Fractional Order PIλDμ Control Research Articles

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

The suspension system is a crucial part of an electric vehicle, which directly affects its handling performance, driving comfort, and driving safety. The dynamics of the 8-DoF full-vehicle suspension with seat active control are established based on rigid-body dynamics, and the time-domain stochastic excitation model of four tires is constructed by the filtered white noise method. The suspension dynamics model and road surface model are constructed on the Matlab/Simulink simulation software platform, and the simulation study of the dynamic characteristics of active suspension based on the fractional-order PIλDμ control strategy is carried out. The three performance indicators of acceleration, suspension dynamic deflection, and tire dynamic displacement are selected to construct the fitness function of the genetic algorithm, and the structural parameters of the fractional-order PIλDμ controller are optimized using the genetic algorithm. The control effect of the optimized fractional-order PIλDμ controller based on the genetic algorithm is analyzed by comparing the integer-order PID control suspension and passive suspension. The simulation results show that for optimized fractional-order PID control suspension, compared with passive suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions reaches over 25%, the average optimization of suspension dynamic deflection exceeds 30%, and the average optimization of tire dynamic displacement is 5%. However, compared to the integer-order PID control suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions decreased by 5%, the average optimization of suspension dynamic deflection increased by 3%, and the average optimization of tire dynamic displacement increased by 2%.

A Sine Cosine Algorithm-Based Fractional MPPT for Thermoelectric Generation System

Thermoelectric generators (TEGs) are equipment for transforming thermal power into electricity via the Seebeck effect. These modules have gained increasing interest in research fields related to sustainable energy. The harvested energy is mostly reliant on the differential temperature between the hot and cold areas of the TEGs. Hence, a reliable maximum power point tracker is necessary to operate TEGs too close to their maximum power point (MPP) under an operational and climate variation. In this paper, an optimized fractional incremental resistance tracker (OF-INRT) is suggested to enhance the output performance of a TEG. The introduced tracker is based on the fractional-order PIλDμ control concepts. The optimal parameters of the OF-INRT are determined using a population-based sine cosine algorithm (SCA). To confirm the optimality of the introduced SCA, experiments were conducted and the results compared with those of particle swarm optimization (PSO) and whale optimization algorithm (WOA) based techniques. The key goal of the suggested OF-INRT is to overcome the two main issues in conventional trackers, i.e., the slow dynamics of traditional incremental resistance trackers (INRT) and the high steady-state fluctuation around the MPP in the prevalent perturb and observe trackers (POTs). The main findings prove the superiority of the OF-INRT in comparison with the INRT and POT, for both dynamic and steady-state responses.

WITHDRAWN – Administrative Duplicate Publication: The study of a unified driver model controller based on fractional-order PIλDμ and internal model control

Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.

WITHDRAWN – Administrative Duplicate Publication: The study of a unified driver model controller based on fractional-order PIλDμ and internal model control

Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.

The study of a unified driver model controller based on fractional-order PIλDμ and internal model control

Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.

Precision Displacement Control of a Diamond-shaped Amplifying Mechanism Driven by Piezoelectric Actuator Based on Fuzzy Fractionalorder PIλDμ Controller

Background: As a high-performance functional material, stacked piezoelectric actuator can produce a displacement under the effect of changing voltage. Its advantages of fast response and easy operation make it to be widely applied in the precision structure field. However, its small displacement stroke and hysteresis nonlinearity affect the accuracy of the output. : In the next step, some experiments were undertaken based on the constructed platform. Methods: In order to enlarge the displacement of piezoelectric actuator and reduce the influence of hysteresis, this study designs a diamond-shaped amplifying mechanism to amplify the output of the piezoelectric actuator, and then develops a self-tuning fuzzy fractional-order PIλDμ controller for the high precision displacement control of the proposed amplifying mechanism. After analyzing the working principle and modeling the amplifying mechanism, the fractional-order PIλDμ control model of the proposed mechanism was built and discretized according to the theoretical base of the fractional calculus in the time domain. Moreover, the fuzzy control algorithm was also introduced to achieve self-turning of parameters. Besides, the amplifying mechanism was also adopted for a microdroplet jetting dispenser to verify the practicability of the mechanism and control strategy. In the next step, some experiments were undertaken based on the constructed platform. Results: Experiments show that the displacement overshoots, the times of reaching a steady state of the traditional integer-order controller and the fractional-order controller are 5.08%, 1.17% and 17.25 s, 12.00 s, respectively. However, the fuzzy PIλDμ controller lowers the overshoot and the time of reaching a steady state to 0.95% and 9.00 s, respectively. The control algorithm can not only improve the follow-ability of the output displacement of the proposed mechanism, but also maintain the deviation within the range of 0.4% after the displacement stroke is stable and reduce the entering time of the mechanism up to 47.8%. In actual application, the droplet volume of micro-droplet jetting dispenser under fuzzy fractional-order PID control method is more stable, and its repeatability accuracy can reach up to 1.6475%. Conclusion: Experimental results indicate that the self-tuning fuzzy fractional-order PIλDμ controller can significantly improve the tracking performances of the PID and the integer-order PID with regard to the amplifying mechanism with the advantages of good dynamic character and regulation precision. Furthermore, the diamond-shaped amplification mechanism and control strategy can be applied for some micro-droplet jetting dispensers used in microelectronic packaging, life science and 3D printing fields.

Fractional Order PIλDµ Control Design for a Class of Cyber-Physical Systems with Fractional Order Time-Delay models

The control of cyber-physical systems (CPS) is a great challenge for researchers in control theory and engineering mainly because of delays induced by merging computation, communication, and control of physical processes. Consequently, control solutions for time-delay systems can be applied efficiently for many CPS system configurations. In this article, a fractional order PIλ and PIλDµ control design is investigated for a class of fractional order time-delay systems. The proposed control design approach is simple and efficient. The controller parameter's adjustment is achieved in two steps: first, the relay approach is used to compute satisfactory classical PID coefficients, namely kp, Ti and Td. Then, the fractional orders λ and µ are optimized using performance criteria. Simulation results show the efficiency of the proposed design technique and its ability to enhance the PID control performance.

Adaptive Fractional Order PIλDμ Control for Multi-Configuration Tank Process (MCTP) Toolbox

Adaptive Fractional Order PIλDμ Control for Multi-Configuration Tank Process (MCTP) Toolbox

Dynamic Modeling and Fractional Order PIλDμ Control of PEM Fuel Cell

Dynamic Modeling and Fractional Order PIλDμ Control of PEM Fuel Cell

Particle Swarm Optimization of PI<sup>λ</sup>D<sup>μ</sup> Control of Heating Furnace Temperature Control System

Due to the complexity of the heat transfer for heating furnace, some characteristics are caused such as big inertia, great lag. In the temperature control system for heating furnace, the traditional PID controller can not get satisfactory effect, that dynamic is instability and control accuracy is bad, which is very detrimental to the system to achieve optimum efficiency. A fractional order PIλDμ controller based on particle swarm optimization method was designed, at the same time compared with PID control. Simulation results show that, fractional order PIλDμ control based on particle swarm optimization has better convergence stability, faster response times and higher accuracy value. Fractional order PIλDμ controller has better dynamic performance, compared with traditional PID controller, greatly improves the quality control system.