Compare BrightHire vs. Model Predictive Control Toolbox

BrightHire is the go-to solution for some of the fastest growing companies around the globe, enabling them to hire more efficiently, fairly, and effectively. Integrated seamlessly with Zoom, our platform ensures that your team conducts organized, high-quality interviews that can be easily reviewed and shared, streamlining the hiring process and enhancing the candidate experience. Recognizing the significance of hiring decisions, we believe that talent strategies should not rely on instinct or guesswork. Instantly elevate the standard of every recruitment conversation by providing recruiters and interviewers with an Interview Assistant that facilitates discussions and captures essential highlights in real-time. Focus on evaluating candidates based on their actual qualifications rather than your recollections. Effortlessly access candidate highlights to remember crucial information, reassess initial perceptions, and substitute implicit biases with tangible evidence. By demystifying the hiring process, you move beyond anecdotal evidence, gaining a clearer understanding of your recruitment strategy, ultimately leading to a more informed decision-making process. Embracing this approach allows organizations to cultivate a fairer and more effective hiring environment.

The Model Predictive Control Toolbox™ offers a comprehensive suite of functions, an intuitive app, Simulink® blocks, and practical reference examples to facilitate the development of model predictive control (MPC) systems. It caters to linear challenges by enabling the creation of implicit, explicit, adaptive, and gain-scheduled MPC strategies. For more complex nonlinear scenarios, users can execute both single-stage and multi-stage nonlinear MPC. Additionally, this toolbox includes deployable optimization solvers and permits the integration of custom solvers. Users can assess the effectiveness of their controllers through closed-loop simulations in MATLAB® and Simulink environments. For applications in automated driving, the toolbox also features MISRA C®- and ISO 26262-compliant blocks and examples, allowing for a swift initiation of projects related to lane keep assist, path planning, path following, and adaptive cruise control. You have the capability to design implicit, gain-scheduled, and adaptive MPC controllers that tackle quadratic programming (QP) problems, and you can generate an explicit MPC controller derived from an implicit design. Furthermore, the toolbox supports discrete control set MPC for handling mixed-integer QP challenges, thus broadening its applicability in diverse control systems. With these extensive features, the toolbox ensures that both novice and experienced users can effectively implement advanced control strategies.