged power. This is one of the hottest issues in the power system field and is usually called load frequency control. To overcome the influences of load disturbances on multi-source power systems containing thermal power plants, hydropower plants, and gas turbine plants, we design a linear active disturbance rejection control (LADRC) based on the tie-line bias control mode. For LADRC, the parameter selection of the controller directly affects the response performance of the entire system, and it is usually not feasible to manually adjust parameters. Therefore, to obtain the optimal controller parameters, we use the Soft Actor-Critic algorithm in reinforcement learning to obtain the controller parameters in real time, and we design the reward function according to the needs of the power system. We carry out simulation experiments to verify the effectiveness of the proposed method. Compared with the results of other proportional–integral–derivative control techniques using optimization algorithms and LADRC with constant parameters, the proposed method shows significant advantages in terms of overshoot, undershoot, and settling time. In addition, by adding different disturbances to different areas of the multi-source power system, we demonstrate the robustness of the proposed control strategy.