Ali Mesbah

• PhD
• Professor (Associate) at University of California, Berkeley

Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives ha...

Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance constraints, which enables a systematic trade-off between attainable control performance and probability of state constraint violations in a stochastic setting. This paper pre...

This paper provides a review of model predictive control (MPC) methods with active uncertainty learning. System uncertainty poses a key theoretical and practical challenge in MPC, which can be aggravated when system uncertainty increases due to the time-varying nature of system dynamics. For uncertain systems with stochastic uncertainty, this paper...

Stochastic model predictive control hinges on the online solution of a stochastic optimal control problem. This paper presents a computationally efficient solution method for stochastic optimal control for nonlinear systems subject to (time-varying) stochastic disturbances and (time-invariant) probabilistic model uncertainty in initial conditions a...

Atmospheric pressure plasma jets (APPJs) have widespread use in materials processing and biomedical applications. Safe and effective operation of hand-held APPJs is however highly sensitive to the intrinsic variability of plasma characteristics as well as exogenous disturbances such as variations in the separation distance between the device tip an...

Bayesian optimization (BO) is a widely used method for data-driven optimization that generally relies on zeroth-order data of objective function to construct probabilistic surrogate models. These surrogates guide the exploration-exploitation process toward finding global optimum. While Gaussian processes (GPs) are commonly employed as surrogates of...

Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the op- timization procedure. Preferences are often abstracted in the form of an unknown utility function, estimated through pair- wise comparisons of potential outcomes. However, utility-driven MOBO methods can yield solutions that are do...

The reinforcement learning (RL) and model predictive control (MPC) communities have developed vast ecosystems of theoretical approaches and computational tools for solving optimal control problems. Given their conceptual similarities but differing strengths, there has been increasing interest in synergizing RL and MPC. However, existing approaches...

Making optimal decisions under uncertainty is a shared problem among distinct fields. While optimal control is commonly studied in the framework of dynamic programming, it is approached with differing perspectives of the Bellman optimality condition. In one perspective, the Bellman equation is used to derive a global optimality condition useful for...

Low-temperature plasma catalysis holds promise for electrification of energy-intensive chemical processes such as methane reforming and ammonia synthesis. However, fundamental understanding of plasma–catalyst interactions, essential for catalyst design and screening for plasma catalysts, remains largely limited. Recent work has demonstrated the imp...

Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the optimization procedure. Preferences are often abstracted in the form of an unknown utility function, estimated through pairwise comparisons of potential outcomes. However, utility-driven MOBO methods can yield solutions that are domina...

Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making. Given the impact both fields have had independently across numerous domains, there is growing interest in combining the general-purpose learning capability of RL with the safety and robustness features of MPC. To this...

Bilevel optimization problems are challenging to solve due to the complex interplay between upper-level and lower-level decision variables. Classical solution methods generally simplify the bilevel problem to a single level problem, whereas more recent methods such as evolutionary algorithms and Bayesian optimization take a black-box view that can...

This paper presents a framework for bounding the approximation error in imitation model predictive controllers utilizing neural networks. Leveraging the Lipschitz properties of these neural networks, we derive a bound that guides dataset design to ensure the approximation error remains at chosen limits. We discuss how this method can be used to des...

Low-temperature plasmas (LTP) are increasingly used in medicine and biotechnology, materials processing, and environmental remediation, but their complex physics often render design and scale-up of LTP processes challenging. Scaling and similarity relations can aid in understanding and designing LTP processes, as they can be used to construct reduc...

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

The digital age has made embedded control a key component to user-oriented, portable, and the Internet of Things (IoT) devices. In addition, with emergent complex systems, there is a need for advanced optimization-based control strategies such as model predictive control (MPC). However, the unified implementation of these advanced strategies on har...

Food production and pharmaceutical synthesis are posited as essential biotechnologies for facilitating human exploration beyond Earth. These technologies not only offer critical green space and food agency to astronauts but also promise to minimize mass and volume requirements through scalable, modular agriculture within closed-loop systems, offeri...

Deep neural networks are increasingly used as an effective way to represent control policies in a wide-range of learning-based control methods. For continuous-time optimal control problems (OCPs), which are central to many decision-making tasks, control policy learning can be cast as a neural ordinary differential equation (NODE) problem wherein st...

Low-temperature plasma catalysis holds promise for electrification of energy-intensive chemical processes such as methane reforming and ammonia synthesis. However, fundamental understanding of plasma-catalyst interactions, essential for catalyst design and screening for plasma catalysts, remains largely limited. Recent work has demonstrated the imp...

Plasma etching is an essential semiconductor manufacturing technology required to enable the current microelectronics industry. Along with lithographic patterning, thin-film formation methods, and others, plasma etching has dynamically evolved to meet the exponentially growing demands of the microelectronics industry that enables modern society. At...

