This work focuses on a gain scheduled Model Predictive Controller (MPC) for the dynamic performance enhancement of an excavator boom. The method can be generalized and applied to similar machines with the installation of pressure and boom angle transducers without modifying the hydraulic circuit of the system. In the proposed method, the transient part of pressure signals is isolated and used to counteract the actuator vibrations through a valve-based compensator. A full-scale model of an excavator's arm has been used for the experimental verification of the proposed approach. The experiments are conducted under various conditions with the proposed controller and compared with the conventional system. Since the stability of these types of machines is extremely important from the safety point of view, a stability analysis has been carried out and closed-loop stability of the system is ensured with the proposed controller. A significant reduction of 58% and 41% have been achieved in the cost function under maximum velocity extension and lowering phase respectively, where maximum boom oscillations are observed. Furthermore, an average vibration reduction of 46 % is obtained using the proposed method.

Hydraulic excavators operate in harsh environments where direct measurement of actuator chamber pressures and boom displacement is often unreliable or infeasible. This study presents a novel control strategy that estimates actuator chamber pressures from work port pressures using differential equations, eliminating the need for direct pressure or position sensors. A fuzzy logic-based proportional–derivative (PD) controller is developed to mitigate boom oscillations, particularly under high-inertia load conditions and variable operator inputs. The controller dynamically adjusts gains through fuzzy logic-based gain scheduling, enhancing adaptability across a wide range of operating conditions. The proposed method addresses the limitations of classical PID controllers, which struggle with the nonlinearities, parameter uncertainties, and instability introduced by counterbalance valves and pressure-compensated proportional valves. Experimental data is used to design fuzzy rules and membership functions, ensuring robust performance. Simulation and full-scale experimental validation demonstrate that the fuzzy PD controller significantly reduces pressure overshoot (by 23% during extension and 32% during retraction) and decreases settling time (by 31.23% and 28%, respectively) compared to conventional systems. Frequency-domain stability analysis confirms exponential stability and improved damping characteristics. The proposed control scheme enhances system reliability and safety, making it ideal for excavators operating in remote or rugged terrains where conventional sensor-based systems may fail. This approach is generalizable and does not require modifications to the existing hydraulic circuit, offering a practical and scalable solution for modern hydraulic machinery.

Hydrostatic transmissions are essential in applications demanding variable torque and speed, such as mining and agricultural machinery, due to their compact design, high power-to-weight ratio, and efficient variable speed control. Despite these advantages, their inherent nonlinearities and susceptibility to parametric uncertainties pose significant challenges for precise motion control. This study presents a comparative analysis of classical PID and robust H-infinity controllers for regulating the speed of hydraulic motors under varying torsional loads. A linearized uncertain system model is developed using upper Linear Fractional Transformations (LFTs) to capture key parametric uncertainties. A simplified H-infinity controller is designed to robustly manage system dynamics, particularly addressing phase lags induced by uncertain loads. Simulation results demonstrate that the H-infinity controller offers superior performance over the PID controller in terms of stability, disturbance rejection, and robustness to load fluctuations. This work contributes a practically viable robust control solution for improving the reliability and precision of electro-hydraulic systems, particularly in demanding, real-world environments.

This work presents for detecting internal leakage faults in hydraulic actuator cylinders using signal analysis and a supervised artificial neural network classifier. An artificial leakage is introduced and a signal-based fault detection method is employed to process and transform the signals for internal leakage detection. The analysis focuses on extracting features from the pressure signal, particularly the peaks, which include information as location, height, and width. Once the neural network is trained, it is utilized to classify the fault level into three categories: healthy system, system with low fault, and system with high fault. The proposed technique utilizes pressure signals and extracts features from the peak signals to reduce dimensionality. This method offers advantages such as reduced computational cost through feature extraction and dimensionality reduction, and it is capable of detecting multiple leakage classes. This effective technique based on artificial neural networks for detecting internal leakage faults in hydraulic cylinders.