Explore the vast range of titles available.

This work presents an approach to control the temperature of a nonlinear continuous stirred tank reactor (NCSTR) through parallel cascade control structure (PCCS). For the first time, PCCS is used to control the temperature of NCSTR by (1) modelling the dynamic behavior of CSTR with a recirculating jacket heat transfer system into a third order unstable transfer function and (2) using the model matching technique to synthesize the controller parameters. The controller of the secondary loop of PCCS is designed to achieve enhanced regulatory performance whereas, the primary loop controller is designed for better setpoint tracking. The closed loop performance of the proposed method is evaluated by carrying out simulation on the differential equation of the NCSTR and comparing it with other structures such as series cascade control structure (CCS) and parallel control structure (PCS). The response shows that the proposed method provides satisfactory performance in nominal, perturbed and noisy conditions.

This article addresses a trajectory tracking control approach for an uncertain surface vessel using the new cascade structure of adaptive reinforcement learning (ARL) algorithm and kinematic controller, feed-forward term. Since a surface vessel is decoupled by kinematic sub-system and dynamic sub-system, the cascade control system is an ideal method for obtaining the tracking problem. In the proposed control structure, the dynamic control loop is designed to be the optimized method of the corresponding dynamic sub-system and the kinematic control loop is implemented by a nonlinear controller combining with feed-forward term. The online actor-critic architecture is considered in ARL algorithm to overcome the challenge of solving the Hamilton-Jacobi-Bellman (HJB) equation. Additionally, the proposed controller is able to handle the difficulty of the non-autonomous optimal control problem by designing the ARL technique for the corresponding system with a small number of state variables. Based on theoretical analysis, the ARL based control design has been made to guarantee the uniformly ultimately bounded (UUB) stability of the closed system. Finally, the simulation results are illustrated to verify the effectiveness of the proposed control scheme.

During the cementing stage, it is necessary to strictly control the density of the slurry injecting well to form cement sheath with good interlayer sealing capacity. However, due to the influence of factors such as unstable ash supply and coupling of operating parameters, problems such as unstable cement slurry density control and serious deviation from process design values often occur, which seriously limit the improvement of cementing efficiency and quality. In order to solve this problem, this paper proposes a coordinated control method of slurry density and level by combining feedforward-cascade control and split range control, to strengthen the collaborative linkage between the operating parameters of cement supply, liquid supply, slurry mixing and pump injection, and improve the quality of cement slurry density control. Finally, the proposed control method is verified by the test platform built for the actual cementing operation scenario. The test results show that the proposed method can achieve stable and accurate control of cement slurry density and effectively guarantee the safety and quality of cementing operation.

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

In this paper, the problem of state feedback quantized controller design for nonlinear networked cascade control system under multi-channel adaptive event-triggered strategy is studied. Based on the Takagi-Sugeno fuzzy model, the nonlinear system is modeled. An improved adaptive event-triggered mechanism is proposed, in which the dynamic threshold function is composed of multiple dynamic sub-functions, and the sampling data and trigger data of the sensor-controller are fully considered, which significantly alleviates the network burden of the communication channel. In addition, the dynamic quantization parameters of the dynamic quantizer are improved. The dynamic quantization parameters are coordinated by the quantized signal and the original parameters, which ensures that there is no singularity problem in the dynamic quantization parameters. Finally, the effectiveness and superiority of the proposed design method are verified by simulation examples.

The paper deals with the creation and implementation of a methodology for optimizing the parameters of cascade control of the machine tool axis drives. The first part presents the identification of a dynamic model of the axis based on experimental data from measuring the axis dynamics. The second part describes the controller model, selection of optimization objective functions, and optimization of constraint conditions. The optimization of controllers is tuned by simulation using identified state-space model. Subsequently, the optimization procedure is implemented on the identified model, and the found control parameters are used on a real machine tool linear axis with different loads. The implementation of the proposed complex procedure on a real horizontal machine tool proved the advantage of simultaneous tuning of all parameters using optimization methods. The strategy solves the problem of mutual interaction of all control law parameters disabling effective usability of gradual sequential tuning. The methodology was developed on a speed control loop, the tuning of which is usually the most difficult due to the close interaction with the dynamic properties of the machine mechanics. The whole procedure is also applicable to the position and current control loop.

