Find and compare the best Model Predictive Control (MPC) software in 2024
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Use the comparison tool below to compare the top Model Predictive Control (MPC) software on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Model Predictive Control Toolbox
MathWorks
$1,180 per yearModel Predictive control Toolbox™, which includes functions, an app, Simulink®, blocks, and references for the development of model predictive control (MPC), provides functions, an application, and Simulink®, blocks. The toolbox supports the creation of explicit, explicit, adaptive, gain-scheduled, and adaptive MPC for linear problems. Nonlinear problems can be solved by single- or multi-stage nonlinear MPC. The toolbox includes deployable optimization solvers, as well as the ability to create a custom solver. Closed-loop simulations can be used to evaluate controller performance in Simulink and MATLAB®. You can also use the MISRA C(r-)- and ISO 26262-compliant examples and blocks to automate driving. These blocks and examples are compatible with lane keep, path planning, following and adaptive cruise control applications. Design adaptive, gain-scheduled, or implicit MPC controllers that solve quadratic programming (QP). From an implicit design, generate an explicit MPC controller. For mixed-integer QP problems, use a discrete control set MPC. -
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MPCPy
MPCPy
FreeMPCPy is a Python package which allows you to test and implement occupant-integrated models predictive control (MPC), for building systems. The package is focused on the use data-driven, simplified statistical or physical models to predict building performance and optimize control. Four modules provide object classes that allow you to import data, interact and validate models and control input. MPCPy is an integration platform. However, it relies upon third-party, free, open-source software packages to implement models, simulates, parameter estimation algorithms, optimization solvers, and other related tasks. This includes Python packages that can be used for data manipulation and scripting, as well as more advanced software packages for specific purposes. Modelica is the language specification that is used for optimization and modeling of physical systems. -
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COLUMBO
PiControl Solutions
Closed-loop universal multivariable optimizer for Model Predictive Control's (MPC), performance and quality improvements. You can use Excel files from Aspen Tech or Honeywell RMPCT (Robust Model Predictive Control Technology), or Predict Pro (Emerson) to create and improve the correct models for each MV-CV pair. This new optimization technology is not dependent on step tests, as Honeywell and Aspen tech require. It works in the time domain, is compact and practical, and is easy to use. Model Predictive Controls can have dozens or hundreds of dynamic models. One or more of these models could be wrong. Bad (wrong), Model Predictive Control dynamic models produce a bias between the predicted signal (model prediction error), and the measured signal from the sensor. COLUMBO can help you improve Model Predictive Control models (MPC) with either closed-loop or open-loop data. -
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INCA MPC
Inca Tools
Advanced Process Control (APC), a cost-effective method to optimize your plant's performance without having to change the hardware, is very cost-effective. APC applications stabilize the operation and optimize production and/or energy use. An important side effect is a better understanding of your production process. Advanced process control (APC), refers to a wide range of technologies and techniques that interact with the base process control systems (built with PID controls). APC technologies include e.g. LQR and LQC, H_infinity, neural, fuzzy, and Model-Based Predictive Controller (MPC) are some examples of APC technologies. An APC application optimizes every minute of your plant, 24 hours a day, 7 days a week. MPC is the most widely used APC technology in the industry. Model Predictive Control software uses a model to predict the plant's behavior in the future. It can usually be done in a matter of minutes or even hours. -
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PlantPAx
Rockwell Automation
Produces like you are skilled at navigating the complexities of staying competitive. This is true across a range of industries, including pharmaceuticals, consumer packaged goods, food and beverage, mining, chemical, and chemical. It is crucial to keep up with technological advances in order to continue your digital transformation journey. Process system users, from the control room to board rooms, face the constant challenges of balancing productivity with budget and resource constraints. They also have to address evolving operational risks. PlantPAx distributed control systems (DCS) can help you meet these challenges and deliver real productivity gains across all areas of your plant. The system features positively impact the plant's lifecycle by ensuring that your plant-wide, scalable systems increase productivity, profitability, and reduce overall risk for operations. -
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Emerson DeltaV
Emerson
DeltaV S-series Electronic Marshalling (CHARMs) allows you to land field cabling anywhere you want, regardless if you use any signal type or control strategy. The DeltaV™, Distributed Control System (DCS), is an automation system that simplifies operations and lowers project risks. The state-of the-art range of products and services improves plant performance and is easy to maintain and operate. The DeltaV DCS scales easily to meet your requirements, without adding complexity. The DeltaV system integrates with other systems, such as batch, advanced control, change management and engineering tools. -
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Pavilion8
Rockwell Automation
Complex industrial processes make it difficult to be market-driven and profitable. Manufacturers need to adjust their production methods to offer a wider range of products with higher quality and shorter production runs. They must produce more, run more efficiently, and improve product quality within the limitations of their equipment. They must ensure maximum uptime, efficient transitions and less waste. Manufacturers are also being asked to reduce their environmental impact and comply with regulated emission limits. Rockwell Automation Pavilion8r Model Predictive Control technology (MPC) is an intelligence layer that sits on top of automation systems and continuously drives the plant to achieve multiple business goals, including cost reductions, decreased emissions, and production growth--all in real-time. -