Food production and pharmaceutical synthesis are posited as essential biotechnologies for facilitating human exploration beyond Earth. These technologies not only offer critical green space and food agency to astronauts but also promise to minimize mass and volume requirements through scalable, modular agriculture within closed-loop systems, offeri...

Sustainable fertilizer production is a pressing challenge due to a growing human population. The manufacture of synthetic nitrogen fertilizer involves intensive emissions of greenhouse gases. The synthetic nitrogen that ends up in biowaste such as animal waste perturbs the nitrogen cycle through significant nitrogen losses in the form of ammonia vo...

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Low-temperature plasma catalysis has shown promise for various chemical processes such as light hydrocarbon conversion, volatile organic compounds removal, and ammonia synthesis. Plasma-catalytic ammonia synthesis has the potential advantages of leveraging renewable energy and distributed manufacturing principles to mitigate the pressing environmen...

Cold atmospheric plasmas (CAPs) have emerged as the central component to plasma medicine, a relatively new research field in which CAPs have shown promise for a variety of biomedical uses and medical therapies. CAPs comprise of a partially-ionized gas that exists at near room temperature and atmospheric pressure. CAPs affect biological materials vi...

The design of advanced learning-and optimization-based controllers requires selecting parameters that balance performance objectives and constraints. Bayesian optimization (BO) has proven effective for resource-efficient calibration of such controllers. Preference-guided BO incorporates user preferences to prioritize areas of interest, but it lacks...

In this paper, we propose a novel model predictive control (MPC) framework for output tracking that deals with partially unknown constraints. The MPC scheme optimizes over a learning and a backup trajectory. The learning trajectory aims to explore unknown and potentially unsafe areas, if and only if this might lead to a potential performance improv...

We show that the minimum effort control of colloidal self-assembly can be naturally formulated in the order-parameter space as a generalized Schrödinger bridge problem-a class of fixed-horizon stochastic optimal control problems that originated in the works of Erwin Schrödinger in the early 1930s. In recent years, this class of problems has seen a...

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous...

Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storag...

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expe...

We show that the minimum effort control of colloidal self-assembly (SA) can be naturally formulated in the order-parameter space as a generalized Schrödinger bridge problem (GSBP)—a class of fixed-horizon stochastic optimal control problems that originated in the works of Erwin Schrödinger in the early 1930s. In recent years, this class of problems...

Scenario-based model predictive control (MPC) methods can mitigate the conservativeness inherent to open-loop robust MPC. Yet, the scenarios are often generated offline based on worst-case uncertainty descriptions obtained a priori, which can in turn limit the improvements in the robust control performance. To this end, this paper presents a learni...

Optimal control problems are prevalent in model-based control, state and parameter estimation, and experimental design for complex dynamical systems. An approach for obtaining solutions to these problems is based on the notion of parsimonious input parameterization and comprises two tasks: the enumeration of arc sequences followed by the computatio...

We propose formulating the finite-horizon stochastic optimal control problem for colloidal self-assembly in the space of probability density functions (PDFs) of the underlying state variables (namely, order parameters). The control objective is formulated in terms of steering the state PDFs from a prescribed initial probability measure towards a pr...

Optimal experiment design (OED) aims to optimize the information content of experimental observations by designing the experimental conditions. In Bayesian OED for parameter estimation, the design selection is based on an expected utility metric that accounts for the joint probability distribution of the uncertain parameters and the observations. T...

Binary colloidal superlattices (BSLs) have demonstrated enormous potential for the design of advanced multifunctional materials that can be synthesized via colloidal self-assembly. However, mechanistic understanding of the three-dimensional self-assembly of BSLs is largely limited due to a lack of tractable strategies for characterizing the many tw...

Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems. Learning the hidden physics within SDEs is crucial for unraveling fundamental understanding of these systems' stochastic and nonlinear behavior. We propose a flexible and scalable framework for training artificial neural networks to...

Low temperature, air plasmas have shown promise for production of NOxfor nitrogen fixation. However, to make nitrogen fixation via air plasmas economically viable, a major challenge arises from reducing the energy cost of NOx generation, which is a complex function of a multitude of factors including the plasma discharge type, discharge operating par...

Space bioprocess engineering (SBE) is an emerging multi-disciplinary field to design, realize, and manage biologically-driven technologies specifically with the goal of supporting life on long term space missions. SBE considers synthetic biology and bioprocess engineering under the extreme constraints of the conditions of space. A coherent strategy...

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computatio...

In this paper, we propose a novel model predictive control (MPC) framework for output tracking that deals with partially unknown constraints. The MPC scheme optimizes over a learning and a backup trajectory. The learning trajectory aims to explore unknown and potentially unsafe areas, if and only if this might lead to a potential performance improv...

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expe...

