Autotuning　and　online　tuning　of　control　parameters　in　control　processes　(OTP)　are　widely　used　in　practice,　such　as　in　chemical　production　and　industrial　control　processes.　Better　performance　(such　as　dynamic　speed　and　steady-state　error)　and　less　repeated　manual-tuning　workloads　in　bad　environments　for　engineers　are　expected.　The　main　works　are　as　follows:　Firstly,　a　change　ratio　for　expert　system　and　fuzzy-reasoning-based　OTP　methods　is　proposed.　Secondly,　a　wavelet　neural-network-based　OTP　method　is　proposed.　Thirdly,　comparative　simulations　are　implemented　in　order　to　verify　the　performance.　Finally,　the　stability　of　the　proposed　methods　is　analyzed　based　on　the　theory　of　stability.　Results　and　effects　are　as　follows:　Firstly,　the　proposed　control　parameters　of　online　tuning　methods　of　artificial-intelligence-based　classical　control　(AI-CC)　systems　had　better　performance,　such　as　faster　speed　and　smaller　error.　Secondly,　stability　was　verified　theoretically,　so　the　proposed　method　could　be　applied　with　a　guarantee.　Thirdly,　a　lot　of　repeated　and　unsafe　manual-based　tuning　work　for　engineers　can　be　replaced　by　AI-CC　systems.　Finally,　an　upgrade　solution　AI-CC,　with　low　cost,　is　provided　for　a　large　number　of　existing　classical　control　systems.