【正文】 nd controlled actuator are estimated on experimental data of a medium size gasoline car and used to check through simulations the effectiveness of the proposed controller. Index Terms— Automated manual transmissions (AMTs), automotive control, clutch engagement control, dry clutch, gearshift. I. INTRODUCTION CARS with modern transmission systems exhibit high fuel economy, low exhaust emission, and excellent driveability. Recent reports on the future automotive market forecast that in 2020 the production of manual transmissions will have fallen below 50% while the modern automatic transmissions will have reached 25% of production [1], [2]. Among other responces, the automated manual transmissions (AMTs) represent a promising solution since they can be considered as an inexpensive addon solution for classical (in European and Latin countries) manual transmission systems. Moreover, AMTs are also extensively used in racing cars and as a reconfiguration element in modern hybrid electric vehicles. One of the most critical operations in AMTs is represented by the gearshift and more specifically by the clutch engagement. In automotive drivelines, the goal of the clutch is to smoothly connect two rotating masses, the flywheel and the transmission shaft, that rotate at different speeds, in order to allow the transfer of the torque generated by the engine to 2 the wheels through the driveline. The automation of the clutch engagement must satisfy different and conflicting objectives: It should obtain at least the same performance manually achievable by the driver (short gearshift time and fort) and improve performance in terms of emission and facing wear. The engine and clutch speeds during the engagement and at the lockup play an important role both for fort and friction losses [3], [4]. In order to achieve the objectives of the clutch engagement automation, several control approaches which deal with the vehicle startup operating conditions have been proposed: quantitative feedback theory [5], model predictive control strategy [6], fuzzy control [7], decoupling control [4], and optimal control [8], further in [9], the authors propose a particular engagement technique. Problems and solutions related to the clutch engagement during the gearshift phase have been also considered in the literature. In [10], an analytical procedure for puting the desired engine speed during upshift and downshift is proposed. In [11], a modelbased backstepping methodology is used to design the gearshift control in AMTs without the synchronizer. In [12], a neurofuzzy approach is used by considering the driver’s intention and variable loads. In spite of the extensive literature on AMT control, some problems still need further investigation: the role of speed feedback loops in the clutch engagement control, the definition of a controller architecture which can be exploited both during vehicle startup and gearshift, the robustness of the solution with respect to clutch aging, and uncertainties in the clutch characteristic. This paper tries to provide a contribution in this direction by proposing a new controller for gearshift and clutch engagement in AMTs. The paper is organized as follows. In Section II, models of driveline, dry clutch, and closedloop electrohydraulic actuator are considered and tuned on experimental data. In Section III, five different operating phases of the AMT are considered: engaged, slippingopening, synchronization, gotoslipping, and slippingclosing. The controllers, designed through a hierarchical approach with decoupled and cascaded feedback loops based on measurements of clutch speed, engine speed, and throwout bearing position, are presented in Section IV. The controlled AMT is simulated in the Matlab environment where the Simulink scheme corresponding to the current AMT phase and the corresponding controller are selected by a Stateflow finite state machine. Simulation results showing the effectiveness of the proposed approach are presented