This paper proposes a learning-based adaptive-scenario-tree model predictive control (MPC) approach with probabilistic safety guarantees using Bayesian neural networks (BNNs) for nonlinear systems. First, a data-driven description of the model uncertainty (i.e., plant-model mismatch) is learned using a BNN. Then, the learned description is employed...

The closed-loop performance of model-based controllers, such as model predictive control, largely depends on the quality of their underlying model of system dynamics. Inspired by the notion of identification for control, this paper presents a strategy for performance-oriented, data-driven model adaptation for control. The fundamental idea is to mit...

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expe...

The performance of optimization‐ and learning‐based controllers critically depends on the selection of several tuning parameters that can affect the closed‐loop control performance and constraint satisfaction in highly nonlinear and nonconvex ways. Due to the black‐box nature of the relationship between tuning parameters and general closed‐loop per...

Cold atmospheric plasmas (CAPs) are increasingly used for applications requiring the processing of heat- and pressure-sensitive (bio)materials. A key challenge in model-based control of CAPs arises from the high-computational requirements of theoretical plasma models as well as lack of mechanistic understanding of plasma-surface interactions. Thus,...

Reinvigorated public interest in human space exploration has led to the need to address the science and engineering challenges described by NASA's Space Technology Grand Challenges (STGCs) for expanding the human presence in space. Here we define Space Bioprocess Engineering (SBE) as a multi-disciplinary approach to design, realize, and manage a bi...

Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. The...

The performance of advanced controllers depends on the selection of several tuning parameters that can affect the closed-loop control performance and constraint satisfaction in highly nonlinear and nonconvex ways. There has been a significant interest in auto-tuning of complex control structures using Bayesian optimization (BO). However, an open ch...

Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems. Learning the hidden physics within SDEs is crucial for unraveling fundamental understanding of the stochastic and nonlinear behavior of these systems. We propose a flexible and scalable framework for training deep neural networks to...

Models of nonlinear dynamical systems are typically composed of unknown and known parts due to the existence of mismatch between the true nonlinear system dynamics and their models. This article presents a multivariable control strategy for nonlinear systems that deals explicitly with the existence of incomplete models in a data‐driven framework. T...

p>Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. Th...

Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. The...

A crewed mission to and from Mars may include an exciting array of enabling biotechnologies that leverage inherent mass, power, and volume advantages over traditional abiotic approaches. In this perspective, we articulate the scientific and engineering goals and constraints, along with example systems, that guide the design of a surface biomanufact...

Drug dosing decisions in clinical medicine and in introducing a drug to market for the past 60 years are based on the pharmacokinetic/clinical pharmacology concept of clearance. We used chemical reaction engineering models to demonstrate the limitations of presently employed clearance measurements based upon systemic blood concentration in reflecti...

The increasing complexity of modern technical systems can exacerbate model uncertainty in model-based control, posing a great challenge to safe and effective system operation under closed loop. Online learning of model uncertainty can enhance control performance by reducing plant-model mismatch. This article presents a learning-based stochastic mod...

Cold atmospheric plasmas (CAPs) are increasingly used for treatment of complex surfaces in biomedical and biomaterials processing applications. However, the multivariable, distributed-parameter, and nonlinear nature of CAP dynamics and plasma–surface interactions, coupled with the sensitivity of plasmas to exogenous disturbances, make their safe, r...

Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems’ stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about st...

Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications....

The closed-loop performance of model predictive controllers (MPCs) is highly dependent on the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy, instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints...

There has been an increasing interest in explicit and cheap-to-evaluate control policies that approximate (computationally expensive) control laws such as model predictive control (MPC). However, approximate control policies are subject to approximation errors, leading to asymptotic performance losses. The contribution of this paper is three-fold:...

A crewed mission to and from Mars may include an exciting array of enabling biotechnologies that leverage inherent mass, power, and volume advantages over traditional abiotic approaches. In this perspective, we articulate the scientific and engineering goals and constraints, along with example systems, that guide the design of a surface biomanufact...

The complex and uncertain dynamics of emerging systems pose several unique challenges that need to be overcome in order to synthesize high-performance controllers. A key challenge is that safety is often achieved at the expense of closed-loop performance. This is particularly apparent when the uncertainty description is provided in the form of a bo...

Systematic design and verification of advanced control strategies for complex systems under uncertainty largely remains an open problem. Despite the promise of black-box optimization methods for automated controller tuning, they generally lack formal guarantees on the solution quality, which is especially important in the control of safety-critical...

The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In thi...

Scenario-based model predictive control (MPC) methods introduce recourse into optimal control, and can thus reduce the conservativeness inherent to open-loop robust MPC. However, the uncertainty scenarios are often generated offline using worst-case uncertainty bounds quantified a priori, limiting the potential gains in control performance. This pa...