The Lambda Papers: When LISP Got Turned Into a Microprocessor
During the AI research boom of the 1970s, the LISP language – from LISt Processor – saw a major surge in use and development, including many dialects being developed. One of these dialects was Scheme, developed by [Guy L. Steele] and [Gerald Jay Sussman], who wrote a number of articles that were published by the Massachusetts Institute of Technology (MIT) AI Lab as part of the AI Memos. This subset, called the Lambda Papers, cover the ideas from both men about lambda calculus, its application with LISP and ultimately the 1980 paper on the design of a LISP-based microprocessor.
Scheme is notable here because it influenced the development of what would be standardized in 1994 as Common Lisp, which is what can be called ‘modern Lisp’. The idea of creating dedicated LISP machines was not a new one, driven by the processing requirements of AI systems. The mismatch between the S-expressions of LISP and the typical way that assembly uses the CPUs of the era led to the development of CPUs with dedicated hardware support for LISP.
The design described by [Steele] and [Sussman] in their 1980 paper, as featured in the Communications of the ACM, features an instruction set architecture (ISA) that matches the LISP language more closely. As described, it is effectively a hardware-based LISP interpreter, implemented in a VLSI chip, called the SCHEME-78. By moving as much as possible into hardware, obviously performance is much improved. This is somewhat like how today’s AI boom is based around dedicated vector processors that excel at inference, unlike generic CPUs.
During the 1980s LISP machines began to integrate more and more hardware features, with the Symbolics and LMI systems featuring heavily. Later these systems also began to be marketed towards non-AI uses like 3D modelling and computer graphics. As however funding for AI research dried up and commodity hardware began to outpace specialized processors, so too did these systems vanish.
Top image: Symbolics 3620 and LMI Lambda Lisp machines (Credit: Jason Riedy)
High Performance Motor Control With FOC From the Ground Up
Vector Control, also known as Field Oriented Control or FOC is an AC motor control scheme that enables fine-grained control over a connected motor, through the precise control of its phases. In a recent video [Excessive Overkill] goes through the basics and then the finer details of how FOC works, as well as how to implement it. These controllers generally uses a proportional integral (PI) loop, capable of measuring and integrating the position of the connected motor, thus allowing for precise adjustments of the applied vector.
If this controller looks familiar, it is because we featured it previously in the context of reviving old industrial robotic arms. Whether you are driving the big motors on an industrial robot, or a much smaller permanent magnet AC (PMAC) motor, FOV is very likely the control mechanism that you want to use for the best results. Of note is that most BLDC motors are actually also PMACs with ESC to provide a DC interface.
The actual driving is done with two MOSFETs per phase, forming a half-bridge, switching between the two rails to create the requisite PWM signal for each phase. Picking the right type of MOSFET was somewhat hard, especially due to the high switching currents and the high frequency at 25 kHz. The latter was picked to prevent audible noise while driving a robot. Ultimately SiC MOSFETs were picked, specially the GeneSiC G3R30MT12K. Of note here are the four legs, with a fourth Kelvin Source pin added. This is to deal with potential gate drive issues that are explained in the video.
With the hardware in place, whether following the [Excessive Overkill] GitHub projects or not, what makes all of it work is the software. This is where the microcontroller aspect is essential, as it has to do all the heavy lifting of calculating the new optimal vector and thus the current levels per phase. In this controller an STM32F413 is used, which generates the PWM signals to drive the half-bridges, while reading the measurements from the motors with its ADC.
As can be seen in the resulting use of this controller with old industrial robots, the FOC controller works quite well, with quiet and smooth operation. This performance is why we’re likely to see FOC and PMAC motors used in applications like 3D printers in the future, though the rule of ‘good enough’ makes the cost of an FOC controller still a tough upsell over a simple open loop stepper-based system.
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