Mobile robots represent one of the most relevant areas of study within robotics due to their potential for designing and developing new nonlinear control structures that can be implemented in simulations and applications in specific environments. In this work, a fuzzy steering controller with a symmetric distribution of fuzzy numbers is proposed and designed for implementation in the kinematic model of a non-holonomic mobile robot. The symmetry in the distribution of triangular fuzzy numbers contributes to a balanced response to disturbances and minimizes systematic errors in direction estimation. Additionally, it improves the system’s adaptability to various reference paths, ensuring accurate tracking and optimized performance in robot navigation. Furthermore, this fuzzy logic-based controller emulates the behavior of a classic PID controller by offering a robust and flexible alternative to traditional methods. A virtual environment was also developed using the UNITY platform to evaluate the performance of the fuzzy controller. The results were evaluated by considering the average tracking error, maximum error, steady-state error, settling time, and total distance traveled, emphasizing the trajectory error. The circular trajectory showed high accuracy with an average error of 0.0089 m, while the cross trajectory presented 0.01814 m, reflecting slight deviations in the turns. The point-to-point trajectory registered a more significant error of 0.9531 m due to abrupt transitions, although with effective corrections in a steady state. The simulation results validate the robustness of the proposed fuzzy controller, providing quantitative insights into its precision and efficiency in a virtual environment, and demonstrating the effectiveness of the proposal.

The aero-pendulum is a non-linear system used broadly to develop and test new controller strategies. This paper presents a new methodology for an adaptive PID fuzzy-based tracking controller using a Hunting Search (HuS) algorithm. The HuS algorithm computes the parameters of the membership functions of the fuzzification stage. As a novelty, the algorithm guarantees the overlap of the membership functions to ensure that all the functions are interconnected, generating new hunters to search for better solutions in the overlapping area. For the defuzzification stage, the HuS algorithm sets the singletons in optimal positions to evaluate the controller response using the centroid method. To probe the robustness of the methodology, the PID fuzzy controller algorithm is implemented in an embedded system to track the angular position of an aero-pendulum test bench. The results show that the adaptive PID fuzzy controller proposed presents root mean square error values of 0.42, 0.40, and 0.49 for 80, 90, and 100 degrees, respectively.

This paper discusses the energy consumption of three parabolic, trapezoidal, and S-curve profiles when implemented using an embedded system. In addition, it presents an alternative methodology for implementing motion controllers using an Advanced RISC Machine (ARM) microcontroller, which computes the trajectory and performs the control action in hard real-time. We experimented using a linear plant composed of a direct current (DC) motor coupled to an endless screw where a carriage was mounted. It can move mechanically along a rail at a distance of 1.16 m. A 4096 pulses per revolution (PPR) encoder was connected to the motor to calculate position and angular velocity. A Hall-effect-based current sensor was used to assess energy consumption. We conducted 40 tests for each profile to compare the energy consumption for the three motion profiles, considering cases with and without load on the carriage. We determined that the parabolic profile provides $22.19\%$ lower energy consumption than the other profiles considered in this study, whereas the S-curve profile exhibited the highest energy consumption.

The UAV bicopter is a double-propeller system whose main objective is to stabilize a rod at a given angle by precisely controlling the rotation speed of each propeller. This mechanism generates asymmetric thrust forces that induce a torque on the bar, thus allowing its pitch angle to be modified. Since its dynamics involve complex interactions between the thrust generated by the rotors, aerodynamic effects, and the pendulum behavior of the system, the bicopter is classified as a highly nonlinear system sensitive to external disturbances. To address this complexity, the implementation of a fuzzy Takagi–Sugeno–Kang (TSK) controller is proposed. This controller decomposes the nonlinear dynamics into multiple local linear models associated with a specific operating condition, such as different pitch angles and rotor speeds. The control strategy provides accurate trajectory tracking and effectively handles disturbances and varying conditions, making this approach a practical solution for both dynamic and uncertain environments. This strategy ensures precise trajectory tracking and demonstrates robust performance compared to other control methods, such as PID and LQR, which often struggle with disturbances and system nonlinearities. The TSK controller has proven its effectiveness in experimental trajectory tracking tests, achieving root mean square errors (RMSEs) of 0.2049, 0.3269, 0.3899, 0.3335, and 0.2494, which evaluate the average error in degrees of the system concerning the target position, for tracking trajectories of −10 to 10, −12 to 12, −15 to 15, −17 to 17, and −20 to 20 degrees, respectively.

Two-wheeled balancing robots are commonly modeled as inverted pendulum systems, which are inherently unstable and present significant challenges for control due to their nonlinear and underactuated dynamics. One of the main problems in their control is achieving stability and precise position tracking under real-world disturbances and dynamic uncertainties. This research addresses these challenges by proposing a methodology to optimize a linear quadratic regulator (LQR) controller using metaheuristic algorithms. Specifically, the parameters Q and R of the LQR are tuned using three optimization strategies: artificial bee colony (ABC), differential evolution (DE), and global-best harmony search (GHS). The performance of each algorithm is evaluated through a comparative analysis, focusing on control stability and dynamic responsiveness. Experimental results demonstrate that the controller optimized by differential evolution achieves the lowest mean square error (0.4910 degrees), outperforming ABC (0.5187 degrees) and GHS (0.5195 degrees). These findings underscore the effectiveness of metaheuristic algorithms in enhancing control performance for dynamically unstable systems, facilitating their implementation in real-world autonomous control applications.

Currently, a wide variety of velocity profiles are used in motion control. Thus, this paper compares the energy consumption of three parabolic, trapezoidal, and S-curve profiles. We studied the case of using embedded systems since they are a powerful solution to monitor and coordinate the movement of devices and machinery in various industries combined with motion control applications. These integrated systems allow precise and efficient control of motors and actuators through control and feedback algorithms. In addition, we propose an alternative methodology for implementing motion controllers using an Advanced RISC Machine (ARM) microcontroller, which computes the trajectory and the control action in real-time. We conducted an experiment using a linear plant composed of a direct current (DC) motor coupled to an endless screw where a carriage is mounted that can move mechanically along a rail a distance of 1.16m. In addition, a 4096 pulses per revolution (PPR) encoder is connected to the motor to measure position and angular velocity, which compute the distance the carriage has moved in meters. A current sensor is used to assess the energy consumption, and 40 tests for each profile were carried out to compare the energy consumption for the three-motion profiles considering cases with and without load on the carriage.