scholarly journals Model-Based Monitoring of Biotechnological Processes—A Review

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 908
Author(s):  
Velislava Lyubenova ◽  
Georgi Kostov ◽  
Rositsa Denkova-Kostova
Keyword(s):  
Sliding Mode ◽  
High Gain ◽  
State Variables ◽  
Applied Model ◽  
Model Based ◽  
Software Sensors ◽  
On Line ◽  
Biotechnological Processes ◽  
Sliding Mode Observers ◽  
And Control

The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.

scholarly journals Robust High-Gain Observers Based Liquid Levels and Leakage Flow Rate Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Feten Smida ◽  
Salim Hadj Saïd ◽  
Faouzi M’sahli
Keyword(s):  
Flow Rate ◽  
Sliding Mode ◽  
High Gain ◽  
Perfect Reconstruction ◽  
Leakage Flow ◽  
State Variables ◽  
Leakage Flow Rate ◽  
Tank System ◽  
Sliding Mode Observers ◽  
Estimation Quality

The paper aims to solve the problem of liquid level and leakage flow rate estimations for a state coupled four-tank process, that is why an UIO is developed to simultaneously estimate the unmeasured state variables and the perturbations considered as unknown inputs. We have proposed a state repartition that allows putting the model of the quadruple tank system to the canonical form for which the design of the observer is more easier. The observation scheme that uses a combination of high-gain observers and sliding mode observers allows improving robustness in the state estimation quality and a perfect reconstruction of the disturbance waveforms.

Sliding-mode observers for systems with unknown inputs: A high-gain approach

Automatica ◽  
2010 ◽  
Vol 46 (2) ◽  
pp. 347-353 ◽  
Author(s):  
Karanjit Kalsi ◽  
Jianming Lian ◽  
Stefen Hui ◽  
Stanislaw H. Żak
Keyword(s):  
Sliding Mode ◽  
High Gain ◽  
Unknown Inputs ◽  
Sliding Mode Observers

scholarly journals Sliding mode control of biogas production by anaerobic digestion with addition of acetate

2019 ◽  
Vol 93 ◽  
pp. 03002
Author(s):  
Plamena Zlateva
Keyword(s):  
Anaerobic Digestion ◽  
Sliding Mode Control ◽  
Expert Knowledge ◽  
Sliding Mode ◽  
Biogas Production ◽  
External Disturbance ◽  
Control Input ◽  
State Variables ◽  
Mode Control ◽  
And Control

Biogas production by anaerobic digestion with addition of acetate is considered. Sliding mode control for regulation of the biogas flow rate using the addition of acetate as a control action is proposed. The control design is carried out with direct use of nonlinear model and expert knowledge. Chattering phenomena are avoided by realizing the sliding mode with respect to the control input derivative. The state variables, external disturbance, process output and control input are varied in the known intervals. The performance of the designed sliding mode control is investigated by varying the process set point and the uncertain process parameter, which reflecting the influence of the external disturbance. The excellent performance of presented control is proved through simulation investigations in MATLAB using Simulink.

Model-based event-triggered adaptive formation control for underactuated surface vehicles via the minimal learning parameter technique

2021 ◽  
pp. 095965182110402
Author(s):  
Guoqing Zhang ◽  
Wei Yu ◽  
Jiqiang Li ◽  
Weidong Zhang
Keyword(s):  
Formation Control ◽  
Lyapunov Theory ◽  
Polar Coordinate System ◽  
State Variables ◽  
Model Based ◽  
Minimal Learning Parameter ◽  
Polar Coordinate ◽  
Learning Parameter ◽  
And Control ◽  
Event Triggered

This article presents an adaptive neural formation control algorithm for underactuated surface vehicles by the model-based event-triggered method. In the algorithm, the leader–follower structure is employed to construct the formation model. Meanwhile, two new coordinate variables are introduced to avoid the possible singularity problem that exists in the polar coordinate system. Furthermore, the event-triggered mechanism is developed by constructing the adaptive model in a concise form. Related state variables and control parameters are required to be updated only at the event-triggered instants. Thus, the communication load between the controller and the actuator could be effectively reduced. Besides, for merits of the radial basis function neural network and the minimal learning parameter techniques, only two adaptive parameters are employed to compensate for the effects of the model uncertainties and the external disturbances. With the Lyapunov theory, all signals in the closed-loop system are proved to be semi-global uniformly ultimately bounded. Finally, numerical simulations are conducted to illustrate the effectiveness and feasibility of the proposed algorithm.

Guidance and control with fixed-time convergence for an interception missile

2021 ◽  
pp. 095441002110440
Author(s):  
Lei Cui ◽  
Nan Jin ◽  
Yantao Zong
Keyword(s):  
Sliding Mode ◽  
Fixed Time ◽  
Convergence Time ◽  
Singular Problem ◽  
State Variables ◽  
Terminal Sliding Mode ◽  
Surface Design ◽  
Guidance And Control ◽  
Nonsingular Terminal Sliding Mode ◽  
And Control

This article deals with the problem of partial integrated guidance and control (IGC) design with fixed-time convergence. First of all, two new fixed-time stability systems are proposed, and a novel nonsingular terminal sliding mode with fixed-time convergence is constructed by switching the exponential term of system state variables, which can realize the transition from finite-time convergence to fixed-time convergence. Concurrently, in order to solve the singular problem of terminal sliding mode, a continuous piecewise function is used in the sliding mode surface design. Then, a novel nonsingular terminal sliding mode control with fixed-time convergence is proposed for partial IGC design; that is, the upper-bound of convergence time is independent of the initial states of both missile and target and can be set in advance. In addition, a radial basis function neural network (RBFNN) is used to adaptively estimate and compensate for the uncertainties caused by the target’s maneuvering, so that the design of fixed-time sliding mode controller does not need to know any information about the target maneuver in advance, which enables the proposed controller to be better with robustness. Finally, the effectiveness and merits of the proposed control strategy are shown by the numerical simulation results based on the nonlinear longitudinal model of missile.

On-Line Reconfigurable Machines

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
Author(s):  
Lara S. Crawford
Keyword(s):  
Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Planning And Scheduling ◽  
Model Based ◽  
Planning And Control ◽  
Resource Reasoning ◽  
On Line ◽  
Scheduling Heuristic ◽  
High Level ◽  
And Control

A recent trend in intelligent machines and manufacturing has been toward reconfigurable manufacturing systems, which move away from the idea of a fixed factory line executing an unchanging set of operations, and toward the goal of an adaptable factory structure. The logical next challenge in this area is that of on-line reconfigurability. With this capability, machines can reconfigure while running, enable or disable capabilities in real time, and respond quickly to changes in the system or the environment (including faults). We propose an approach to achieving on-line reconfigurability based on a high level of system modularity supported by integrated, model-based planning and control software. Our software capitalizes on many advanced techniques from the artificial intelligence research community, particularly in model-based domain-independent planning and scheduling, heuristic search, and temporal resource reasoning. We describe the implementation of this design in a prototype highly modular, parallel printing system.

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