Intelligent　servo　control　significantly　reduces　the　need　to　adjust　control　parameters,　and　is,　therefore,　widely　used　in　robot　joint　control.　However,　existing　intelligent　servo　control　strategies　for　robot　joints　have　problems　of　computational　redundancy,　limited　prediction　accuracy,　and　insufficient　generalization　capability.　To　solve　these　problems,　this　article　proposes　a　servo　control　strategy　for　robot　joints　that　is　based　on　the　incremental　Bayesian　fuzzy　broad　learning　system　(IBFBLS).　First,　we　construct　an　intelligent　servo　control　strategy　with　broad　learning　system　on　the　basis　of　fuzzy　rules　to　achieve　good　self-learning　and　generalization　abilities.　Second,　the　learning　parameters　of　the　control　strategy　are　optimized　by　Bayesian　inference　to　achieve　precise　joint　servo　control.　Finally,　the　convergence　of　the　control　strategy　is　enhanced　by　combining　it　with　Lyapunov　theory　to　constrain　the　learning　parameters　of　the　proposed　control　strategy.　The　feasibility　and　superiority　of　the　proposed　control　strategy　are　verified　by　simulation　to　compare　it　with　existing　intelligent　servo　control　methods.　In　addition,　experiments　are　conducted　using　robot　joint　test　bed.　Both　the　simulation　and　experiments　verify　that　the　proposed　servo　control　strategy　outperforms　other　servo　control　methods　with　respect　to　tracking　accuracy,　stability,　and　convergence.　The　root-mean-square　error　in　servo　control　of　robot　joints　was　0.012$\%$,　which　has　been　reduced　by　55.56$\%$　compared　with　the　current　state　of　the　art.　IEEE