25 papers · ranked by Valyu relevance
E. Kaiser, J. N. Kutz, S. L. Brunton
Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling and control efforts, providing a tremendous opportunity to extend the reach of model predictive control (MPC). However, many leading methods in machine learning, such as neural networks (NN), require large volumes of training…
Junho Lee, Hyuk-Jun Chang, Xiaosong Hu
In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its…
Arthur J. Krener
— Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying as needed the horizon length of Model Predictive Control (MPC). Its goal is to achieve stabilization with horizons as small as possible so that MPC can be used on faster or more complicated dynamic processes. Beside the standard requirements of…
Saman Cyrus, Ali Khaki Sedigh
Input constrained Model predictive control (MPC) includes an optimization problem which should iteratively be solved at each time-instance. The well-known drawback of model predictive control is the computational cost of the optimization problem. This results in restriction of the application of MPC to systems with…
Krzysztof Zarzycki, Maciej Ławryńczuk, Haruo Kobayashi, Ramón Vilanova Arbós
'Ramón Vilanova Arbós'] This work is concerned with an original ball-on-plate laboratory process. First, a simplified process model based on state-space process description is derived. Next, a fast state-space MPC algorithm is discussed. Its main advantage is computational simplicity: the manipulated variables are…
Junho Lee, Hyuk-Jun Chang, Liang Li
Explicit model predictive control (eMPC) has been proposed to reduce the huge computational complexity of MPC while maintaining the performance of MPC. Therefore, this control method has been more widely employed in the automotive industry than MPC. In this paper, an eMPC is designed to perform a double-lane-change…
Christof Fehrman, C Daniel Meliza
where $ℓx_{i},u_{i}$ is the loss associated with $i$th time step, which is a function of the state variable(s) $x$ and input(s) $u$. Many types of loss functions are possible, but typically involve the state error and energy cost of the command signal. The constraints allow one to specify the dynamics of the system and…
A. Takagi, H. Gomi, E. Burdet, Y. Koike
Humans are adept at moving the arm to interact with objects and surfaces. The brain is thought to regulate motion and interactions using two different controllers, one specialized for movements and the other for force regulation. However, it remains unclear whether different control mechanisms are necessary. Here we…
Urban Fasel, Eurika Kaiser, J. Nathan Kutz, Bingni W. Brunton + 1 more
'Steven L. Brunton'] Abstract— Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of…
Roja Eini, Sherif Abdelwahed
— This paper introduces an indirect adaptive fuzzy model predictive control strategy for a nonlinear rotational inverted pendulum with model uncertainties. In the first stage, a nonlinear prediction model is provided based on the fuzzy sets, and the model parameters are tuned through the adaption rules. In the second…
Kathleen Van Beylen, Ali Youssef, Alberto Peña Fernández, Toon Lambrechts + 2 more
'Toon Lambrechts' 'Ioannis Papantoniou' 'Jean-Marie Aerts'] Implementing a personalised feeding strategy for each individual batch of a bioprocess could significantly reduce the unnecessary costs of overfeeding the cells. This paper uses lactate measurements during the cell culture process as an indication of cell…
Nafay Hifzur Rehman, Neelam Verma
—This paper presents a modified multi model predictive control algorithm for the control of riser outlet temperature and regenerator temperature for the fluid catalytic cracking unit (FCCU). The models of the fluid catalytic cracking unit are estimated using subspace identification (N4SID) algorithm. The PRBS signal is…
Med. Essahafi
— Coupled Tank system used for liquid level control is a model of plant that has usually been used in industries especially chemical process industries. Level control is also very important for mixing reactant process. This survey paper tries to presents in a systemic way an approach predictive control strategy for a…
Authors not listed
This paper presents an empirical comparison of process control algorithms, with particular emphasis on classical Proportional–Integral–Derivative (PID) control, Model Predictive Control (MPC), and neural network-based methods in the context of complex industrial plants. Since industrial sectors frequently demand…
Menno van Laarhoven, Alfredo Rates, Josiah B. Passmore, Shengling Shi + 3 more
Optogenetics enables experiments in out-of-equilibrium conditions to clarify biological mechanisms and quantify biophysical parameters. However, modelling and control techniques to study mammalian cell biology under optogenetic perturbation remain underutilised. Here, we benchmark these methods within mammalian cells…
Mohammad Reza Ahmadpour, Hamid Ghadiri, Saeed Reza Hajian
Given the importance of high blood pressure, it is important to control and maintain a constant blood pressure level in the normal state. The main aim of this article is to design a model predictive controller with a genetic algorithm (GA) for the regulation of arterial blood pressure. The present study is an applied…
Tomoki Ohkubo, Yuichi Sakumura, Katsuyuki Kunida
Fed-batch culture enables high productivity by maintaining low substrate concentrations in the early stage of the culture to suppress the accumulation of by-products that are harmful to cell growth. Therefore, they are widely used in the production of biopharmaceuticals by mammalian cells. However, there exists a…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Robin Henry, Jean-Baptiste Lugagne
Achieving real-time control of genetic systems is critical for improving the reliability, efficiency, and reproducibility of biological research and engineering. Yet the intrinsic stochasticity of these systems makes this goal difficult. Prior efforts have faced three recurring challenges: (a) predictive models of gene…
Harrison Ritz, Matthew R. Nassar, Michael J. Frank, Amitai Shenhav
In order to behave adaptively in environments that are noisy and non-stationary, humans and other animals must monitor feedback from their environment and adjust their predictions and actions accordingly. An under-studied approach for modeling these adaptive processes comes from the engineering field of control theory…
Alexander Pomberger, Nicholas Jose, David Walz, Jens Meissner + 4 more
Buffer solutions have tremendous importance in biological systems and in formulated products. Whilst the pH response upon acid/base addition to a mixture containing a single buffer can be described by the Henderson-Hasselbalch equation, modelling the pH response for multi-buffered poly-protic systems after acid/base…
Authors not listed
Sand production poses a major challenge in wells completed within sandstone reservoirs, despite their inherent benefits. It could lead to erosion of surface and subsurface equipment, well plugging from sand grain deposition and accumulation, and potential collapse of sections in horizontal wells, resulting in…
Authors not listed
Processing high dimensional and complex monoclonal antibody (mAb) bioprocess data in industry is now more efficient due to conversational AI. The human in the loop approach to Large Language Model (LLM) inferencing with document retrieval and chained outputs is a probable benefit to existing biotechnology workflows.…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…
Jiyizhe Zhang, Daria Semochkina, Naoto Sugisawa, David Woods + 1 more
Multi-objective Bayesian optimization (MOBO) has shown to be a promising tool for reaction development. However, noise is usually unavoidable during experiments and makes it challenging to find reliable solutions. In this study, we focus on finding a set of optimal reaction conditions using multi-objective Euclidian…