Quadrotor Design Procedure and PID Control for Outdoor Free Flight

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

The design and implementation of a multi-stage PID (MS-PID) controller for non-inertial referenced UAVs are highly complex. Symmetrical multirotor UAVs are unstable systems, and it is thought that the kinematics of the symmetrical UAV rotor, such as the quadrotor and hexacopter resembles the kinematics of an inverted pendulum. Several researchers have investigated the structure and design of PID controllers for high-order systems during the last decade. The designs were always concerned with the enhanced response, robustness, model reduction and performance of PID controllers. An accurate tuning process of such a controller depends on the engineer’s experience level. This is due to the number of variables and hyperparameters tuned during the process. An adaptive genetic algorithm (AGA) is utilized to optimize the MS-PID controllers for controlling the quadrotor in this study. The proposed method optimizes the offline-planned approach, providing several possibilities for adapting the controllers with various paths and or varying weather conditions. The MS-PID parameters are optimized in parallel, as every PID controller affects the other controller’s behavior and performance. Furthermore, the proposed AGA generates new chromosomes for “new solutions” by randomly developing new solutions close to the previous best values, which will prevent any local minima solution. This study intends to investigate the design and development of a highly tuned robust multi-stage PID controller for a symmetrical multirotor UAV. The work presents a model for a non-referenced inertial frame multirotor UAV (quadcopter). Once the model is defined, a robust multi-stage PID controller for the non-inertial referenced frame symmetrical multirotor UAV is designed, tuned, and tested. A genetic algorithm (GA) will be used to tune the MS-PID controller. Finally, the performance comparison between the proposed and conventional methods is presented. The results show that the proposed method provides stability improvement, better transient response, and power consumption.

Model-based synthesis of nonlinear PI and PID controllers

Surface mobility control is a fundamental challenge in robotics, and its applications range from autonomous vehicles to industrial automation. The main aim of the paper is to analyze the PID controller parameters for the speed control of a surface mobility platform mobile robot. MATLAB simulation toolbox and mathematical model of the DC motor are used to find the optimal PID controller parameters to complete the DC motor speed control system. For the SMP motion control system, the mobile robot kinematic model is supported as a differential drive system. In this paper, the PID controller parameters for the DC motor control system are analyzed with simulation results using various KP, KI, KD gains. The experimental results of the SMP mobile robot with optimal PID controller parameters are analyzed by driving on the rough, slope and even terrains. According to simulation results and experimental tests, the proposed system effectively controls the speed of the SMP mobile robot, demonstrating satisfactory performance in settling time, rise time, and system overshoot. Among PI and PID controllers, the designed PID controller can quickly control to reach the desired DC motor speed. Using the optimal PID coefficient parameters, all motors reached their desired speed in 0.12 seconds during simulations. In the experimental tests on various terrain (even, slope and rough), all motors achieved their desired speed simultaneously within 25 seconds.Keywords: Differential drive, PID controller, motion trajectory, real-time speed control. skid steering, SMP mobile robot, surface mobility control

This paper presents a method for PID controller design which can achieve dominant pole assignment using two of the controller parameters. The non-dominant poles are restricted on the left of the line s = σ̂, where σ̂ is the minimum feasible value, called as the feasibility border. It is obvious that a dominant pole assignment is not practical if σ̂ is close to the real parts of the required dominant poles. Hence, finding σ̂ for a given system is very important. The method, which parameterizes all such controllers in order to allow further design criteria, can be applied to other kinds of low-order compensators.

Heat exchangers are expedients used to transfer of heat energy from one medium to other while preventing the two from mixing. Heat exchangers are hired in a selection of domestic, commercial, and industrial applications, such as air conditioning, power production, refrigeration, process industry, and manufacturing industry. A larger variation in the temperature affects property of non-Newtonian fluids, thus affects the uniform heat transfer process. This paper is primarily to deal the response analysis of different controllers that is regulating the outlet temperature of fluid inside the PHE is proposed. The cold fluid flow is highly influenced on the hot water temperature, the research work mainly focuses on controlling cold fluid flow. In order to attain the precise model of the system, a mathematical model by first principle methods and process history based methods were used. A PI, PID and Model Predictive Controller (MPC) are known for its better control action of the system. The controller’s performance analysed such as PI, PID and MPC controller to adjust the temperature of hot fluid outlet to a required setpoint. Performance criteria analysis value for various controllers are PI, PID and MPC. It is understood that MPC controller results lower value of performance measures compared to other controllers, this implies that it could track the setpoint by rejecting the external disturbances with less settling time and moderate oscillation. The transient performance and the error criteria of the controllers are investigated and the best controller to regulate the temperature is found out. It is found out that the MPC controller outperforms PI and PID controller from the results of simulation.

