Synthesis of a quasi-optimal fuzzy controller model under a priori uncertainty in intelligent transport underactuated systems

Robust Control and Estimation Framework for Parallel Manipulators: A Comparative Study of Super-Twisting and Fractional-Order PID Strategies

This research investigates the problem of robust control of a nonlinear, fast-dynamics, high-precision planar manipulator with a limited workspace. Starting from a mathematical model formulated in terms of Euler-Lagrange dynamics, the proposed approach considers, given the limitations of the physical system, that some states are unavailable for direct measurement, and the system is perturbed by external disturbances. Consequently, a reduced-order state observer and an estimator with time-varying gains are proposed. Using these estimates in the control stage, four types of control laws are synthesized. Two are derived from super-twisting control theory (STC), the third is an integer-order PID (IOPID) controller, and the fourth is based on a fractional-order PID (FOPID) approach with optimization-based tuning. The first three strategies require accurate information about the mathematical model of the system. All the considered control strategies address external perturbations to ensure the robustness of the system. Several tests have been conducted to validate the overall estimation and control scheme. Based on the dynamic evolution of the manipulator tip and the analyzed performance metrics, the numerical results show that all the proposed controllers provide suitable solutions to robust tracking problems.

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

Abstract 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 automation, particularly in applications such as petroleum refining, distillation operations, and pulp manufacturing. To obtain the target liquid level, a sliding-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 Lyapunov 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 absolute 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.

Reinforcement Learning-Based Optimization for Interval Type-2 Fuzzy Unknown Nonlinear System: An Zero-Sum Control Scheme.

A novel mode-free reinforcement learning (RL) algorithm is proposed for the optimal control of unknown nonlinear system presented by the interval type-2 fuzzy (IT2F) model. The optimal control is converted into a zero-sum game, where the control input and the external disturbance are the opposing competitors. Based on the RL method, a model-based policy iteration (PI) algorithm is constructed to solve the fuzzy stochastic coupled algebraic Riccati equations of nonlinear system. Considering that the dynamic parameter is difficult to obtain completely in real systems, a model-free fuzzy control algorithm is designed under the framework of the RL algorithm. An optimal control of IT2F system is realized without parameter information, only requiring the state and input information. Furthermore, the asymptotic stability with $H_{\infty } $ performance index is ensured by the Lyapunov function. Finally, the effectiveness is tested by the semi-car active suspension model (SCASM).

Practical Stabilization of Uncertain Fuzzy Systems: Design Methods and Case Studies in Environmental Control

This study develops a feedback stabilization framework for uncertain Takagi–Sugeno (T–S) fuzzy systems, guaranteeing practical exponential stability. A primary contribution is the derivation of sufficient conditions established via Lyapunov analysis, which ensure that all system trajectories converge exponentially to a bounded compact set despite modeling uncertainties, actuator variations, and external perturbations. Compared to existing T–S fuzzy control approaches, our method provides an explicit characterization of the convergence rate and the ultimate bound in terms of perturbation bounds, and handles both matched and unmatched uncertainties simultaneously. The theoretical framework is directly applied to the stabilization of an HVAC (Heating, Ventilation, and Air Conditioning) system for smart building climate control. This application demonstrates the method's effectiveness in maintaining precise temperature and humidity regulation under realistic disturbances, such as occupancy changes and equipment efficiency drift. A detailed numerical example with all matrices explicitly provided and a comprehensive HVAC case study with complete simulation parameters confirm the approach's practical utility for designing resilient, energy efficient control systems in intelligent buildings.

ADRC Based on Fuzzy State Observer with Varying Dynamics Applied for Two-Mass System

Due to the constant improvement of technological processes in today's industry, there is a visible struggle to improve a deployment process of new scientific solutions. As the complexity of industrial machinery increases, it becomes more difficult to conduct a whole implementation quickly and successfully during the first attempt. One of the most challenging issues is the dependency of the designed control systems on the plant parameters, they are meant to control. The paper proposes a novel solution, which allows to control a two-mass electric drive unit, considering a significant uncertainty in flexible shaft parameterization. The described solution is based on the Active Disturbance Rejection Controller (ADRC) with Fuzzy Extended State Observer (FESO), which increases the dynamics of the estimated disturbance regarding the actual torque demand. The experimental verification proves that the elaborated solution provides the possibility to operate the drive with roughly known shaft parameters, including efficient damping capabilities of torsional vibrations and remarkable overshoot minimization.

A Taylor-series-based linearization approach for dynamic parameter identification of robotic manipulators