Comparative Analysis of PSO Algorithms for PID Controller Tuning

The active magnetic bearing（AMB）suspends the rotating shaft and maintains it in levitated position by applying controlled electromagnetic forces on the rotor in radial and axial directions.Although the development of various control methods is rapid,PID control strategy is still the most widely used control strategy in many applications,including AMBs.In order to tune PID controller,a particle swarm optimization（PSO）method is applied.Therefore,a comparative analysis of particle swarm optimization（PSO）algorithms is carried out,where two PSO algorithms,namely（1）PSO with linearly decreasing inertia weight（LDW-PSO）,and（2）PSO algorithm with constriction factor approach（CFA-PSO）,are independently tested for different PID structures.The computer simulations are carried out with the aim of minimizing the objective function defined as the integral of time multiplied by the absolute value of error（ITAE）.In order to validate the performance of the analyzed PSO algorithms,one-axis and two-axis radial rotor/active magnetic bearing systems are examined.The results show that PSO algorithms are effective and easily implemented methods,providing stable convergence and good computational efficiency of different PID structures for the rotor/AMB systems.Moreover,the PSO algorithms prove to be easily used for controller tuning in case of both SISO and MIMO system,which consider the system delay and the interference among the horizontal and vertical rotor axes.

Tuning PID Parameters Based on a Combination of the Expert System and the Improved Genetic Algorithms

a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic algorithms, namely rapid convergence and attainment of the global optimum. Utilization of an orthogonal experiment method solves the determination of the genetic factors. Combination with an expert system can make best use of the actual experience of the plant operators. Simulation results of typical process systems examples show a good control performance and robustness.

Robust PID Controller Design on Quantum Fuzzy Inference: Imperfect KB Quantum Self-Organization Effect-Quantum Supremacy Effect

The new method of robust self-organized PID controller design based on a quantum fuzzy inference algorithm is proposed.The structure and mechanism of a quantum PID controller(QPID)based on a quantum decision-making logic by using two K-gains of classical PID(with constant K-gains)controllers are investigated.Computational intelligence toolkit as a soft computing technology in learning situations is applied.Benchmark’s simulation results of intelligent robust control are demonstrated and analyzed.Quantum supremacy demonstrated.

Ziegler-Nichols Customization for Quadrotor Attitude Control under Empty and Full Loading Conditions

An aircraft quadrotor is a complex control system that allows for great flexibility in flight.Controlling multirotor aerial systems such as quadrotors is complex because the variables involved are not always available,known,and accurate.The inclusion of payload changes the dynamic characteristics of the aircraft,making it necessary to adapt the control system for this situation.Among the various control methods that have been investigated,proportional-integralderivative(PID)control offers good results and simplicity of application;however,achieving stability and high performance is challenging,with the most critical task being tuning the controller gains.The Ziegler-Nichols(ZN)theory was used to tune the controller gains for pitch and roll attitude command;however,the performance results were not satisfactory.The response of this system was refined,resulting in an improvement in the reference tracking and the rejection of disturbances.This particular refinement was applied to the quadrotor,and via a reverse calculation,the parameters that allow the tuning of PID gains were obtained,based on ZN.The particularization of the ZN theory applied to a quadrotor with and without a load(termed ZNAQ and ZNAQL,respectively)is proposed and results in a significant improvement in the control system response performance(up to 75%),demonstrating that ZNAQ and ZNAQL are valid for tuning the controller PID gains and are more efficient than the original ZN theory approach.

Autonomous trajectory tracking control method for an agricultural robotic vehicle

To address the nonlinearities and external disturbances in unstructured and complex agricultural environments,this paper investigates an autonomous trajectory tracking control method for agricultural ground vehicles.Firstly,this paper presents the design and implementation of a lightweight,modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster wheels.The vehicle comprises drive wheel modules,passive wheel modules,battery modules,a vehicle frame,a sensor system,and a control system.Secondly,a novel robust trajectory tracking method was proposed,utilizing an improved pure pursuit algorithm.Additionally,an Online Particle Swarm Optimization Continuously Tuned PID(OPSO-CTPID)controller was introduced to dynamically search for optimal control gains for the PID controller.Simulation results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control strategy.To validate the performance,the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural environment.Experimental outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in distance.The proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error(RMSE)of the curve and straight lines,given a suitable set of parameters,meeting the requirements of agricultural applications.The findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots.