Ultra-local Model Control Research Articles

Design of Ultra-Local Model Control Based on Interval Type-2 Fuzzy Logic for Nine-Level Packed E-Cell

Design of Ultra-Local Model Control Based on Interval Type-2 Fuzzy Logic for Nine-Level Packed E-Cell

Optimal Ultra-Local Model Control Integrated with Load Frequency Control of Renewable Energy Sources Based Microgrids

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG.

Automatic Regulation of Anesthesia via Ultra-Local Model Control

Abstract As a part of the BMS2021 Benchmark Challenge, this paper deals with the design and testing of a closed-loop anesthesia delivery regulation system by exploiting the open-source Matlab-based patient simulator. Because of system inherent complexity together with intra-and inter-patient parameters variability and partially unknown disturbances, traditional model-based approaches may suffer. To overcome these limitations, we opt for a data-driven approach using real-time ultra-local models coupled with the corresponding so-called intelligent controllers. In this way, one maintains the hemodynamic variables while regulating the levels of hypnosis, analgesia, and neuromuscular blockade in anesthesia by automatic delivery of drugs. The performance of the proposed approach has been evaluated in silico by considering a representative dataset composed of 24 patients, the presence of disturbances mimicking both surgical stimulations and actions of “anesthesiologist in the loop”, including also noise effects and time-varying system delays.

Machine Learning Approach Based on Ultra-Local Model Control for Treating Cancer Pain

Cancer illness still is one of the most common illnesses in the world, which is constantly rising. Chemotherapy plays a crucial role in treating cancer patients. In this paper, we have presented a novel intelligent sensor for controlling and adjusting chemotherapy parameters which consist of an ultra-local (ULM) controller based on a deep deterministic policy gradient (DDPG). First, the feedback signal is provided using a sensor to calculate the population of cells. Then, a controller sends the proper control commands to the actuator (chemotherapy). In the suggested scheme, the ULM is applied to the dynamic model of cancer. In order to shrink tumor cells and rising immune and normal cells at the same time. Moreover, for improving the performance of the established ULM scheme, a DDPG algorithm with the actor-critic structure is used for tuning the parameters of ULM in an adaptive manner. To demonstrate the supremacy of the DDPG based ULM controller, the conventional ULM and proportional integrator (PI) are also designed for the cancer treatment. Simulation outcomes prove the improved cancer treatment compared to the ULM and PI schemes.

Performances comparison between ultra-local model control, integral sliding mode control and PID control for a coupled tanks system

This paper deals with the comparison of robust control approaches for the level water control of coupled tanks system. A new ultra-local model control (ULMC) approach leading to adaptive controller is proposed. The parameter identification of the ultra-local model is based on the algebraic derivation techniques. The main advantages of this control strategy are its simplicity and robustness. A comparison study with the integral sliding mode control (ISMC) approach is carried out. The perfect knowledge of the output variable degree, which is a standard assumption for sliding modes, is assumed here. The comparison of the simulation results for the proposed adaptive controller with the ISMC controller and the classical PID controller has a better performances in the presence of external perturbations and parameter uncertainties.

Performances comparison between ultra-local model control, integral sliding mode control and PID control for a coupled tanks system

Performances comparison between ultra-local model control, integral sliding mode control and PID control for a coupled tanks system

Design of adaptive PID controllers based on adaptive Smith predictor for ultra-local model control

In this paper, an ultra-local model control approach based on adaptive Smith predictor is proposed. The design of adaptive PID controller takes into account the estimation of variable time delay which is compensated by the addition of an adaptive Smith predictor. The purpose of this paper is to solve the online estimation problem of time delay thanks to the proposed identification method of ultra-local model parameters. A performance comparison between the proposed control approach and the Smith predictor control with classical PID is carried out. The numerical simulation results of the thermal process study with severe constraints and operating conditions show the superiority of the adaptive PID controller. The robustness with respect to noises, disturbances and system parameter uncertainties of control approaches are highlighted.

Design of adaptive PID controllers based on adaptive Smith predictor for ultra-local model control

Design of adaptive PID controllers based on adaptive Smith predictor for ultra-local model control

Experimental comparison of new adaptive PI controllers based on the ultra-local model parameter identification

This paper is devoted to an experimental comparison between two different methods of ultra-local model control. The concept of the first proposed technique is based on the linear system resolution technique to estimate the ultra-local model parameters. The second proposed method is based on the linear adaptive observer which allows the joint estimation of state and unknown system parameters. The closed-loop control is implemented via an adaptive PID controller. In order to show the efficiency of these two control strategies, experimental validations are carried out on a two-tank system. The experimental results show the effectiveness and robustness of the proposed controllers.

Towards an ultra-local model control of two-tank-system

This paper deals with the design of an ultra-local model control. The proposed approach is based on the estimation of the ultra-local model parameters using least squares resolution technique instead of numerical derivation technique. The closed-loop control is implemented through an adaptive PI in order to reject the influences of the disturbance and noise output signals. Its main advantages are: its simplicity and its robustness with respect to the parameter uncertainties of system. In this paper, it is processed to test the efficiency of the parameter estimation method compared with the performance of numerical derivation technique. The method is applied to the water level control of a two-tank-system. Numerical simulations show that the generated desired trajectory is followed in an efficient way even with severe operating conditions.