Autonomous exploration in unknown environment has remained challenging due to unexpected collisions, stuckness and slowdowns around obstacles. This paper reports a novel approach based on Signed Distance Field (SDF), to optimize path planning algorithms and autonomous exploration strategy for safe and adaptive navigation in search and rescue missions. A quantitative criterion is established for evaluating the safety of planned trajectories. Simulation results show that the proposed SDF-A* path planner outperforms traditional methods with a 30.10% increase in path safety (i.e. average distance from robot to obstacles) and a 64.11% reduction in time consumption; The proposed SDF-based Safe Autonomous Exploration Strategy, combined with SDF-A* path planner, outperform traditional methods, leading to significant increases (47.06%) in path safety and reductions (44.75% and 15.32%) in exploration time and path length, respectively. The viability, efficiency, and safety of the proposed methods are further validated through real-world experiments on a three-wheeled differential steering robot equipped with Jetson Nano and RPLIDAR-A3 lidar. Results show that the proposed approach adapts to different indoor environments and map configurations without prior parameter settings.

Position observer design is one of the most important tasks for sensorless PMSM drives. In this paper, we apply extended state observer (ESO) to estimate the PMSM angular position. For the operation of constant velocity, we use the linearization technique to study the error dynamics and design parameter tuning strategies for fast convergence and robustness against adverse effects. We further analyze the equilibria distributions of the estimation error dynamics and ensure that the origin is the only stable equilibrium. For velocity tracking, the ESO estimator can be readily extended to deal with this case and its global convergence is guaranteed by the results of slowly varying systems. The experimental results on a 750W motor drive demonstrate the efficacy of the ESO position observer.

In this paper, an analytic model predictive current controller is proposed for the grid-connected power conversion system (PCS) in the battery energy storage system (BESS). This controller is designed to provide the fast and robust dynamic response while maintaining the moderate computational cost, making it suitable for real-time industrial microcontrollers. An accurate discrete-time model of the grid-connected PCS is derived to enable a closed-form solution, and the 3-dimensional current reference tracking problem, incorporating the varying dc-link voltage of the BESS, is formulated as a multi-parametric quadratic program. For this optimization problem, the constrained optimal control law is shown to be the accurate orthogonal projection of the unconstrained solution onto the corresponding active voltage constraint. Subsequently, an efficient geometric method is developed to obtain the analytic optimal control in the general case, enabling the online application of MPC with a long prediction horizon for industrial PCS. Moreover, a disturbance observer is combined with the predictive current controller to ensure offset-free tracking and robustness against system uncertainties. Simulation and experimental results on a 20 kVA PCS demonstrate the fast dynamics, low total harmonic distortion, strong robustness, and high computational efficiency of the proposed current controller.

An active explicit model predictive current controller (APCC) is developed for the power conversion system (PCS) in grid-connected battery energy storage systems (BESS). The accurate discrete-time model of the PCS is derived, with both the system and input matrices expressed as scaled rotation matrices, significantly simplifying controller design. The current reference tracking problem, considering BESS dc-link voltage variations, is formulated as a multiparametric quadratic program, where the feasible current domain is divided into multiple critical regions. The optimal control law in each region is shown to be a piecewise affine function of the current tracking error and can be pre-computed explicitly, enabling efficient real-time implementation in industrial power drives. A disturbance observer is combined with the explicit predictive controller to reject lumped disturbances, and the offset-free tracking capability of the APCC has been theoretically proven. The proposed controller is validated on a 20 kVA experimental platform using an industrial DSP (TMS320F28335). Experimental results demonstrate that the APCC achieves prompt transient response, offset-free tracking performance in steady state, and enhanced robustness against grid variations and system uncertainties.

Voltage source inverters (VSIs) in permanent magnet synchronous motor (PMSM) drives are susceptible to errors caused by dead-time insertion and other nonideal characteristics. In this article, we develop a hybrid VSI error model for the PMSM drives, which consists of a linear model in the small current range and a look-up table model in the medium to large current range. This model is identified by using linear regression and extended state observer (ESO) techniques, before the motor is in online working condition. We also investigate the equilibrium, convergence, and robustness of estimation error dynamics, and provide gain design guidelines for the ESO in both sensor and sensorless cases. Experimental studies demonstrate the efficacy of the compensation strategy with this hybrid error model.

In this article, we present analytic solutions for model predictive control (MPC) applied to the current control of surface-mounted permanent magnet synchronous motor drives. A key challenge in applying MPC to motor current control is solving the constrained optimization problem in real time. To address this, we develop an explicit procedure based on planar geometric projections to solve the problem efficiently. The correctness of our method is validated through comparisons with standard numerical solvers. Experimental results demonstrate that our algorithm can be implemented on low-cost hardware with rapid execution, supporting long prediction and control horizons while delivering fast dynamic response and low total harmonic distortion at steady state. These advantages make it suitable for both high-speed servo systems and budget-sensitive applications.