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Aspen DMC3
Aspen Technology
Deep learning combined with linear and nonlinear variables allows for more accurate and long-lasting APC models that cover a wider range of operations. Rapid controller deployment, continuous model improvements and simplified workflows enable engineers to improve ROI. Automate model building with AI. Controller tuning is simplified with step-by–step wizards that allow you to specify linear or nonlinear optimization goals. Increase controller uptime with real-time cloud KPIs that can be accessed, visualized and analysed. Energy and chemical companies must operate with greater agility in today's global economy to meet market demand and maximize their margins. Aspen DMC3 is a next generation digital technology that helps companies maintain a 2-5% increase in throughput, a 3-fold increase in yield, and a 10% reduction in energy use. Learn more about next-generation advanced control technology. -
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Cybernetica CENIT
Cybernetica
Cybernetica offers Nonlinear Model Predictive Controller (NMPC) based upon mechanistic models. Cybernetica CENIT is a flexible software product that can address any industrial problem with optimal solutions. Multivariable optimal control, predictive control, intelligent feed forward, optimal constraint handling. Adaptive control via state and parameter estimation and feedback from indirect measurements through a process model. Nonlinear models can be used over larger operating ranges. Control of nonlinear processes can be improved. There is less need to perform step-response tests and there are better state and parameter estimates. Control of batch and semibatch processes, control over nonlinear processes that operate under varying conditions. Continuous processes require optimal grade transition. Safe control of exothermal process and control of unmeasured variables such as conversion rates, product quality, and other variables. Reduced energy consumption and carbon footprint -
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AVEVA APC
AVEVA
AVEVA APC is a model predictive advanced control system that improves process economics. Manufacturers are facing increasing overhead costs and reduced capital budgets in today's economic environment. They also face rising manufacturing and energy prices and fierce global competition. Comprehensive Advanced Process Control by AVEVA helps you solve complex manufacturing problems with state-of the-art automated control solutions that can extract maximum value out of your processes. It can increase production yield, quality, and reduce energy consumption. It can optimize manufacturing operations and provide the performance improvements that you need to improve your bottom-line. AVEVA APC is a comprehensive, predictive, advanced process control software that increases process profitability by improving quality, increasing throughput and reducing energy consumption. It utilizes state-of the-art technology to create automatic control systems capable of unlocking process potential. -
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System 800xA is more than a DCS (Distributed Controller System), it's also an Electrical control system, a Safety system, a collaboration enabler, and has the ability to improve engineering efficiency, operator performance and asset utilization. ABB Ability System 800xA, which has an integrated electrical control system, allows you to control the entire electrical system, including high-voltage switchgear and low-voltage motor controls. ABB Ability System 800xA can be used in conjunction with 800xA DCS. Connect to intelligent devices and reduce hardwired cabling for switchgear, regardless of the protocol. Digital communication is more reliable and can be used to improve the information flow between devices.
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Apromon
PiControl Solutions
Apromon is an online software tool that monitors the PID loop control performance of primary or advanced process control (APC) loops. Apromon can evaluate single loops, cascades, Advanced Process Control (APC), loops, and signals that have PV but no controller. Apromon can automatically convert flow controllers and temperature controllers into a single "grade", just like a professor giving a student a grade on a test or examination. 100 is the best performance, while 0 is the worst. It runs automatically every set period, so performance is always being calculated. It runs all the time and does not skip any period like competitor products. -
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Pitops
PiControl Solutions
Pitops is the only software product to perform closed-loop system ID with PID controllers in Auto mode and secondary PID controllers within a Cascade mode. This allows Pitops to identify the PID controllers without the need to break the cascade chains or conduct additional, time-consuming and intrusive step tests. Pitops is the only competitor that can perform successful transfer function identification with data from PID controllers in Cascade modes (no other tool does this). Pitops can perform transfer function identification in the time domain, whereas other tools use the more complex Laplace (S), or Discrete(Z) domains. Pitops can handle multiple inputs simultaneously and identify multiple transfer function simultaneously. Pitops can identify multiple inputs in closed-loop transfer functions system identification in time domain using a breakthrough algorithm that Pitops has developed. This algorithm is far more powerful than the older methods like ARX/ARMA/Box or Jenkins, which are used in competitors tools.
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Model Predictive Control (MPC) Software Overview
Model Predictive Control (MPC) is a type of advanced control software that enables engineers to optimize the performance of industrial processes. This type of software uses a mathematical model to predict future process variables and then adjusts the process parameters in real time to continuously achieve optimal performance.