Predictive PID Control for Ship Autopilot Design

Adaptive Design of PID Controllers Based on an Alternative Method to Root Locus ⋆

Researching optimization problems of controller is need of industrial process control ,in which PID controller is widely used,which its parameters can be equivalent to optimization problems. In the industrial control the PID controller is widely used in excitation control system to improve its control performance.In order to find the optimal PID controller parameters effectively ,a kind of Adaptive Particle Swarm Optimization method (CAPSO) based on Cloud Theory is applied to fuzzy PID controller. Through the establishment of particle swarm algorithm of fuzzy PID controller parameters optimization model, which it can be used to optimize the membership function of fuzzy PID controller. Particle code adopts real coding. Particle dimension is related with the number of the input variables divided by fuzzy set and the number of control rules of whole fuzzy control system, thus the parameters of PID control are optimized on real time. The result from the simulation shows that compared with PID control and fuzzy control this system has several advantages which are small overshoots ,fast response ,and good stable performance to improve the control performance of excitation control system.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

Robust Design of PID and IMC-Based Controllers in the Time Domain

This paper compares the performance of four different controllers implemented on two multi agent robots to stabilize its motion from one station to another during delivery tasks. The controllers are; multi-position controller, PID controller, fuzzy logic controller and fuzzy-PID controller. The aim of this paper is to control the mobile robot robustly to arrive its target destination. The robots and station coordinates are recognized using machine vision system and all programming is carried out in LabVIEW. The paper compares the transient response and steady state error of each of controller and experimental results show that the Fuzzy-PID controller produced the best performance and good trajectory of robot from its current position to its target position. It had a better convergence rate when compared with other controllers like PID and Fuzzy logic controllers.

ABSTRACTIn this article, we propose practical rules for tuning event-based PID controllers with two sampling strategies: symmetric send-on-delta (SSOD) and regular quantification (RQ). We present a detailed analysis about the effect of the derivative term of the controller when using SSOD or RQ and some guide lines are given to select the derivative filter coefficient. The two sampling strategies are compared, showing that, even when both of them lead to similar controlled output response, systems with RQ have better robustness properties than those with SSOD. The study is based on the describing function and the results are applicable to process with dynamic responses of different types: with time delays, non-minimum phase, under-damped response, etc. The rules presented here are given in terms of phase and gain margins that are measures of robustness used in the design of continuous PID controllers. This allows the application of conventional PID tuning methods to the case of event-based PID. The tuning rules are very simple and can be used for tuning PID, PI, PD and other controller structures.

The main objective of the paper is to design of Fractional order PID (FOPID) controller for heat flow system using evolutionary algorithms. The recent developed metaheuristic algorithms such as Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO) and Moth Flame Optimizer (MFO) are used in the FOPID design. The FOPID design for heat flow system is formulated as an optimization problem to minimize different indices error such as Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral Time Absolute Error (ITAE), Integral Time Squared Error (ITSE). The evolutionary algorithm based FOPID control performances are compared with PID control performance for the heat flow system. In addition, the proposed methods have superiority value in terms of transient and frequency domain analysis than the traditional and PID methods. Comparative performance evaluation of meta-heuristic based FOPID design for heat flow system has not carried out before.

Quantitative feedback theory (QFT) is a frequency domain based robust control design techniques [1]. In this paper, the piecewise linear approximation based linear programming (PLA-LP) algorithms presented in [6] are extended for automatic synthesis of robust PID controller in the QFT framework. Firstly, the λ-formulations for the parallel or interacting form of the PID controller are derived. Then, the λ-formulations are incorporated into the QFT synthesis algorithm proposed in [6], and solved to global optimality using a mixed-integer programming (MIP) branch and bound solver. The proposed robust QFT-PID synthesis algorithm is demonstrated on two benchmark examples. Compared to existing methods, the PLA-LP based QFT-PID algorithm is faster by a couple of orders of magnitude, and also gives considerable reductions in the controller gain.