Quadrotor tuning for attitude control based on double-loop PID controller using fictitious reference iterative tuning (FRIT)

Design of a Data-Driven PID Controller using Operating Data

In this paper, we propose an off-line tuning method of the vibration suppression controller gains for 2-inertia system using Fictitious Reference Iterative Tuning (FRIT). FRIT is one of data-driven design methods using only one-shot experimental input-output data without model parameters of the controlled object. We use the modified-IPD speed controller which consists of the 2-degree of freedom PID control, considering response speed, and in addition, we use the first lag element in order to increase degree of freedom of the control design. Then, we consider the current loop for the high speed torque control. In this paper, a PI controller is used for the current control. Here, in this paper, we use the particle swarm optimization as the optimization searching method, and the performance index is the IAE. So, we confirmed the effectiveness of the proposed method which used only one-shot experiment result by experimental setup.

A new method of controller parameter tuning based on input-output data – Fictitious Reference Iterative Tuning (FRIT) –

A Direct PID Gain Tuning Based on FRIT Method Using Optimal Filter in the Frequency Domain

This paper presents a new strategy for online robust control-based fractional order PID controller (FPID). The technique suggests an online parameters tuning of the FPID using fictitious reference iterative tuning (FRIT) approach. This approach enables us to track the desired optimal FPID parameters online using experimental data without any plant model identification. After having set the optimal non-integer orders α and β, the gains of the FPID are then estimated in a recursive manner using the so-called recursive least squares (RLSs) algorithm with forgetting factor, which can cope with variation of plant characteristics adaptively. The performances of this technique are compared with other results already existing in the literature through some typical examples; the results of the proposed approach show significant performance improvement.

This paper presents a new control strategy which unifies the direct and indirect multi-scale control schemes via a double-loop control structure. This unified control strategy is proposed for controlling a class of highly nonminimum-phase processes having both integrating and unstable modes. This type of systems is often encountered in fed-batch fermentation processes which are very difficult to stabilize via most of the existing well-established control strategies. A systematic design procedure is provided where its applicability is demonstrated via a numerical example.

Reinforcement learning of ball screw feed drive controllers

To maintain high performance of controlling and minimize the total cost for readjustments of the controller parameters, determination the appropriate timing for readjustment the controller parameters is important. This paper proposes a design of performance-driven control system to maintain the system performance by detecting the system change using the recurrent neural networks and readjusting the control parameters based on Fictitious Reference Iterative Tuning (FRIT). This paper conducts some numerical experiments to verify the availability of the proposed method to some systems.

This paper proposed a single-loop Model Predictive Control (MPC) strategy for the Grid-side converter (GSC), which reduces the difficulty of controller design and improves the system transient performance. In field oriented control, double-loop cascade control structure is widely adopted by GSC system, but this control structure has some disadvantages, such as design and matching difficulty of inner and outer loop parameters, and controller delay. Therefore, there aspects are improved in this paper. First, a single-loop strategy is proposed for GSC to simplify the traditional cascade control structure and the system dynamic response capability is enhanced; Second, the integral discretization method is adopted to solve the predictive problem of second-order system, which reduces the predictive complexity; Third, to prevent over current cases, the current limit function is taken as a nonlinear additional term of cost function. Finally, the feasibility and effectiveness of the proposed control strategy are verified by simulation experiments.

Robust oxygen excess ratio control of PEMFC systems using adaptive dynamic programming

Among incidents on grids, the sag/swell voltage is considered as the most frequent incident. To solve this problem, custom power devices are used. In particular, the dynamic voltage restorer (DVR) is a modern and efficient customer device. DVRs are used to mitigate voltage sag/swells and harmonics on the load bus, thus protecting the sensitive loads. The DVR is a serial compensator that applies a voltage to the point of common coupling to maintain the voltage of sensitive load at the nominal value. To improve the performance of DVRs, in this study, the control strategy of the two-stage loop circuit is implemented. The external voltage control loop uses a sequence-decoupled resonant (SDR) controller, and the inner current-control loop uses the proportional resonant (PR) controller implemented in the stationary frame αβ.

This paper addresses the 3D trajectory tracking problem of a novel underactuated underwater vehicle with 4 propellers. A 5-DOF kinematic and dynamic model of the quadrotor underwater vehicle (QUV) is established based on the underwater vehicle and quadrotor unmanned aerial vehicle movement mechanism. A double-loop control structure is then developed. By constructing a Lyapunov function for the outer-loop controller, a 5-DOF trajectory tracking error equation and a velocity virtual control law are obtained. The inner-loop controller is built based on nonsingular integral terminal sliding mode control (NITSMC). Finally, the effectiveness and robustness of our control algorithm are demonstrated through numerical simulations. In particular, given a smooth, second-order derivable 3D reference trajectory, the QUV can quickly track the trajectory and satisfactorily converges in the neighborhood of the expected value in a finite time via NTISMC, which verifies its superiority compared with the other popular backstepping control (BSC) method.

Through LFC tie-line interchange power of the area within prescribed limit and scheduled frequency of the system can maintain constant. Generally, Integral (conventional) Controllers are used in Load Frequency Controllers but they are quite slow. In this paper, we have used the PID controller for two-area power system in place of conventional controller and by using various adaption techniques we are tuning controllers. In order to verify the performance of PID and conventional controller, comparative results of both controllers are also presented. Overshoot and settling time are better with PID controller. The result demonstrates the effectiveness of PID controller over conventional controller. Different techniques like Ziegler Nichols and Nonlinear Control Design technique are used for comparing the responses of conventional controllers with PID controllers.

Generally Quad-rotor type Unmanned Aerial Vehicles are unstable in nature, so to stabilize it, attitude controller is used. This paper presents a Fuzzy Logic based PID control system (FPID) for attitude stabilization of Quad-rotor Unmanned Aerial Vehicle (UAV). The Fuzzy logic controller updates the PID controller gains efficiently in such a way that it efficiently stabilizes the attitude of quad-rotor UAV. This method enhances the capabilities of conventional PID Controller to a dynamic PID Controller. The propose FPID control technique for attitude controlling of Quad-rotor UAV was tested on developed prototype of Quad rotor UAV for experimental purpose. Experimental results proved that proposed Fuzzy Logic based PID (FPID) controller quickly stabilized Quad-rotor UAV and its response time and settling time is reasonably good in attitude stabilization control application.

A double-loop control system based on human-simulated intelligent control (HSIC) is designed using a mathematical model of a brushless DC motor (BLDCM). In the double-loop motor control system, a conventional PI controller is implemented for the current loop and an HSIC speed controller using the characteristic model-based multi-mode control is adopted for the speed loop. The controller parameters are set and optimized using an improved genetic algorithm (IGA). Compared with the double-loop motor subject to adaptive PID control, fuzzy control, neural-network control and conventional PID control, simulation results show the double-loop motor control system of the HSIC-based BLDCM has high dynamic and static performances as well as high adaptability and robustness.