In this paper, a generalized nontriangular normal form is presented to facilitate designing a recursive integral backstepping control for the class of underactuated nonholonomic systems, i.e., wheeled mobile robots (WMRs) that perform posture stabilization and trajectory tracking in environments without obstacles. Based on the differential geometry theory, we develop a multiple input multiple output (MINO) generalization of normal form using the input-output feedback linearization technique. Then, the change of variables (diffeomorphism) transform the state-space model of WMR, incorporating both kinematic and dynamic models into nontriangular normal form. As a result, the system dynamics can be represented as internal and external dynamics. The nonlinear internal dynamics of WMR pose serious challenges to design a suitable controller due to its internal dynamics being not minimum phase and non-strict feedback form structure. The proposed backstepping controller is designed in two steps. First, a standard integral backstepping controller is designed to stabilize the robot’s orientation angle. Then, a recursive integral backstepping control technique is applied to achieve asymptotic convergence of position error to zero. Hence, both asymptotic posture stabilization and trajectory tracking are achieved in semi-global regions, except the nonzero initial condition of the orientation angle. The asymptotic stability of the entire closed-loop system is shown using the Lyapunov criteria.

This paper proposed a systematic approach to select a mathematical model for an industrial heating system by adopting system identification techniques with the aim of fulfilling the design requirement for the controller. The model identification process will begin by collecting real measurement data samples with the aid of MATLAB system identification toolbox. The criteria for selecting the model that could validate model output with actual data will based upon: parametric identification technique, picking the best model structure with low order among ARX, ARMAX and BJ, and then applying model estimation and validation tests. Simulated results have shown that the BJ model has been best in providing good estimation and validation based upon performance criteria such as: final prediction error, loss function, best percentage of model fit, and co-relation analysis of residual for output.

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input–output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties.

Solar irradiance is intermittent and nonstationary, creating a need for accurate multi‐horizon forecasts to support reliable grid operation. This study proposes a hybrid empirical mode decomposition–extreme learning machine–transformer (EMD–ELM–TF) framework. EMD separates the global horizontal irradiance (GHI) series into low‐frequency trend and high‐frequency residual components; ELM forecasts the trend component, while a transformer uses the residual history and meteorological covariates to model longer temporal dependencies. The framework was evaluated on 10‐min datasets from Karachi and Islamabad at 1, 3, and 5 h horizons and compared with seven baseline models. LightGBM achieved the lowest 1 h test RMSE, with 40.29 W/m ² for Karachi and 66.66 W/m ² for Islamabad. The proposed model achieved the lowest test RMSE at 3 h (65.95 and 93.61 W/m ² ) and 5 h (75.92 and 121.11 W/m ² ) for Karachi and Islamabad, respectively. Relative to the strongest baseline at the 5 h horizon, these values represent RMSE reductions of 23.9% for Karachi and 13.8% for Islamabad. The results indicate that separately modeling trend and residual components can improve longer‐horizon short‐term forecasting (STF) after site‐specific training. Computational cost and cross‐site transferability require further evaluation.

This paper examines the secrecy performance of a decode-and-forward (DF) relay-assisted device-to-device (D2D) communication system operating over Terahertz (THz) channels in multi-cluster two-wave (MTW) fading environments. Eavesdroppers are located near the relay and the receiver, intercepting their respective signals. Co-channel interference (CCI) affecting the relay, receiver, and eavesdroppers is also considered. To counter fading, both the relay and the receiver employ Maximal Ratio Combining (MRC). The analysis uses a characteristic function (CF)-based approach to derive key secrecy metrics, such as secrecy outage probability, secrecy success probability, the probability of strictly positive secrecy capacity, and intercept probability. The derived expressions are dependent on the characteristics of the THz, MTW fading, and CCI parameters. Finally, the system’s performance is then evaluated numerically for a range of channel and interference parameters.