In this paper, we address the modeling, simulation, and control of a rotary inverted pendulum (RIP). The RIP model assembled via the MATLAB (Matlab 2021a)^®/Simulink (Simulink 10.3) Simscape (Simscape 7.3)™ environment demonstrates a high degree of fidelity in its capacity to capture the dynamic characteristics of an actual system, including nonlinear friction. The mathematical model of the RIP is obtained via the Euler–Lagrange approach, and a parameter identification procedure is carried out over the Simscape model for the purpose of validating the mathematical model. The usefulness of the proposed Simscape model is demonstrated by the implementation of a variety of control strategies, including linear controllers as the linear quadratic regulator (LQR), proportional–integral–derivative (PID) and model predictive control (MPC), nonlinear controllers such as feedback linearization (FL) and sliding mode control (SMC), and artificial intelligence (AI)-based controllers such as FL with adaptive neural network compensation (FL-ANC) and reinforcement learning (RL). A design methodology that integrates RL with other control techniques is proposed. Following the proposed methodology, a FL-RL and a proportional–derivative control with RL (PD-RL) are implemented as strategies to achieve stabilization of the RIP. The swing-up control is incorporated into all controllers. The visual environment provided by Simscape facilitates a better comprehension and understanding of the RIP behavior. A comprehensive analysis of the performance of each control strategy is conducted, revealing that AI-based controllers demonstrate superior performance compared to linear and nonlinear controllers. In addition, the FL-RL and PD-RL controllers exhibit improved performance with respect to the FL-ANC and RL controllers when subjected to external disturbance.

Control and estimation are carried out in this paper for a ballbot underactuated robotic system. Under position measurements, the Extended Kalman Filter (EKF) is used as an asymptotic estimator in order to estimate the unavailable state vector of the robotic system. Then, the estimated state vector is used by a Linear Quadratic Regulator (LQR) that steers the ballbot to its upright position. It is demonstrated that the closed loop system that includes the ballbot, the LQR, and the EKF remains locally asymptotically stable. Numerical results corroborate the viability of the proposed control and estimation scheme.

In this paper, a decentralized model reference adaptive control (MRAC) with integral action scheme is proposed in order to achieve the attitude control task for a quadrotor unmanned aerial vehicle (UAV). The nonlinear model for the quadrotor UAV is decomposed in the rotational and altitude systems. Then, a second order subsystem for each attitude angle is extracted from the rotational system dynamics. Thus, under the assumption that a linear-in-the-parameters model for each subsystem can be defined, each subsystem is represented by a parameterized model from which a MRAC with integral action is designed. A PID controller is implemented for altitude control. The performance of our decentralized adaptive control with integral action scheme is validated via simulation results taking into account parametric variations, noise in the measurements and external perturbations.

Underactuated systems are mechanical systems with fewer actuators than the system degrees of freedom, i.e., some degrees of freedom are not directly driven by actuators. Many benchmark underactuated systems have been developed to study the nature of underactuated dynamics and their possible controller designs. These systems include, but are not limited to, the acrobot, pendubot, cart pole system, overhead crane, translational oscillator/rotational actuator (TORA) system, and rotary inverted pendulum. Unlike fully actuated systems where many control strategies are available for an entire class of systems, there are few results applicable to even a small class of underactuated mechanical systems. In this paper, an adaptive neural sliding mode controller (ANSMC) is proposed in order to stabilize an inverted pendulum mounted on a ball system; this latter an interesting underactuated mechanical system for which the controller designs are a pending task, i.e. the performance for different control strategies could be explored. The performance of our proposal is verified via numerical simulation using the mathematical model for a benchmark prototype based on the LEGO Mindstorms NXT kit, showing the robustness against time-varying uncertainties and disturbances.

Underactuated systems are mechanical systems with fewer actuators than the system degrees of freedom, i.e., some degrees of freedom are not directly driven by actuators. Many benchmark underactuated systems have been developed to study the nature of underactuated dynamics and their possible controller designs. Unlike fully actuated systems where many control strategies are available for an entire class of systems, there are few results applicable to even a small class of underactuated mechanical systems. In this paper, an Adaptive Fuzzy Sliding Mode Controller (AFSMC) is proposed in order to stabilize an inverted pendulum mounted on a ball system, also known as ballbot; this latter an interesting underactuated mechanical system for which the controller designs are a pending task, i.e. the performance for different control strategies could be explored. The performance of our proposal is verified via numerical simulation, showing the robustness against time-varying uncertainties and disturbances.

This paper addresses the formation control problem of a multi-agent system (MAS) under the leader-follower scheme. Thus, the followers must to keep a desired relative distance with respect to the leader trajectory against known and constant communication delay. Thus, in order to ensured the coordinated behavior and formation control against parametric uncertainties and external disturbances, a decentralized control approach based on a state predictor and a Second Order Sliding Mode control is proposed. Sufficient conditions are provided to guarantee the stability of the closed-loop system. Finally, simulation results illustrate the effectiveness of the proposed method.