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SummaryThis work presents a multivariable predictive controller applied on a redundant robotic manipulator with three degrees of freedom. The article focuses on the design of a discrete model‐based predictive controller (DMPC) using the Laguerre function as a control effort weighting method to enhance the solution of Hildreth's quadratic programming and to minimize the trade‐off problem in constrained case. The Laguerre functions are used to simplify and enhance the control horizon effect through parsimonious control trajectory, thus reducing the computational load required to find the optimal control solution. Furthermore, these results can be confirmed by simulations and experimental tests on the manipulator and comparing it to the traditional DMPC approach and the discrete linear quadratic regulator.

This paper presents the design of a multivariable model predictive controller using Laguerre Functions, for the purpose of demonstrating the benefits and facilities of the application this controller in multipleinput and multiple-output (MIMO) systems. This control strategy is notable for using the state space model, facilitating and generalizing the design for multivariate systems with "n" inputs and "q" outputs. This work also reports simulated tests with the Wood and Berry binary distillation column which is a MIMO benchmark system with two inputs and two outputs, also containing transport time delays and coupled outputs. Then, demonstrate the advantages of the method using the Laguerre functions and their efficiency for MIMO systems.