That is a bit of a vague question, but yes. I’ll try to elaborate more, since you seem to fault perceptual model for things they couldn’t possibly fix. Maybe this helps to make it more clear:
You can see this problem without at all looking at perceptual models, it is just a property of perception and the sRGB primaries. Or more specifically the Abney effect and sRGB primaries.
If we start by with the blue RGB primary, and start moving it linearly towards white 15% of the way, it looks like this:
Here of course we see the Abney effect coming into play, the linear path towards white looks like it is shifting towards purple.
By adjusting the path and reducing the amount of red mixed in, it is possible to compensate for this and get something closer to our perception of hue. That looks something like this:
How much of the red sRGB primary do we have to remove to compensate for the Abney effect? All of it turns out (or very close to all of it). The rightmost square here has, in linear sRGB, values (0.0, 0.15, 1.0). So, we get this strange conclusion: if we start with the blue sRGB primary and gradually add small amounts of the green sRGB primary, the result does not look more green (or more cyan), it just looks like a lighter and less colorful blue.
Only until significantly more green is added does the result start looking like a hue shift (as well as shifts in chroma and lightness). In that process though, we have also gotten a significantly less saturated color. Here is the path to (0.0, 0.5, 1.0):
So, if we start with the blue sRGB primary and want to find the closest color to it that has a hue slightly more cyan hue, we can’t just mix in a tiny bit of green, since that doesn’t affect the hue noticeably. Instead we have to mix in a lot of green, in turn also reducing chroma substantially and affecting lightness. The lightness can be corrected for, by scaling the RGB values, but the chroma is impossible to correct for since we are already on the RGB gamut boundary. The only way to achieve that would be with a stronger green primary.
What does this mean for color models trying to model hue perception? Well nothing on their own, there only is a problem when working with the sRGB gamut (or other similar RGB color space).
What happens then is that if you have a model that somewhat accurately models hue around the blue srgb primary, and try to make a plot of maximum chroma in RGB as hue changes uniformly, you will get very abrupt changes in chroma, due to the shape of the RGB gamut. This in turn results in large color differences.
This is what you see in a plot like this:
You can of course change the lightness calculation in various ways to see how it affects things, but it won’t get rid of the large difference in chroma and the discontinuity that is simply a result of the interaction between the sRGB primaries and the Abney effect. For example like this with a different metric for the y-axis, blue is looking less fluorescent, but the edge still remains:
Of course, if you don’t need to reach the edges of the sRGB gamut, you can get a much smoother plot (here smoothing the RGB cube somewhat, but the chroma is still varying significantly through the image)
If you plot constant chroma instead, you can get a very smooth result, but of course many colors escape the gamut quite quickly:
