Nonlinear PID Controller Design for a 6-DOF UAV Quadrotor System

A Nonlinear PID (NLPID) controller is proposed to stabilize the translational and rotational motion of a 6-DOF UAV quadrotor system and enforce it to track a given trajectory with minimum energy and error. The complete nonlinear model of the 6-DOF quadrotor system are obtained using Euler-Newton formalism and used in the design process, taking into account the velocity and acceleration vectors resulting in a more accurate 6-DOF quadrotor model and closer to the actual system. Six NLPID controllers are designed, each for Roll, Pitch, Yaw, Altitude, and the Position subsystems, where their parameters are tuned using GA to minimize a multi-objective Output Performance Index (OPI). The stability of the 6-DOF UAV subsystems has been analyzed in the sense of Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers. The simulations have been accomplished under MATLAB/SIMULINK environment and included three different trajectories, i.e., circular, helical, and square. The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the Linear PID (LPID) one and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady-state error.

In this paper, we compare two control techniques for a DJI F450 quadcopter which is controlled and stabilized by a non-rooted onboard Android smartphone, without the aid of an external IMU. Specifically, we compare an advanced modelfree PID and LQI controller. Since Android is not a realtime system, the control commands and sensor measurements are subject to significant latencies, and hence the PID controller is modified to account for non-trivial measurement asynchronicities. We also show that some features can be added to the widely used baseline PID to obtain better performance in the presence of latencies and noise. Finally, we introduce a LQI controller which is capable of satisfactorily tracking a reference command in the presence of errors, noise, and model uncertainties, and compare the results to the PID controller.

A comparison between advanced model-free PID and model-based LQI attitude control of a quadcopter using asynchronous android flight data

Space robots control and the advantages of controllers for these robots are active research topics. In this paper, a variety of control methods were used to control the space robots attitude to obtain time response in order to minimize the EULERINT criterion. EULERINT is in fact the time integral of the Euler angel between the body axis and the target axis over the maneuvers and thus is an interpretation of error trajectory, however control efforts have also been reviewed. First, the EULERINT criterion values were compared in the case of a PD controller applied to linear and nonlinear models using various kinematics descriptions (Euler angles, quaternion vector and direction cosine matrix). It was discovered that among the aforementioned kinematics terminologies, the quaternion exhibits the lowest value of the EULERINT criterion. Thus, the investigation of other control methods including LQR, pole placement and adaptive feedback linearization controls was conducted using the quaternion kinematics to determine which method yields the lowest EULERINT value, control effort and simulation elapsed time.

Adaptive feedback linearization control of space robots

A robust LQR-FOPIλDµ controller design for output voltage regulation of stand-alone self-excited induction generator

Land Use Land Cover Change (LULCC) and urban growth prediction and analysis are two of the best methods that can help decision-makers for better sustainable management and planning of socioeconomic development in the countries. In the present paper, the growth of urban land use was analyzed and predicted in all districts of the El Baha region (Kingdom of Saudi Arabia) based on high-resolution Landsat, 5, 7, and 8 satellite imagery during the period of study between 1985–2021. Using remote sensing techniques, the LULCC were obtained based on the maximum likelihood classification (MLC), where the geographic information system (GIS) had been used for mapping LULCC classes. Furthermore, Markov cellular automata (MCA) in Idrisi TerrSet was applied for assessing the future growth of urban land use between 2021–2047. The findings of the LULCC analysis based on the MLC indicate great socioeconomic development during the study period and that the urban expansion was at the expense of rangeland, forest and shrubland, and barren land and sand areas, with the contribution of each in the built-up area estimated to be around 9.1% (179.7 km2), 33.4% (656.3 km2) and 57.5% (1131.5 km2), respectively. The simulation of the future LULCC period 2021–2047 revealed a loss in rangeland, forest and shrubland, and barren land and sand by 565, 144 and 105 km2, respectively, where rangeland is the most influenced, its land cover will decrease from 4002 to 3437 km2. From the obtained results based on MCA, urban growth is predicted to be large and it is estimated at around 2607 km2 until the year 2047 with a net increase of 811 km2. The results obtained from this study may provide information to help decision-makers to implement efficient practices for future planning and management of the growth of urban land use, especially Saudi vision 2030.

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Land Use Land Cover Change Analysis for Urban Growth Prediction Using Landsat Satellite Data and Markov Chain Model for Al Baha Region Saudi Arabia

In this paper, we implement an advanced modelfree PID controller for a DJI F450 quadcopter which is controlled and stabilized by a non-rooted onboard Android smartphone, without the aid of an external IMU. Furthermore, since Android is not a realtime system, the control commands and sensor measurements are subject to significant latencies, and hence the PID controller is modified to account for non-trivial measurement asynchronicities. While baseline PID control is widely used with no further modifications, we show that some features can be added to obtain better performance in the presence of latencies and noise.

Advanced PID attitude control of a quadcopter using asynchronous android flight data

In this paper, we estimate the continuous-time nonlinear rotational dynamics of a DJI F450 quadcopter which is controlled and stabilized by an onboard unrooted Android phone with a PID controller. Since the Android phone contains the only IMU in the system, we only have measurements provided by the Android Java API for estimation, which are subject to significant latencies because Android is not realtime system. Although prediction error methods are typically preferred for estimating general continuous-time nonlinear models, we show that the standard prediction error estimates are highly sensitive to the initial guess, and hence we introduce a total least-squares approach for estimating the initial model. Finally, we compare the estimated models to the model obtained from a traditional inertia measurement device, namely, a Bifilar test stand.

Estimation of a drone's rotational dynamics with piloted Android flight data

In this paper, we study the process of identifying a continuous-time nonlinear model for the rotational dynamics of a DJI F450 quadcopter being commanded by a non-rooted onboard Android smart phone, where the control commands are sent from the Android phone to an IMU-less IOIO board before being sent to the electronic speed controllers of the motors. We use only measurements provided by Android's Java API, which are subject to significant latencies since the default Android implementation is not realtime. While prediction error methods are typically considered to be the best methods for estimating such models, we show that they are highly sensitive to the initial guess. Hence we introduce two novel algorithms for obtaining an initial guess of the inertia matrix using convex optimization. Finally, we compare all of the relevant estimates to those obtained using a traditional inertia measurement device.

Mohammad A. Alsharif

Papers

A comparison between advanced model-free PID and model-based LQI attitude control of a quadcopter using asynchronous android flight data

Land Use Land Cover Change Analysis for Urban Growth Prediction Using Landsat Satellite Data and Markov Chain Model for Al Baha Region Saudi Arabia

Advanced PID attitude control of a quadcopter using asynchronous android flight data

Estimation of a drone's rotational dynamics with piloted Android flight data

System identification of a quadcopter's rotational dynamics using android flight data