Real processes with heat exchange have usually complex behaviour and are energy intensive. In practical applications, the process variables are always bounded, and it is suitable to include these boundaries into the controller design. The soft-constrained robust model predictive controller has been designed to improve the control performance and energy consumption in comparison with the robust model predictive control with only hard constraints. Experimental application of soft-constrained robust model predictive control (SCR MPC) for a laboratory plate heat exchanger is presented in this paper. The plate heat exchanger is a non-linear process with asymmetric dynamics and is modelled as a system with parametric uncertainties. The controlled variable is the temperature of the heated fluid at the outlet of the heat exchanger and the manipulated variable is the volumetric flow rate of the heating fluid. The actuator is a peristaltic pump and the influence of the linear and non-linear actuator characteristics on the control performance is also investigated. The set-point tracking using SCR MPC is studied for the laboratory plate heat exchanger in an extensive case study. The experimental results confirmed the improvement of the control responses and reduction of energy consumption by introducing the soft constraints into MPC design. (C) 2019 Elsevier Ltd. All rights reserved.