For the last decade, there has been great interest in studying dynamic control for unmanned aerial vehicles, but drones—although a useful technology in different areas—are prone to several issues, ...such as instability, the high energy consumption of batteries, and the inaccuracy of tracking targets. Different approaches have been proposed for dealing with nonlinearity issues, which represent the most important features of this system. This paper focuses on the most common control strategy, known as model predictive control (MPC), with its two branches, linear (LMPC) and nonlinear (NLMPC). The aim is to develop a model based on sensors embedded in a Tello quad-rotor used for indoor purposes. The original controller of the Tello quad-rotor is supposed to be the slave, and the designed model predictive controller was created in MATLAB. The design was imported to another embedded system, considered the master. The objective of this model is to track the reference trajectory while maintaining the stability of the system and ensuring low energy consumption. The case study in this paper compares linear and nonlinear model predictive control (MPC). The results show the efficiency of NLMPC, which provides more promising results compared to LMPC. The comparison concentrates on the energy consumption, the tracked trajectory, and the execution time. The main finding of this research is that NLMPC is a good solution to smoothly track the reference trajectory. The controller in this case processes faster, but the rotors consume more energy because of the increased values of control inputs calculated by the nonlinear controller.
Considering the current world trends, the most challenging issue industry is facing revolves around how to reduce the power consumption of electronic systems. Since the invention of computers, ...electrical energy consumption has increased dramatically; this is due to the emergence of new systems in industry. Systems like industrial robots and autonomous vehicles—including electric vehicles (EVs) and unmanned aerial vehicles (UAVs)—have had a great impact in making human life easier but have also led to higher energy consumption. At present, researchers and developers are actively seeking solutions and patents to optimize the energy consumption of the mentioned systems and generate savings, with the goal of reducing their environmental impact and improving their efficiency and effectiveness. From the literature review, papers related to energy optimization and energy consumption are considered vital, and a huge number of research publications and survey papers discuss it. This paper presents a systematic review of the classification and analysis of various methodologies and solutions that have been developed to enhance the energy performance of robotic systems, focusing on industrial robots, autonomous vehicles, and embedded systems. The aim of this research is to provide a reference point for the existing methods, techniques, and technologies that are available. It compares and evaluates different hardware and software methods related to industrial robots, autonomous vehicles, and embedded systems, highlighting the possible future perspectives in the field.
With the increase in the non-linearity and complexity of the driving system’s environment, developing and optimizing related applications is becoming more crucial and remains an open challenge for ...researchers and automotive companies alike. Model predictive control (MPC) is a well-known classic control strategy used to solve online optimization problems. MPC is computationally expensive and resource-consuming. Recently, machine learning has become an effective alternative to classical control systems. This paper provides a developed deep neural network (DNN)-based control strategy for automated steering deployed on FPGA. The DNN model was designed and trained based on the behavior of the traditional MPC controller. The performance of the DNN model is evaluated compared to the performance of the designed MPC which already proved its merit in automated driving task. A new automatic intellectual property generator based on the Xilinx system generator (XSG) has been developed, not only to perform the deployment but also to optimize it. The performance was evaluated based on the ability of the controllers to drive the lateral deviation and yaw angle of the vehicle to be as close as possible to zero. The DNN model was implemented on FPGA using two different data types, fixed-point and floating-point, in order to evaluate the efficiency in the terms of performance and resource consumption. The obtained results show that the suggested DNN model provided a satisfactory performance and successfully imitated the behavior of the traditional MPC with a very small root mean square error (RMSE = 0.011228 rad). Additionally, the results show that the deployments using fixed-point data greatly reduced resource consumption compared to the floating-point data type while maintaining satisfactory performance and meeting the safety conditions
The use of microelectromechanical systems' sensors is growing steadily since it is the cheapest way to get information about the inertial state of a moving object. However, preprocessing inertial ...data using hybrid reconfigurability is challenging. This article is an analysis of the noise generated by a multisensory data acquisition and preprocessing system that has been implemented to interface inertial sensors (particularly accelerometers and gyroscopes). A multichannel reconfigurable voltage attenuator (MRVA) based on the field-programmable analog array, an alternative to classical voltage dividers, has been created to interface the sensors with the field-programmable gate array. This article describes a hardware accelerator implementation of two calibration/denoising processes based on the moving average filters in order to select the best choice for the preparation of inertial measurement units data, where a precision of 99.72% was reached. The MRVA is interfaced via Xilinx analog-to-digital converter, where an intellectual property has been designed for hardware acceleration using Xilinx system generator.
Many classic control approaches have already proved their merits in the automotive industry. Model predictive control (MPC) is one of the most commonly used methods. However, its efficiency drops off ...with increase in complexity of the driving environment. Recently, machine learning methods have been considered an efficient alternative to classical control approaches. Even with successful implementation of reinforcement learning in real-world applications, it is still not commonly used compared to supervised and unsupervised learning. In this paper, a reinforcement learning (RL)-based framework is suggested for application in autonomous driving systems to maintain a safe distance. Additionally, an MPC-based control model is designed for the same task. The behavior of the two controllers is compared and discussed. The trained RL model was deployed on a low-end FPGA-in-the-loop (field-programmable gate array in-the-loop). The results showed that the two controllers responded efficiently to changes in the environment. Specifically, the response of the RL controller was faster, at approximately 1.75 s, than that of the MPC controller, while the MPC provided better overshooting performance (approximately 1.3 m/s less) in terms of following the reference speeds. The reinforcement-learning model showed efficient behavior after being deployed on the FPGA with (4.9×10−6) m2/s as a maximum deviation compared to MATLAB Simulink.
This review paper centers on strategies employed for location determination in regions lacking GPS signals. It primarily explores a range of vision-based methods tailored for this purpose, ...categorizing them accordingly. The article delves into the utilization of optical flow for feature extraction-based Visual Odometry (VO) and delves into advanced optical flow estimation methods that hinge on deep learning techniques. It compares the efficacy and practical applications of frequently utilized visual localization methods while also checking the efficiency of previous researches by reapplying the algorithms to new data and comparing the results.
Despite the advanced technologies used in recent years, the lack of robust systems still exists. The automated steering system is a critical and complex task in the domain of the autonomous vehicle’s ...applications. This paper is a part of project that deals with model-based control strategy as one of the most common control strategies. The main objective is to present the implementations of Model Predictive Control (MPC) for an autonomous vehicle steering system in regards to trajectory tracking application. The obtained results are analysed and the efficiency of the use of MPC controller were discussed based on its behaviour and performance.
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