PREDICTIVE COMPUTED-TORQUE CONTROL OF A PUMA 560 MANIPULATOR ROBOT Victor M. Becerra, Steven Cook and Jiamei Deng University of Reading, Department of Cybernetics, Reading RG6 6AY, UK Abstract: This paper describes the integration of constrained predictive control and computed-torque control, and its application on a six degree-of-freedom PUMA 560 manipulator arm. The real-time implementation was based on SIMULINK, with the predictive controller and the computed-torque control law implemented in the C programming language. The constrained predictive controller solved a quadratic programming problem at every sampling interval, which was as short as 10 ms, using a prediction horizon of 150 steps and an 18th order state space model. Copyright c 2005 IFAC Keywords: Predictive control, robotic manipulators, computed-torque control. 1. INTRODUCTION This paper describes the integration of con- strained predictive control and computed-torque control, and its application in real time to control a six degree-of-freedom PUMA 560 manipulator arm. Until recent years the application of con- strained predictive control to a manipulator with six degrees of freedom, although theoretically pos- sible, has not been practical as computer proces- sors have not been fast enough to solve online the associated constrained optimisation problem within the short sampling periods that are re- quired by the application. Predictive control has been proposed on a simulated two-link PUMA 560 arm as described in (Torres et al., 2001), who used local linearisation to define the internal model. In the work by (Bemporad et al., 1997), a predictive path generator was designed to deal with various constraints, and experiments on a PUMA 560 manipulator were made using three links of arm. The essence of predictive control is to optimise, over the manipulable inputs, forecasts of process behaviour (Maciejowski, 2002). One of the main benefits of predictive control is that constraints on the inputs and outputs of the system can be explicitly considered in the control problem formulation and its solution. The success of linear predictive control has in- spired researchers to look into the possibility of extending it for non-linear control applications. Since manipulator robots exhibit strong nonlin- earities, their performance can be significantly improved by using nonlinear control strategies. Computed-torque control (Lewis et al., 2004) is a technique that uses a nonlinear dynamic model of the system to remove the nonlinearities of the ma- nipulator, facilitating external control with fixed gains. Poignet et al. (2000) describe a combined predictive functional control / computed-torque control scheme, with simulated tests on a two degree-of-freedom SCARA robot. The PUMA 560, shown in Figure 1, is a six- degree of freedom robotic manipulator that uses six dc servomotors for joint control. Joint po- sitions are measured using encoders and poten- tiometers. Three large motors provide control of