Rodrigo Henrique Silva de Souza, Jonatas Bernardino dos Santos, Luciano Diz da Silva, Ricardo Breganon, Uiliam Nelson Lendzion Tomaz Alves
The Reaction-Wheel Inverted Pendulum (RWIP) is a nonlinear system with an unstable equilibrium point. This system is widely used for teaching and research in the control of dynamical systems. We present a laboratory prototype built at the Industrial Automation and Control Laboratory of the Federal Institute of Education, Science and Technology of Paraná–IFPR (campus Jacarezinho). The hardware consists of a rod mounted on a metal frame, a direct-current (DC) motor driving a reaction wheel, and a counterweight to reduce the required control torque. The control system uses an ESP32 interfaced with a computer running MATLAB/Simulink, with a 1 ms sampling period. Rod and motor shaft angles are measured with incremental encoders; derivative filters obtain angular velocities required for state feedback. The reaction-wheel motor is driven by an H-bridge receiving PWM (Pulse Width Modulation) signals from the ESP32. The dynamic model used to design the controllers was derived from Lagrange’s equations and coupled with the DC-motor dynamics, yielding a state-space model that was linearized about the upright equilibrium. Two full-state feedback controllers were designed and tested: (i) a pole-placement (state feedback) and (ii) a Linear-Quadratic Regulator (LQR). A simplified bang-bang energy-injection swing-up raises the pendulum from the downward equilibrium, and an angle-based switching law hands control over to the state-feedback law once the pendulum nears the upright region. Both controllers were evaluated in simulation and implemented experimentally on the prototype under the same disturbance scenario. Performance was assessed through time-domain plots and the integral of the absolute error (IAE), the integral of the squared error (ISE), and their time-weighted counterparts (ITAE and ITSE). Over the full response, including the swing-up maneuver, the two controllers perform comparably, with experimental index differences below 5 %. A repeatability campaign of fifteen runs per controller shows that the mean stabilization-phase indices of the two designs differ by 0.6 % to 1.4 %, against a run-to-run coefficient of variation of 5.6 % to 13.4 %, so the two controllers are equivalent in performance on this prototype. Across the sixty disturbance events of the campaign, all were rejected without loss of stabilization, with a largest angular excursion of 9.90 ° and using at most 5.78 V of the available 12 V. The work provides practical validation and a didactic comparison of state-feedback synthesis methods applied to the RWIP, serving as a platform for teaching and applied research.
José Ricardo Cuesta-García, Luis Alejandro Márquez-Martínez
Determining the exact set of parameters for which a linear time-delay system is asymptotically stable remains a central problem in the analysis of retarded functional differential equations, and complete, closed-form answers are scarce even in low dimensions. We consider a second-order retarded functional differential equation with four real parameters and a single commensurate delay. The stability region is derived constructively by combining Pontryagin’s classical theorem on quasi-polynomials with a novel geometric characterization based on the supremum envelope of a family of parametric curves; an accompanying algorithm computes the resulting boundary explicitly. We obtain necessary and sufficient conditions identifying the exact region of the parameter space for which the trivial solution is asymptotically stable. For particular sub-classes of systems, the characterization reduces to very simple closed-form stability criteria, illustrated by examples including a delay-independent stabilization condition for an internal combustion engine model. Since the boundary is available in explicit form, robustness indices follow naturally as distances to it, and the algorithm supports standard visualization and control-design workflows.
