Snake Optimization Algorithm for Fractional Order Controller in Three-Area Load Frequency Regulation

Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

The cart‐inverted pendulum is a non‐minimum phase system having right half s‐plane pole and zero in close vicinity to each other. Linear time invariant (LTI) classical controllers cannot achieve satisfactory loop robustness for such systems. Therefore, in the present work the fractional order PIλDμ (FOPID) controller is addressed for robust stabilization of the system, since fractional order controller design allows more degrees of freedom compared to its integer order counterparts by virtue of its two parameters λ and μ. The controller parameters are tuned by three evolutionary optimization techniques. In order to select the controller parameters optimally, a novel non‐linear fitness function using integral time square error (ITSE), settling‐time, and rise time is proposed here. The control algorithm is implemented successfully in real‐time. Moreover, stability analysis of the system compensated with a fractional order controller is presented using Riemann surface. Robustness of the physical cart‐inverted pendulum system towards multiplicative gain variations and plant parameter variations is verified. In this regard, it is shown that the fractional order controller provides satisfactory robust performance in both simulation and real‐time system.

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.

Performance comparison of wind turbine based doubly fed induction generator system using fault tolerant fractional and integer order controllers

This paper proposes a collaborative control system to be designed for multi-UAV. This makes it easy to perform many tasks at the same time and with high accuracy. Therefore, this cooperative control and guidance subsystems of the aircraft should have robust performance against sensors noise and wind disturbances. Four types of control algorithms were designed for a single Aerosonde UAV autopilot. This is to pick up which control algorithm is the best. As such, this control algorithm is proposed to be designed for the cooperative flight control system. Two classical control algorithms and two intelligent control algorithms have been proposed for the autopilot design of a single Aerosonde UAV. The first classical controller proposed is genetically tuned PID, while the second classical controller proposed is the fractional order PID. The first intelligent controller proposed for autopilot system is the Fuzzy logic controller known as FLC, while the second intelligent controller proposed is the adaptive neuro fuzzy inference system known as ANFIS. The proposed control algorithms have been applied to the nonlinear multivariable system of Aerosonde UAV. The analysis of simulation results assure that ANFIS is the best performance and the most robust control algorithm proposed. As such, ANFIS controller has been selected to be the cooperative flight controller system either in the low-level of a single UAV and in the top-level of multi-UAVs. Sometimes, classical controllers are preferred because of their simplicity in design. If this is the case, the simulation results assure that the genetically tuned fractional order PID controller- which proposed here for the first time with UAVs- is better than genetically tuned PID.

A new active control performance index for vibration control of three-dimensional structures

The frequency regulation has become one of the major subjects in microgrid power system due to the complexity structure of microgrid. In order to solve this problem, this paper proposes an improved linear active disturbance rejection control algorithm (ILADRC) that can significantly improve system performances through changing feedback control law to reduce the disturbance estimation error of extended state observer. Then, the proposed algorithm is employed in microgrid power system frequency regulation problem, which demonstrates its effectiveness. The parameters of controllers are optimized by particle swarm optimization (PSO) algorithm improved by genetic algorithm (GA). Simulations with different disturbances including sudden and stochastic change of load demand and wind turbine generation are carried out in comparison with previous studies. And robustness testing based on Monte-Carlo approach also shows better performance. So frequency stability of microgrid power system can be well guaranteed by proposed control algorithm.

This paper presents an algorithm for load frequency control of multi-area power system. A more realistic models is used to represent the elements of power system. The frequency bias factor (B) is set optimally. The effect of generation rate constraints (GRC) and the dead band characteristics of the governor is taken into consideration. A simultaneous optimization of the fractional order (PIλ Dµ) control parameters, the speed regulation (R) of the governor and the frequency bias factor (B) is proposed for the first time. The objective function to be minimizes is a modified integral time absolute error (MITAE). The tuning of the control parameters is based on using cuckoo search algorithm (CSA) to minimize the objective function. The proposed algorithm is applied to a typical interconnected three-area power systems with a different levels of load step change. Results of simulation of the presented method are compared with those obtained by applying particle swarm optimization technique. The results obtained reveal the robustness of the suggested algorithm in terms of settling time and peak over shoot.

In order to improve the lateral stability of in-wheel motors electric vehicles (IMEV) equipped with mechanical elastic electric wheels (MEEW) under extreme conditions, this paper proposes an integral sliding mode control (ISMC) algorithm based on the coordination of active front steering (AFS) and direct yaw moment control (DYC) considering the roll effects and the uncertainty of tire cornering stiffness. Firstly, the AFS control algorithm based on adaptive integral terminal sliding mode is proposed, which uses radial-basis-function neural network (RBFNN) to adaptively approximate the integrated nonlinear unknown disturbance. Secondly, considering the roll effects and uncertain cornering stiffness, a mismatched uncertain system for integrated control of AFS and DYC is established. An integral sliding mode control algorithm based on linear matrix inequality (LMI) is designed, and the asymptotic stability of sliding mode dynamics is proved. Further, a constraint optimization algorithm is used to distribute the additional yaw moment. Then, a coordinated control strategy based on the elliptical stable region of the phase plane and the rollover index is designed to activate the integrated controller of AFS and DYC at the right time. Finally, the effectiveness of the control algorithm is verified by high-fidelity CarSim-Matlab simulations. The results show that the proposed controller can effectively and robustly ensure the vehicle lateral stability under extreme conditions.

