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+and+Proximal+Policy+Optimization+(PPO)%252C+a+Deep+Reinforcement+Learning+(DRL)+algorithm%252C+applied+to+a+1-Degree+of+Freedom+(DOF)+Quanser+Aero+2+system.+Classical+control+techniques+such+as+MPC+and+Linear+Quadratic+Regulator+(LQR)+are+widely+used+due+to+their+theoretical+foundation+and+practical+effectiveness.+However%252C+with+advancements+in+computational+techniques+and+machine+learning%252C+DRL+approaches+like+PPO+have+gained+traction+in+solving+optimal+control+problems+through+environment+interaction.+This+paper+systematically+evaluates+the+dynamic+response+characteristics+of+PPO+and+MPC%252C+comparing+their+performance%252C+computational+resource+consumption%252C+and+implementation+complexity.+Experimental+results+show+that+while+LQR+achieves+the+best+steady-state+accuracy%252C+PPO+excels+in+rise-time+and+adaptability%252C+making+it+a+promising+approach+for+applications+requiring+rapid+response+and+adaptability.+Additionally%252C+we+have+established+a+baseline+for+future+RL-related+research+on+this+specific+testbed.+We+also+discuss+the+strengths+and+limitations+of+each+control+strategy%252C+providing+recommendations+for+selecting+appropriate+controllers+for+real-world+scenarios.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.22345fc59f9c0e27a9add7295f3d0fd5%3frik%3dKiXzAwULht4ppg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Optimization+Algorithm+Model+Predictive+Control&FORM=IRPRST&ck=90C273C17B64A5177FBF4A61053C8AB3&selectedIndex=10&itb=0)