The main advantage of using MPC is that it allows for more accurate, reliable and efficient control over any number of variables in an industrial process than traditional open loop controllers. MPC software can also be automated, making it possible for human operators to oversee multiple processes without having to manually adjust them.
At its heart, MPC is based on predictive models which are used to calculate and forecast future values of various factors such as load, pressure and temperature. This prediction helps determine what course of action should be taken in order to reach the desired output or set point. The model takes into account external disturbances such as changing weather conditions or changes in customer demand and then rearranges plant operations accordingly in order to keep the system within specified limits while still achieving maximum efficiency.
MPC algorithms usually include predictive models that are constructed from linear equations or non-linear functions depending on the application being used. These equations take into consideration historical information as well as current system inputs and future predictions about how all these elements interact with each other, allowing for extremely precise control over a variety of parameters.
For instance, if an unplanned disturbance occurs within the system such as a sudden increase in customer demand or changes in outside temperatures, MPC will detect this quickly and feed updated controlling strategies back into the algorithm so that operations can be modified accordingly without sacrificing performance quality or safety levels. Additionally, since MPC is constantly updating its calculations based on new data points, it ensures that plants remain optimized even when faced with dynamic external influences such as changing resource availability or increasing competition from rivals.
In conclusion, Model Predictive Control (MPC) is an incredibly versatile piece of software designed specifically for industrial applications where optimizing operation costs while maintaining product quality is essential. By taking into account both past data points and future expectations about how certain factors could affect results, it allows users to better manage their processes while simultaneously minimizing energy consumption and maximizing profitability.
What Are Some Reasons To Use Model Predictive Control (MPC) Software?
- Flexibility: Model predictive control (MPC) software is more flexible than traditional control systems, as it allows users to set multiple goals and constraints. This means that the MPC software can be tailored to meet specific goals in complex systems with multiple inputs and outputs.
- Efficiency: The ability of MPC software to predict future performance helps increase efficiency and reduce energy costs by optimizing the use of resources over time. It also enables operators to make better decisions by allowing them to analyze the impact of their decisions over longer time periods.
- Reliability: MPC software provides reliable control even under uncertain conditions or when parameters change or vary unpredictably. By incorporating facts about system dynamics, it can adjust its forecasted strategy if required and thus ensure optimal system performance in changing environments.
- Real-time Control: MPC technology allows for fast reaction times which makes it ideal for real-time applications where fast response is essential such as power plants, robotics, automotive applications and aircraft navigation systems etc. Additionally, the ability of this software to detect potential distorting effects like noise contamination provides additional benefits for controlling dynamic processes that require rapid adjustment rates.
The Importance of Model Predictive Control (MPC) Software
Model Predictive Control (MPC) software is a powerful tool that enables engineers to control complex systems with greater accuracy and reliability than ever before. MPC algorithms are able to predict the behavior of a system based on its current values, allowing for more efficient and accurate control strategies. This type of technology provides an invaluable resource for any industrial setting, as it ensures optimal performance without sacrificing safety or efficiency.
The benefits of MPC software come from its ability to model the behavior and interactions of multiple components in a system simultaneously. By taking into account all the various input and output channels in the system, MPC can accurately anticipate how a certain change will affect the overall performance of the device. The result is improved efficiency, more consistent results and reduced energy costs over time. Moreover, MPC creates opportunities for proactive maintenance practices that can identify potential problem areas before they become too serious or costly to address.
MPC also reduces risk by providing an automated means of predicting how changes will affect performance based on current conditions—i.e., compared to manual systems with hard-coded rules or "rules of thumb" that may not always provide reliable or accurate results due to lack of available data points or outdated assumptions about underlying trends in data sets over time. Thanks to predictive analytics, controllers no longer have to rely solely on their own intuition when making decisions; instead they can use precise calculations generated by sophisticated computer models that continuously update over time as new information becomes available.
Finally, because MPC algorithms are able to adjust themselves according to changing environmental conditions such as temperature or other external factors affecting production rates and quality levels—they are especially useful for companies who require up-to-date recommendations as operations take place in remote locations around the globe where human intervention is either difficult or impossible altogether due to distance/time constraints; this brings yet another layer of added convenience that helps engineers maintain operational consistency regardless of their physical location at any given point in time.
All things considered, Model Predictive Control (MPC) software has revolutionized modern industrial automation processes by allowing companies unprecedented levels of accuracy, precision and control within their day-to-day operations—ultimately delivering cost savings while minimizing risk exposure at every step along the way.
Features Provided by Model Predictive Control (MPC) Software
- Model Predictive Control (MPC) Software provides a “lookahead” feature that allows it to predict future states of the system. This helps to make better decisions based on what is expected in the next few time steps, instead of just relying on the current state.
