**6. Experimental Results**

In order to check the performance of the proposed method, a number of experiments have been carried out. The seeking MTPA algorithm used for testing, involves the PandO principle and permanently disturbs the phase of the stator current, analyzing changes of its magnitude. Thus, the constraints for this technique were set as curves "E" and "F" from Figure 11 and implemented using LUT, containing 33 points for each curve with linear interpolation between them. The disturbance angle Δ*γ* used by the seeking algorithm was 4◦ and its calculation step *N* was selected as 20 electrical revolutions. The maximum calculation time *T*max of the MTPA tuning algorithm was set to 0.5 s, which results in a minimum motor speed, at which an MTPA tuning algorithm can operate:

$$n\_{\rm min} = \frac{60 \cdot N}{p \cdot T\_{\rm max}} = \frac{60 \cdot 20}{3 \cdot 0.5} = 800 \text{ rpm} \tag{10}$$

This speed perfectly fits the requirements of the control system of compressors, where the operational area requires speeds over 900~1200 rpm.

In order to check the dynamic behavior of the seeking algorithm with different constraints, the load torque contained equal intervals of constant load, and positive and negative ramps. For the performance evaluation of the proposed idea, the measurements were performed at the lowest allowed operational speed, which is the most difficult condition for the seeking algorithms. Despite the minimum operational speed of the selected seeking technique being 800 rpm, the experiments were conducted at 1200 rpm, which is the minimum permitted speed for reciprocating the compressors utilizing the considering drive. After obtaining the data on estimated MTPA angle, the efficiency of the motor was measured in operation of the MTPA-seeking algorithm with the considered sets of constraints. In this test, the data were averaged at the interval of ten load periods (200 s), which minimizes the impact of random perturbations.

For comparison of the developed method with the prior art, the constraints were designed according to their recommendations.
