Model Predictive Control Toolbox vs. Solver SDK vs. Unlambda vs. ZenScript Comparison Chart

Model Predictive Control Toolbox™ provides functions, an app, Simulink® blocks, and reference examples for developing model predictive control (MPC). For linear problems, the toolbox supports the design of implicit, explicit, adaptive, and gain-scheduled MPC. For nonlinear problems, you can implement single- and multi-stage nonlinear MPC. The toolbox provides deployable optimization solvers and also enables you to use a custom solver. You can evaluate controller performance in MATLAB® and Simulink by running closed-loop simulations. For automated driving, you can also use the provided MISRA C®- and ISO 26262-compliant blocks and examples to quickly get started with lane keep assist, path planning, path following, and adaptive cruise control applications. Design implicit, gain-scheduled, and adaptive MPC controllers that solve a quadratic programming (QP) problem. Generate an explicit MPC controller from an implicit design. Use discrete control set MPC for mixed-integer QP problems.

Unlambda is a programming language. Nothing remarkable there. The originality of Unlambda is that it stands as the unexpected intersection of two marginal families of languages. Functional programming languages, of which the canonical representative is Scheme (a Lisp dialect). This means that the basic object manipulated by the language (and indeed the only one as far as Unlambda is concerned) is the function. Rather, Unlambda uses a functional approach to programming: the only form of objects it manipulates are functions. Each function takes a function as an argument and returns a function. Apart from a binary “apply” operation, Unlambda provides several built-in functions (the most important ones being the K and S combinators). User-defined functions can be created, but not saved or named, because Unlambda does not have any variables.