This paper proposes a novel passivity cascade technique (PCT)-based control for nonlinear inverted pendulum systems. Its main objective is to stabilize the pendulum’s upward states despite uncertainties and exogenous disturbances. The proposed framework combines the estimation properties of radial basis function neural networks (RBFNs) with the passivity attributes of the cascade control framework. The unknown terms of the nonlinear system are estimated using an RBFN approximator. The performance of the closed-loop system is further enhanced by using the integral of angular position as a virtual state variable. The lumped uncertainties (NN—Neural Network approximation, external disturbances and parametric uncertainty) are compensated for by adding a robustifying adaptive rule-based signal to the PCT-based control. The boundedness of the states is confirmed using the passivity theorem. The performance of the proposed approach was assessed using a nonlinear inverted pendulum system under both nominal and disturbed conditions.

This study addresses developing systematic guidelines for the design of concentration control in the oxidation of benzene to maleic anhydride within a tubular reactor. The influence of step size selection and temperature sensor location on the tuning and performance of a PI/P cascade control system applied to the oxidation process was evaluated. The reactor’s dynamic behavior was analyzed using numerical simulations based on the solution of the Fortran mathematical model. Sensor positions and multiple step sizes (from +10% to −10%) were analyzed to characterize reactor dynamics and optimize control parameters. The results show that a controller design corresponding to a −9% step in the jacket temperature offered the best performance, ensuring process stability and selectivity. In contrast, step changes between +10% and −8% caused temperature deviations beyond safe limits. Since maleic anhydride is an essential precursor in the production of resins, plastics, lubricants, and pharmaceutical intermediates, optimizing the efficiency and safety of its production represents a significant benefit to the global chemical industry.

Purpose This paper aims to present an efficient and simplified proportional-integral/proportional-integral and derivative controller design method for the higher-order stable and integrating processes with time delay in the cascade control structure (CCS). Design/methodology/approach Two approaches based on model matching in the frequency domain have been proposed for tuning the controllers of the CCS. The first approach is based on achieving the desired load disturbance rejection performance, whereas the second approach is proposed to achieve the desired setpoint performance. In both the approaches, matching between the desired model and the closed-loop system with the controller is done at a low-frequency point. Model matching at low-frequency points yields a linear algebraic equation and the solution to these equations yields the controller parameters. Findings Simulations have been conducted on several examples covering high order stable, integrating, double integrating processes with time delay and nonlinear continuous stirred tank reactor. The performance of the proposed scheme has been compared with recently reported work having modified cascade control configurations, sliding mode control, model predictive control and fractional order control. The performance of both the proposed schemes is either better or comparable with the recently reported methods. However, the proposed method based on desired load disturbance rejection performance outperforms among all these schemes. Originality/value The main advantages of the proposed approaches are that they are directly applicable to any order processes, as they are free from time delay approximation and plant order reduction. In addition to this, the proposed schemes are capable of handling a wide range of different dynamical processes in a unified way.

A cascade control structure (CCS) is still the most commonly used control scheme in variable speed control (VSC) electrical drives with alternating current (AC) motors. Several tuning methods are used to select the coefficients of controllers applied in CCS. These approaches can be divided into analytical, empirical, and heuristic ones. Regardless of the tuning method used, there is still a question of whether the CCS is tuned optimally in terms of considered performance indicators to provide high-performance behavior of the electrical drive. Recently, artificial intelligence-based methods, e.g., swarm-based metaheuristic algorithms (SBMAs), have been extensively examined in this field, giving promising results. Moreover, the intensive development of artificial intelligence (AI) assistants based on large language models (LLMs) supporting decision-making processes is observed. Therefore, it is worth examining the ability of LLMs to tune the CCS in the VSC electrical drive. This paper investigates tuning methods for the cascade control structure equipped with PI-type current and angular velocity controllers for PMSM drive. Sets of CCS parameters from electrical engineers with different experiences are compared with reference solutions obtained by using the SBMA approach and LLMs. The novel LLM-based Tuning Assistant (TA) is developed and trained to improve the quality of responses. Obtained results are assessed regarding the drive performance, number of attempts, and time required to accomplish the considered task. A quantitative analysis of LLM-based solutions is also presented. The results indicate that AI-based tuning methods and the properly trained Tuning Assistant can significantly improve the performance of VSC electrical drives, while state-of-the-art LLMs do not guarantee high-performance drive operation.