Super-Twisting Control: An Alternative to PI Control in Practical Applications

Hybrid Control Approach for a Three-Tank Non-Interacting System

This article presents a control strategy that combines a super-twisting sliding-mode reaching law with a fuzzy inference system to regulate the liquid level in the third tank of a three-tank non-interacting process. This type of plant is widely used in contemporary industrial process automa-tion, particularly in applications such as petroleum refining, distillation operations, and pulp manufacturing. To obtain the target liquid level, a slid-ing-mode controller employing the super-twisting algorithm is formulated to guarantee finite-time convergence of the tank level to the reference value, thereby improving robustness and tracking precision while inherently mitigating chattering. The fuzzy system is incorporated to estimate the parameters of the super-twisting reaching law adaptively. System stability under the proposed control scheme is demonstrated through Lya-punov analysis with explicit gain conditions. MATLAB/Simulink simulations are carried out and benchmarked against a conventional fuzzy logic controller, a Proportional-Integral-Derivative (PID) fuzzy logic controller, a PID controller using the Amigo tuning rule, and a neural network-based predictive controller. Compared with the selected benchmark controllers, the proposed method achieves faster transient response, zero overshoot, zero steady-state error, and a significantly reduced integral time absolute error (ITAE), while maintaining a competitive integral abso-lute error (IAE). The rise time is 1.6385 s, the settling time is 3.0398 s, and the IAE and ITAE values are 12.37 and 19.58, respectively.

The use of a digital twin to reconfigure a nonlinear internal model controller

SMC for Stirred Tank Reactor Model: Hybrid Systems With Bi-Boundary Sojourn Time.

This work investigates sliding mode control (SMC) for stirred tank reactor (STR) under semi-Markov switching with bi-boundary sojourn time (ST). Compared with traditional discrete hybrid systems, both the upper and lower bounds of the ST are considered for each mode, providing a more accurate characterization of the system than the upper bound. Based on the statistical properties of the semi-Markov kernel (SMK), the SMK is assumed to be partly known. Owing to the limited research on SMC for discrete hybrid systems with semi-Markov switching and bi-boundary ST, the main contribution of this work is the development of the SMC law that guarantees the reachability of the quasi-sliding mode (QSM), together with a linear matrix inequality-based framework that accommodates partly known SMK information. The proposed SMC law drives the system states to a prespecified sliding region while effectively compensating for parameter uncertainties. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed control method.

Edge dynamic matrix control for energy-efficient control of heat distribution system

The dynamic nature of thermal processes means that predictive algorithms are an obvious choice for controlling processes of this type. Unfortunately, the product variability commonly found in real-world processes forces changes in the operating point. This, in turn, forces changes in the controller settings. Unfortunately, even for commonly used PI controllers, this is not usually done in practice. This approach results in a deterioration of the control loop efficiency indices and, consequently, the efficiency of the process and even the quality of the product. Therefore, the implementation of a predictive controller in the control system that would not require personnel to re-parameterize in the event of a change in the operating point seems very attractive. The linear version of the DMC predictive controller meets these expectations, as it features a low computational complexity of the control law formula. This feature allows for its implementation in a PLC. It should be emphasized that the components of the control law for a specific operating point are the result of complex calculations that are difficult to perform in a PLC. A change in the operating point forces them to be re-determined. Therefore, only the combination of a PLC and an industrial computer (IPC) in an edge computing architecture allows the full use of the afore-mentioned advantages of the DMC predictive controller. This paper presents a predictive edge dynamic matrix control (EDMC) algorithm designed to control heat sources operating as a part of heat distribution systems. The EDMC algorithm is implemented partially in a PLC and partially in an edge device. This cooperation significantly increases the system’s available computational power and makes this solution possible to implement in industry. In addition, the publication presents a comparison of the performance quality offered by the EDMC system described in relation to the commonly used PI controller.

High precision composite control of electro-hydrostatic actuator based on extended state observer–model predictive control strategy

Electro-hydrostatic actuator (EHA) serve as crucial effectors in more-electric and all-electric aircraft, where high-precision displacement control is essential for system stability and flight safety. However, the dynamic characteristics of EHA exhibit pronounced nonlinearities, unmodeled dynamics, and susceptibility to external disturbances, which limit the effectiveness of traditional control approaches. To address these challenges, this study proposes a composite control strategy that integrates model predictive control (MPC) with an extended state observer (ESO). The ESO estimates unmodeled dynamics and external disturbances in real time, and the estimated total disturbance is fed forward into the MPC loop for active compensation. This integration enhances both robustness and transient performance. The proposed method is verified using a co-simulation platform. The results show that, compared with PID, MPC, and ESO–ADRC, the settling time of ESO–MPC under step input is reduced by 87.78%, 68.57%, and 59.26%, respectively, while the phase lag under sinusoidal input is reduced by 87.55%, 50.43%, and 32.94%, respectively. The disturbance estimation results show that the ESO can effectively reconstruct both composite sinusoidal and step-varying disturbances. The co-simulation results verify the effectiveness of ESO–MPC in high-precision EHA position control and provide a reference for controller design under complex operating conditions.

Energy-Consistent Super-Twisting Sliding-Mode Control of a Grid-Forming LC Inverter: A Port-Hamiltonian Diagnostic Perspective

This paper presents an energy-consistent super-twisting (ST) sliding-mode control design for a single-phase grid-forming inverter with an output LC filter, motivated by inverter-dominated low-inertia power-electronic applications. Throughout, energy-consistent refers to a sliding surface whose closed-loop motion is shaped to avoid large transient excursions of the filter Hamiltonian. The surface couples the voltage error, its integral, and the inductor current, and its design is informed by the port-Hamiltonian (PH) representation of the LC filter; the Hamiltonian is employed as a physically meaningful diagnostic rather than as a strict control structure. A super-twisting reaching law is used to provide continuous control action and finite-time convergence under bounded matched perturbations, with explicit gain conditions stated. An actuator saturation constraint is included in the model and its effect is reported. The proposed controller is benchmarked against a classical first-order SMC with boundary layer on the same sliding surface, isolating the contribution of the ST reaching law. Simulation studies on a grid-forming LC inverter subject to renewable-like disturbances and parameter uncertainties show that the ST controller reduces steady-state tracking error, control chatter, and Hamiltonian variation by a factor of two or more across the tested operating envelope, while the gain-sensitivity coefficient of variation of $\Delta {\mathcal{H}}$ stays below 3 over a [0.5,1.5]× nominal gain box.