Abraham Efraim Rodriguez-Mata, Victor Alejandro Gonzalez-Huitron, Gerardo Flores-Colunga, Ricardo Lozoya-Ponce, Pedro Acosta Cano de los Rios, Oscar Beltran-Gomez
This paper presents an observer-based fractional-order sliding-mode control framework for nonlinear Brushless DC (BLDC) traction systems, combining a filtered high-gain observer (FHGO) with a fractional-order sliding-mode controller (FOSMC). The fractional exponent β is interpreted not merely as a tuning parameter, but as a structural energy-shaping and bifurcation variable that continuously modifies the dissipative geometry of the sliding manifold, generating a transition from integral-type regulation at β = 1 to nonlinear fractional dissipation for 0 < β < 1. A Lyapunov-based analysis is developed to establish exponential convergence under nominal conditions and input-to-state stability in the presence of bounded load disturbances, measurement noise, and approximation residuals associated with the finite-order realization of the fractional operator. Performance is evaluated through both classical tracking indices, namely, RMSE, IAE, and ITAE, and structural energy metrics, including control energy J u, control smoothness J u ̇, control deviation energy J e u, time-weighted control deviation I T A E u, and the sliding-surface deformation index J s. Numerical simulations on a nonlinear d q Brushless DC (BLDC) traction model show that fractionalization reshapes the closed-loop energy distribution, significantly reducing accumulated tracking error while maintaining bounded actuation effort. The results reveal that an intermediate fractional regime, particularly within β ∈ [ 0.6, 0.9 ], provides the most favorable compromise between robustness, smoothness, and structural efficiency. In addition, the proposed FHGO–filtered-surface–fractional-control interconnection leads to a clear attenuation of chattering, although not to its complete elimination. These findings show that fractional sliding control should be understood not as a simple parametric extension of classical sliding-mode control, but as a geometric energy-shaping mechanism for structurally adaptive nonlinear regulation in electric vehicle drives.
Chisom E. Ogbuanya, Joy N. Eneh, Collins N. Udanor
The increasing intricacy of smart home systems, propelled by the incorporation of renewable energy sources, IoT technologies, and water-dependent subsystems, demands sophisticated frameworks for effective and sustainable resource management. This paper offers a thorough evaluation of AI-based strategies for optimizing energy and water usage in smart homes, with the objective of assessing current methodologies and pinpointing research deficiencies. The review adheres to PRISMA rules and examines papers published from 2021 to 2025, obtained from Google Scholar and IEEE Xplore, with supplementary inclusion of works indexed in Scopus and Web of Science according to specified eligibility criteria. A theme synthesis methodology is utilized to analyze five principal domains: smart home energy management, non-intrusive load monitoring, reinforcement learning-based control, smart water management, and IoT security. The results demonstrate that although AI methodologies like machine learning, deep learning, and reinforcement learning markedly enhance energy efficiency, cost optimization, and real-time decision-making, the majority of research concentrates on single-resource systems and lacks comprehensive energy-water optimization. Further deficiencies encompass restricted practical implementation, inadequate economic assessment, and ongoing security issues. This review provides a thorough synthesis of recent achievements and emphasizes the necessity for integrated, scalable, and context-aware frameworks to facilitate sustainable smart home growth.
Kai Janning, Abdalsalam Housin, Christopher Schulte, Frederik Erkens, Luca Frenken, Laura Herbst, Bastian Nießing, Robert H. Schmitt
This work presents an approach to collision avoidance in multi-agent systems (MAS) by integrating Conflict-Based Search (CBS) with Model Predictive Control (MPC), referred to as Conflict-Based Model Predictive Control (CB-MPC). The proposed method leverages the conflict-avoidance strengths of CBS to generate collision-free paths, which are then refined into dynamic reference trajectories using a minimum jerk trajectory optimizer and then used inside a MPC to follow the trajectories and to avoid collisions. This integration ensures real-time trajectory execution, preventing collisions and adapting to online changes. The approach is evaluated using a magnetic planar drive system for realistic multi-agent scenarios, demonstrating enhanced real-time responsiveness and adaptability. The focus is on the development of a motion planning algorithm and its validation in dynamic environments, which are becoming increasingly relevant in modern adaptive production sites. On the MAS demonstrator with four active agents, ten different scenarios were created with varying degrees of complexity in terms of route planning. In addition, external disturbances that hinder the execution of the paths were simulated. All calculation and solution times were recorded and discussed. The result show that all scenarios could be successfully solved and executed., and the CB-MPC is therefore suitable for motion planning on the presented MAS demonstrator. The results show, that the CB-MPC is suitable for motion planning on the presented MAS demonstrator. The greatest limitation of the approach lies in scalability with regard to increasing the number of agents.
