A new approach for trajectory tracking on uncertain complex networks is proposed. To achieve this goal, a neural controller is applied to a small fraction of nodes (pinned ones). Such controller is composed of an on-line identifier based on a recurrent high-order neural network, and an inverse optimal controller to track the desired trajectory; a complete stability analysis is also included. In order to verify the applicability and good performance of the proposed control scheme, a representative example is simulated, which consists of a complex network with each node described by a chaotic Lorenz oscillator.

This paper presents a systematic approach for designing an electric motor drive system (EMDS) for a permanent magnet DC motor to achieve precise speed regulation using a classical PID controller. Smooth voltage trajectory planning based on Bézier curves is employed to mitigate high voltage and current peaks during step speed transitions, improving dynamic performance, reducing electrical stress, and making the control system physically realizable. A comparative evaluation of inverting buck–boost, positive buck–boost, and quadratic DC–DC converters is conducted using the same motor and controller, enabling the identification of the most suitable controller–converter pairing. Experimental results demonstrate that, with an appropriate converter topology and voltage trajectory, peak voltages and currents are significantly reduced, resulting in a smoother control action and reliable speed regulation without the need for complex control schemes.

Wireless control systems often suffer from performance degradation due to sensor noise, transmission delays, and packet loss over Wi-Fi links. This paper presents an architecture for telemetry with resilient control for real-time DC motor speed regulation. The proposed architecture has a functional division of tasks between a local ESP32-S3 microcontroller and a remote computer. The ESP32-S3 microcontroller receives reference speed set-points through Wi-Fi and applies a Bézier curve profiler to smooth speed transitions, preventing transient voltage and current surges. To avoid the necessity of extra physical sensors, a Euler-discretized sliding-mode observer (SMO) estimates the unmeasured armature current of the motor and the dynamic load torque from encoder-based speed measurements. These states are fed into a discrete Linear Quadratic Regulator (LQR) to compute the control action for a Buck/Boost power converter driving the motor under variable load disturbances. Simultaneously, the ESP32-S3 streams a telemetry vector containing $V_a$, $i_a$, $\omega$, and ${\widehat{T}}_L$ over Wi-Fi to the remote computer. This computer executes an Extended Kalman Filter (EKF) that attenuates communication noise and reconstructs missing telemetry during packet-loss events, enabling uninterrupted real-time visualization and persistent data logging. The experimental validation includes a broad operating range, multiple load-torque levels, packet-loss intervals, and comparative tests against a classical PID controller. The results show that the proposed LQR-based architecture maintains accurate speed tracking over different operating regions and provides faster recovery after load-torque perturbations when compared with the PID controller. In addition, the telemetry link was evaluated under non-ideal communication conditions, where the EKF maintained the availability of the estimated variables during packet-loss intervals and reduced high-frequency fluctuations in the received telemetry signals. Furthermore, an experimental comparison between time-varying and constant EKF covariance matrices supports the use of the proposed $Q_{\mathrm{EKF}}\left(k\right),R_{\mathrm{EKF}}\left(k\right)$ formulation to improve the estimation behavior under changing operating conditions. Experimental validation on a physical test bench demonstrates that the integrated control framework maintains accurate speed regulation, smooth transient responses, disturbance-rejection capability, and data continuity under variable operating speeds, non-linear load-torque variations, and degraded Wi-Fi telemetry conditions.