A device, comprising: one or more processors to: receive a control system model (400; 612), the control system model including: a fixed portion that models elements of a control system to be controlled, and a tunable portion that models elements of the control system used to control the elements modeled by the fixed portion, the fixed portion and the tunable portion interacting with one another in a multiple-input, multiple-output (MIMO) feedback loop configuration; receive information that identifies a tunable parameter of the tunable portion of the control system model; receive information that identifies a hard constraint associated with the control system model (400; 612), the hard constraint identifying a first constraint that is to be satisfied, where the hard constraint refers to an inflexible constraint that the control system must satisfy; receive information that identifies a soft constraint associated with the control system model (400; 612), the soft constraint identifying a second constraint that is to be reduced, where the soft constraint refers to a flexible constraint being a minimization or maximization of a parameter value; calculate the parameter value for the tunable parameter by applying an optimization algorithm to the control system model (400; 612), based on the hard constraint and the soft constraint, the optimization algorithm including a first function associated with the soft constraint and subject to a second function associated with the hard constraint; and provide the parameter value. one or more processors to: receive a control system model (400; 612), the control system model including: a fixed portion that models elements of a control system to be controlled, and a tunable portion that models elements of the control system used to control the elements modeled by the fixed portion, the fixed portion and the tunable portion interacting with one another in a multiple-input, multiple-output (MIMO) feedback loop configuration; receive information that identifies a tunable parameter of the tunable portion of the control system model; receive information that identifies a hard constraint associated with the control system model (400; 612), the hard constraint identifying a first constraint that is to be satisfied, where the hard constraint refers to an inflexible constraint that the control system must satisfy; receive information that identifies a soft constraint associated with the control system model (400; 612), the soft constraint identifying a second constraint that is to be reduced, where the soft constraint refers to a flexible constraint being a minimization or maximization of a parameter value; calculate the parameter value for the tunable parameter by applying an optimization algorithm to the control system model (400; 612), based on the hard constraint and the soft constraint, the optimization algorithm including a first function associated with the soft constraint and subject to a second function associated with the hard constraint; and provide the parameter value. receive a control system model (400; 612), the control system model including: a fixed portion that models elements of a control system to be controlled, and a tunable portion that models elements of the control system used to control the elements modeled by the fixed portion, the fixed portion and the tunable portion interacting with one another in a multiple-input, multiple-output (MIMO) feedback loop configuration; the control system model including: a fixed portion that models elements of a control system to be controlled, and a tunable portion that models elements of the control system used to control the elements modeled by the fixed portion, a fixed portion that models elements of a control system to be controlled, and a tunable portion that models elements of the control system used to control the elements modeled by the fixed portion, receive information that identifies a tunable parameter of the tunable portion of the control system model; receive information that identifies a hard constraint associated with the control system model (400; 612), the hard constraint identifying a first constraint that is to be satisfied, where the hard constraint refers to an inflexible constraint that the control system must satisfy; the hard constraint identifying a first constraint that is to be satisfied, where the hard constraint refers to an inflexible constraint that the control system must satisfy; receive information that identifies a soft constraint associated with the control system model (400; 612), the soft constraint identifying a second constraint that is to be reduced, where the soft constraint refers to a flexible constraint being a minimization or maximization of a parameter value; the soft constraint identifying a second constraint that is to be reduced, where the soft constraint refers to a flexible constraint being a minimization or maximization of a parameter value; calculate the parameter value for the tunable parameter by applying an optimization algorithm to the control system model (400; 612), based on the hard constraint and the soft constraint, the optimization algorithm including a first function associated with the soft constraint and subject to a second function associated with the hard constraint; and the optimization algorithm including a first function associated with the soft constraint and subject to a second function associated with the hard constraint; and provide the parameter value.