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    Integrated Analysis of Mapping, Path Planning, and Advanced Motion Control for Autonomous Robotic Navigation
    (2025-10-01)
    Bingi, Kishore
    ;
    Singh, Abhaya Pal
    ;
    Ibrahim, Rosdiazli
    ;
    Rajamallaiah, Anugula
    ;
    Shaik, Nagoor Basha
    Autonomous robotic navigation is essential in modern systems for revolutionising various industries that operate in both static and dynamic environments. To achieve this autonomous navigation, various conventional techniques that handle environment mapping, path planning, and motion control as individual modules often face challenges in addressing the complexities of autonomous navigation. Therefore, this paper presents an integrated technique that combines three essential components, such as environment mapping, path planning, and motion control, to enhance autonomous navigation performance. The first component, i.e., the mapping, utilises both binary and probabilistic occupancy maps to represent the environment. The second component is path planning, which incorporates various graph- and sampling-based algorithms such as PRM, A*, Hybrid A*, RRT, RRT*, and BiRRT, which are evaluated in terms of path length, computational time, and safety margin on various maps. The final component, i.e., motion control, utilises both conventional and advanced controller strategies such as PID, FOPID, SFC, and MPC, for better sinusoidal trajectory tracking. The four case studies for path planning and one case study on trajectory tracking on various occupancy maps demonstrated that the A* algorithm and MPC outperformed all the compared techniques in terms of optimal path length, computational time, safety margin, and trajectory tracking error. Thus, the proposed integrated approach reveals that the interplay between mapping fidelity, planning efficiency, and control robustness is vital for reliable autonomous navigation.
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    Coordinated PHEV, PV, and ESS for Microgrid Frequency Regulation Using Centralized Model Predictive Control Considering Variation of PHEV Number
    (2018-01-01)
    Pahasa, J.
    ;
    Ngamroo, I.
    The integration of plug-in hybrid electric vehicles (PHEVs), photovoltaic (PV) generators, and energy storage systems (ESSs) into microgrids is highly anticipated. A coordinated control of PHEVs, PVs, and ESS will support frequency control in a microgrid. However, the size of the ESS depends on the surplus power of PV. The lower the surplus power is, the smaller the size of ESS. Furthermore, the number of available PHEVs vary with the cumulative number of the participating PHEVs. This variation of the number of PHEVs may reduce the PHEVs' control effect in the microgrid. This paper proposes a coordinated control of PHEVs, PVs, and ESSs for frequency control in the microgrid using a centralized model predictive control (CMPC) considering the variation of PHEV numbers. The objectives of the coordinated control are: 1) to suppress the system frequency fluctuation and 2) to minimize the surplus power of PV and, therefore, reduce the size of ESS. Simulation studies indicate that the coordinated control of PHEVs, PVs, and ESSs by the proposed CMPC is superior to that of the proportional integral derivative control and the distributed MPC in terms of minimizing the frequency fluctuation, the PV surplus power, and the ESS size.
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    Parameters tuning effects in the model predictive control of an inverted pendulum
    (2011-12-01)
    Chunhacha, Pongtorn
    ;
    Benjanarasuth, Taworn
    This paper investigates the applicability of using model predictive control for an inverted pendulum system and observes its parameters tuning effects. The linear model predictive control under an input saturation constraint is applied to the nonlinear model of the inverted pendulum on cart system. Sampling period and prediction horizon are varied for investigating their influences to the system's stability and performance. The comparison between the linear quadratic regulator and the model predictive control with the same weighting matrices is additionally implemented. © 2011 IEEE.