This paper focuses on the re-entry phase of lunar return spacecraft and addresses the design optimization of their re-entry trajectories in real-world conditions. Considering various constraints of re-entry flights, this study introduces a refined superior control theory, drawing from Xuesen Qian’s descriptions in engineering control theory, and presents a specific superior control algorithm. The designed superior control algorithm and the traditional weighted optimal control algorithm were employed to simulate the lunar return and re-entry processes. Two representative trajectories were selected for a comparative analysis to obtain various parameters. Results indicate that the trajectory optimized using the weighted optimal control algorithm can only ensure that multiple performance indexes are optimized according to preset weights but cannot achieve superior performance in all metrics. In contrast, trajectories optimized using the superior control algorithm effectively leverage the permissible floating range of performance indexes without exceeding the maximum limit, thereby ensuring superior performance in all metrics. This paper is the first to refine the superior control theory proposed by Xuesen Qian, to design a specific algorithm theory for superior control, and to apply it to aerospace re-entry trajectory optimization—providing a theoretical foundation for future non-weighted control algorithm developments.

The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified.

Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems

This paper presents a coordinated control algorithm for comprehensive optimization of the vehicle dynamics performance and the energy consumption for a full drive-by-wire electric vehicle, which is driven by a four-in-wheel-motor actuated system and steered by a steer-by-wire system. In order to coordinate the four-in-wheel-motor actuated system and the steer-by-wire system, first, the mechanisms influencing the vehicle dynamics control performance and the energy consumption of the two systems are derived on the basis of quantitative analyses of a typical vehicle motion control process and, then, the control algorithms for each subsystem are developed. For the steer-by-wire system, a triple-step control technique is implemented to decouple the yaw rate and the side-slip angle controls, which makes it easier to tune the control parameters. The control algorithm of the four-in-wheel-motor actuated system is designed with a hierarchical control scheme, which is able not only to satisfy the yaw rate and side-slip angle tracking demands but also to deal with the actuation redundancy and the constraints. Finally, based on the mechanisms influencing the control performances of the two subsystems, coordinated control is proposed to obtain comprehensive optimization of the vehicle dynamics control performance and the energy consumption. The coordinated control developed is convenient for implementation as the structures and the control algorithms of the subsystems remain unchanged. In this way, the control algorithms of the subsystems can be developed independently. Simulations are carried out with a CarSim software full-vehicle model for three typical driving scenarios and with different road conditions. The results show the effectiveness of the coordinated control algorithm developed.

Experiments have shown the reward adaptive-optics provides in improving the resolution of ground-based astronomical telescopes [1,2,3]. A critical contributor to adaptive-optics system performance is the control algorithm that converts wavefront sensor (WFS) measurements into the deformable mirror (DM) actuator commands. For the adaptive-optics systems in use today this control algorithm consists of a wavefront reconstruction step to estimate the instantaneous phase distortion to be compensated [4], followed by a servo control law to temporally filter this instantaneous estimate before it is applied to the deformable mirror [5]. So-called modal adaptive-optics systems can apply different temporal filters to separate spatial components, or modes, of the overall phase distortion [6]. Extensive analysis has been performed to evaluate and optimize the performance of these adaptive-optics control systems [7,8,9,10,11], but the results obtained depend on atmospheric parameters which are seldom known exactly and are constantly fluctuating. The uncertainty and variability of atmospheric conditions implies that an optimal degree of turbulence compensation cannot be achieved or maintained for long time intervals with a fixed control algorithm. A need exists for methods to update adaptive-optics control algorithms based upon actual system performance. Encouraging results have already been obtained demonstrating the value of emperically optimizing the control bandwidths for a modal adaptive-optics system [12]. In comparison, the subject of real-time adjustments to reconstruction matrices on the basis of measured system performance has received little attention.

A pressurized heavy water reactor (PHWR) is a nuclear power reactor,that commonly uses unenriched natural uranium as its fuel and heavy water as its coolant and moderator. The PHWR has become an important component of the present day power generation industry. Working of a PHWR may often require reduction of nuclear power within a small finite time interval in accordance to the varying load conditions. This phenomenon is referred to as step back condition. Therefore, for the efficient working of the PHWR it is very important to have a control algorithm that has the capability to handle the step back condition very precisely. The fractional order control strategy has emerged as one of the very versatile and accurate control strategies in the recent times. The fractional order PID (FOPID) controllers are known to give very robust performance both in nominal as well as varying operating conditions. However, most of the design techniques available for the FOPID controllers are mathematically very lengthy and complex. Therefore, in this paper an FOPID controller design technique has been suggested using the concept of internal model control (IMCFOPID) for the PHWR under step back conditions. Further, the performance of the proposed controller has been compared with the conventional FOPID controller using the various integral error criterions. The proposed IMC-FOPID controller is found to give an improved performance as compared to the conventional FOPID controller.