- MPC software also has an optimization capability that can be used to identify optimal control settings for maximum efficiency or other performance goals. It can take into account any number of constraints such as desired production rates, safety parameters and dynamic conditions like ambient temperatures or wind speeds.
- In addition, MPC software has built-in tracking capabilities which enable it to adjust its parameters over time in order to maintain a steady output from the process or systems being controlled. This ability increases efficiency by reducing unnecessary use of resources and energy.
- The software is also capable of learning from past experiences, allowing for adaptive controls that respond quickly and effectively to changing conditions without having to manually adjust settings each time. This makes it easier for operators who do not need detailed technical knowledge in order to keep their processes running smoothly.
- Finally, most modern MPC packages include visualization tools which allow users to visually monitor their processes quickly and easily. These tools often include real-time display modes so changes can be tracked in near real time as well as historical visualizations so trends over time can be observed conveniently with minimal effort.
Types of Users That Can Benefit From Model Predictive Control (MPC) Software
- Engineers: MPC software gives engineers the ability to closely monitor and regulate systems with minimal energy usage and disruption, allowing them to quickly optimize processes for maximum efficiency.
- Manufacturers: By leveraging predictive modeling capabilities, manufacturers can identify trends in product performance or production issues before they become major problems, reducing costly downtime and improving overall quality assurance.
- Industrial Automation Professionals: By utilizing pre-programmed rules and functions within MPC software, automation professionals can reduce labor costs while increasing safety in production lines as well as realize higher levels of precision control over automated machinery than other traditional controllers.
- Service Providers: With the help of a comprehensive set of data-driven analytics tools built into many types of modern MPC software, service providers are able to reliably monitor and maintain complex industrial systems from remote locations without sacrificing performance accuracy or operational reliability.
- Academics & Researchers: The advanced mathematical algorithms used in model predictive control provide a powerful tool for academic researchers studying complex dynamics, as well as provide an invaluable platform for testing hypotheses about how different models interact with each other in real-world scenarios.
How Much Does Model Predictive Control (MPC) Software Cost?
The cost of model predictive control (MPC) software can vary greatly depending on the complexity of the system and the specific features needed. Generally, open source software is available for free, while commercial packages can range from hundreds to thousands of dollars, depending on the licensing options chosen. For industrial applications, packaged MPC software solutions are often more reliable and comprehensive than custom-coded solutions. Depending on application specifics and infrastructure requirements such as SCADA/DCS systems, advanced process control training may also be required to properly use these tools. In summary, the cost of implementing an MPC solution depends on both hardware/software costs as well as related personnel resources such as consulting or engineering services necessary for successful application development and long-term maintenance.
Risk Associated With Model Predictive Control (MPC) Software
- Malfunction: In the event of a malfunction of the MPC software, it could cause various unintended and unpredictable errors to occur in the system. This can lead to inaccurate outputs or undesirable results which could be potentially hazardous or damaging.
- Security risks: The MPC software is reliant on input from external sources such as sensors and other data streams. If these sources are not adequately protected, sensitive information or malicious code may be introduced into the system, leading to serious security breaches.
- Increased complexity: The complexity of an MPC system can significantly increase when additional components such as feedback loops are integrated into it. This increased complexity introduces potential points of failure which must be identified and managed effectively in order to ensure that the system continues to perform correctly.
- Human Error: While MPC systems offer some level of automation, they still require regular maintenance which must be performed by qualified personnel in order to maintain accuracy and consistency between multiple components. Any human errors during this process can have potentially devastating consequences for the performance of the system.
What Software Does Model Predictive Control (MPC) Software Integrate With?
Model Predictive Control (MPC) software is a type of software that uses predictive models to control the operation of industrial processes. It can be used in a variety of applications, including automation, robotics, process control, and more. In order to get the most out of MPC software, it is often necessary to integrate other types of software with it. Some common types of software that are compatible with MPC include data acquisition and processing systems, data communication modules, simulation packages, graphical user interfaces (GUIs), real-time toolkits, decision support systems, alarm management packages, and optimization algorithms. Additionally, some integration solutions provide access to databases or cloud-based services through which data from various sources can be shared securely between different applications. Ultimately, the combination of these various types of software allows for efficient collaboration between multiple users and helps ensure smooth operation for automated industrial processes.
What Are Some Questions To Ask When Considering Model Predictive Control (MPC) Software?
- What is the total cost of ownership (TCO) for the MPC software?
- Does the software have a user-friendly interface so that our team can easily understand it?
- Are there any existing integrations with other systems or applications that we use in our business environment?
- Does the software include features like real-time data analysis and optimization capabilities?
- How long does it typically take to set up and configure the MPC software?
- Does the vendor provide technical support services and implementation assistance if needed?
- Is there an option to expand or customize the MPC technology for our specific needs?
- Does the vendor offer any training programs or resources for users to help them get started with using the MPC software quickly?