Pressure　servo　control　plays　a　crucial　role　in　the　majority　of　industrial　applications　that　use　compressed　air　as　their　power　source.　How　to　reduce　the　energy　consumption　of　the　pneumatic　system　while　ensuring　the　high　accuracy　of　pressure　control　remains　a　key　problem　to　be　solved.　This　paper　proposes　a　Gaussian　Process　(GP)　1　model　predictive　controller　(MPC)　applied　to　a　pressure　servo　control　system　based　on　high-speed　on-off　valves.　The　smallest　quantity　of　collected　data　is　used　to　create　the　GP　model.　The　entire　model,　which　includes　some　predictive　data,　is　produced　once　the　generated　model　has　been　optimized.　The　GP　model　is　combined　with　the　MPC　to　perform　comparative　experiments　using　an　industrial　controller　with　a　Field　Programmable　Gate　Array　(FPGA).　The　system　responds　quickly　and　with　little　tracking　error　in　steady-state　response　studies　as　well　as　dynamic　response　experiments.　The　GP-MPC　achieves　the　best　outcomes　in　the　dynamic　comparison　tests.　The　root　mean　square　error　(RMSE)　is　just　2.42　kPa,　and　the　overshoot　is　less　than　59.9%　of　the　Proportional-Integral-Derivative　(PID)　controller　and　68.5%　of　the　sliding　mode　controller　(SMC).　Although　the　compressed　air　con-sumption　of　GP-MPC　is　basically　the　same　as　that　of　PID,　it　is　significantly　better　than　SMC,　with　a　total　saving　of　about　70.2%.　All　the　experiments　prove　that　the　controller　proposed　in　this　paper　can　effectively　improve　the　energy　efficiency　and　tracking　accuracy　of　the　pressure　servo　control　system.