Matteo MacchiniStudent meeting – June 2014 Motion control design for the new BWS Matteo Macchini Technical student BE-BI-BL Supervisor: Jonathan Emery

Matteo Macchini Outline Student meeting – June 2014 PSO method overview Detailed description of the system SVPWM implementation and testing Tuning results from the simulations Robust stability/performance analysis Conclusions – what’s next?

Matteo Macchini Particle Swarm Optimization Student meeting – June 2014 How does it work? Given a multi-dimensional function: Initialises random particles (parameter values) Computes cost Updates position and speed of the particles making them move towards the best result obtained so far

Matteo Macchini Particle Swarm Optimization Student meeting – June 2014 Iteration 1

Matteo Macchini Particle Swarm Optimization Student meeting – June 2014 Iteration 10

Matteo Macchini Particle Swarm Optimization Student meeting – June 2014 Iteration 20

Matteo Macchini Particle Swarm Optimization Student meeting – June 2014 Iteration 30

Matteo Macchini Particle Swarm Optimization Student meeting – June 2014 Cost function

Matteo Macchini Previously achieved results Student meeting – June 2014 Current loop Speed loop Iteration 1 Iteration 4 Iteration 7 Iteration 10 Iteration 1 Iteration 4 Iteration 7 Iteration 10

Matteo Macchini “Old” model Student meeting – June 2014 Motor Current sensor Control system PWM IGBT inverter Analog filter Cable

Matteo Macchini Simplified model(s) Student meeting – June 2014 Cascade version Main differences from classic version Ideal amplifier (no PWM-inverter-filter blocks) Fully digital implementation and simulation (no Simulink-simscape switch) Typical cascade control system with no AW feature Control analysis for iterative optimization Control system Motor Current sensor Cable Amplifier Control analysis

Matteo Macchini Simplified model(s) Student meeting – June 2014 Parallel version Control system Motor Current sensor Cable Amplifier Control analysis Differences from cascade version Parallel control system with no AW feature (designed entirely from scratch)

Matteo Macchini Space Vector PWM Student meeting – June 2014 Advantages [1] : Lower THD (Total Harmonic Distortion) Greater PF (Power Factor) Less switching losses Lower computational cost Principle: Differently from classic SPWM, SVPWM transforms a three-phase sinusoidal wave into its PWM, considering the combination of the three inputs at once.

Matteo Macchini Space Vector PWM implementation Student meeting – June 2014 Simulink implementation Obtained waveform Low-pass filters SVPWM block Output coherent with expectations Couldn’t appreciate advantages (SO FAR!)

Matteo Macchini Tuning strategy Student meeting – June 2014 Create system model Initialize parameters Implement system control based on desired dynamics Compute a COST FUNCTION based on the obtained results Modify parameters in order to minimize the CF

Matteo Macchini Cost function computation Student meeting – June 2014 As a cost function, the integral absolute error (IAE) between the desired motion profile and the results has been used. To improve its quality, some weights were added on the critical zones of the dynamic.

Matteo Macchini Simulations and results Student meeting – June 2014 Cascade design, 100 particles, 10 iterations

Matteo Macchini Simulations and results Student meeting – June 2014 Cascade design, 100 particles, 10 iterations

Matteo Macchini Simulations and results Student meeting – June 2014 Parallel design, 100 particles, 10 iterations

Matteo Macchini Simulations and results Student meeting – June 2014 Parallel design, 100 particles, 10 iterations

Matteo Macchini Simulations and results Student meeting – June 2014 Cost function evolution

Matteo Macchini Cable modeling Student meeting – June 2014 For the last simulations, a cable model has been implemented. It takes into account: Cable self attenuation Cable cross-talk Cable length

Matteo Macchini Performance/robustness Student meeting – June 2014 ROBUST controller: results are “similar” into a given range of uncertainty. PERFORMING controller: the reference profile is followed “properly”, i.e. the cost function has a “low” value. A controller should be performing in order to guarantee the control quality for the tested device. A controller should be robust in order to guarantee the control quality for a family of devices working in different technical/environmental conditions.

Matteo Macchini Robustness test Student meeting – June 2014 Robust controller: results are “similar” into a given range of uncertainty (cable length variable between 1m and 300m) Robust controller Non-robust controller

Matteo Macchini Robustness test Student meeting – June 2014 Robust controller: results are “similar” into a given range of uncertainty (cable length variable between 1m and 300m) Robust controller Non-robust controller

Matteo Macchini Robustness test Student meeting – June 2014 Robust controller: results are “similar” into a given range of uncertainty (cable length variable between 1m and 300m) Robust controller Non-robust controller

Matteo Macchini Robust synthesis Student meeting – June 2014 IDEA: Launch tuning algorithm several times Test robustness Check if robust controller have similar parameters and try to reproduce them Non-robust controller

Matteo Macchini Conclusions Student meeting – June 2014 Particle Swarm Optimization can be used to tune controller parameters in the considered system SVPWM will help increasing the quality of the amplifier Cascade architecture: good performances, very good robustness Parallel architecture: very good performance, hard to make robust Robust controllers can be tuned using iterative methods

Matteo Macchini In the future… Student meeting - May 2014 NEXT GOALS Validating the previous system Implement control on the bench Make it work properly Study VHDL/hardware design in order to port it on FPGA

Matteo Macchini References Student meeting – June 2014 [1] Waheed Ahmed, Syed M Usman Ali, “Comparative study of SVPWM & SPWM three phase voltage source inverters for variable speed drive”